Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Le Debruiteur\n",
    "* Jonas Freiburghaus\n",
    "* Romain Capocasale\n",
    "* He-Arc, INF3dlm-a\n",
    "* Image Processing course\n",
    "* 2019-2020\n",
    "\n",
    "## Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "from debruiteur.generator.datagenerator import DataGenerator\n",
    "from debruiteur.preprocessing.preprocessor import make_original_dataframe, make_resized_dataframe, make_noised_dataframe\n",
    "from debruiteur.noise.noise import GaussianNoise, PoissonNoise, UniformNoise, SaltPepperNoise, SquareMaskNoise, SpeckleNoise\n",
    "from debruiteur.utils.utils import load_model, split_train_val_df\n",
    "from debruiteur.noise.filters import gaussian_filter, wiener_filter, laplacian_filter, gaussian_weighted_substract_filter\n",
    "from debruiteur.noise.filters import mean_filter, median_filter, conservative_filter, low_pass_filter, high_pass_filter\n",
    "from debruiteur.statistics.statistics import compute_noise_reduction_method_statistics, compute_noise_type_statistics\n",
    "from debruiteur.metrics.metrics import metrics_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_class_list = [\n",
    "    GaussianNoise(mean=0, std=20),\n",
    "    PoissonNoise(),\n",
    "    UniformNoise(amplitude=100),\n",
    "    SaltPepperNoise(),\n",
    "    SquareMaskNoise(mask_shape=(10, 10), freq=0.1),\n",
    "    SpeckleNoise(),\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "working_dir = os.path.abspath(os.getcwd())\n",
    "\n",
    "df_original = make_original_dataframe(os.path.join(working_dir, \"images\"))\n",
    "df_resized = make_resized_dataframe(df_original, img_shape=(100, 100), resized_path=os.path.join(working_dir, \"resized_images\"))\n",
    "df_noised = make_noised_dataframe(df_resized, noise_class_list, os.path.join(working_dir, \"noised_images\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metrics\n",
    "\n",
    "Mean squared error (MSE) :\n",
    "$$\\text{MSE} = \\frac{1}{n} \\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2$$\n",
    "\n",
    "Normalized root mean squared error (NRMSE) :\n",
    "$$\\text{NRMSE} = \\sqrt{\\frac{1}{n} \\sum_{i=1}^{n}\\left(\\log{\\frac{\\hat{y} + 1}{y + 1}}\\right)^2}$$\n",
    "\n",
    "Peak signal to noise ration (PSNR) :\n",
    "$$\\text{PSNR} = 10 \\log_{10} \\left( \\frac{{MAX_{I}^2}}{MSE} \\right)$$\n",
    "\n",
    "Structural similarity (SSIM) :\n",
    "$$\\text{SSIM(x, y)} = \\frac{(2\\mu_x \\mu_y + c_1)(2\\sigma_{xy} + c_2)}{(\\mu_x^2 + \\mu_y^2 + c_1)(\\sigma_x^2 + \\sigma_y^2 + c_2)}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show metrics example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jonas.freiburg\\Documents\\Niveau3\\imagerie\\traitement_image\\projet\\ledebruiteur\\debruiteur\\metrics\\metrics.py:65: UserWarning: Inputs have mismatched dtype.  Setting data_range based on im1.dtype.\n",
      "  ssim = metrics.structural_similarity(orignal_img, noised_img)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Noise</th>\n",
       "      <th>MSE</th>\n",
       "      <th>NRMSE</th>\n",
       "      <th>PSNR</th>\n",
       "      <th>SSIM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GaussianNoise</td>\n",
       "      <td>404.694079</td>\n",
       "      <td>0.148148</td>\n",
       "      <td>22.059535</td>\n",
       "      <td>0.432669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PoissonNoise</td>\n",
       "      <td>243.662200</td>\n",
       "      <td>0.114954</td>\n",
       "      <td>24.262922</td>\n",
       "      <td>0.505953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UniformNoise</td>\n",
       "      <td>3312.642379</td>\n",
       "      <td>0.423856</td>\n",
       "      <td>12.929058</td>\n",
       "      <td>0.303072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SaltPepperNoise</td>\n",
       "      <td>2216.831500</td>\n",
       "      <td>0.346735</td>\n",
       "      <td>14.673477</td>\n",
       "      <td>0.278841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SquareMaskNoise</td>\n",
       "      <td>2170.195300</td>\n",
       "      <td>0.343068</td>\n",
       "      <td>14.765815</td>\n",
       "      <td>0.809742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SpeckleNoise</td>\n",
       "      <td>736.907895</td>\n",
       "      <td>0.199911</td>\n",
       "      <td>19.456672</td>\n",
       "      <td>0.508690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Noise          MSE     NRMSE       PSNR      SSIM\n",
       "0    GaussianNoise   404.694079  0.148148  22.059535  0.432669\n",
       "1     PoissonNoise   243.662200  0.114954  24.262922  0.505953\n",
       "2     UniformNoise  3312.642379  0.423856  12.929058  0.303072\n",
       "3  SaltPepperNoise  2216.831500  0.346735  14.673477  0.278841\n",
       "4  SquareMaskNoise  2170.195300  0.343068  14.765815  0.809742\n",
       "5     SpeckleNoise   736.907895  0.199911  19.456672  0.508690"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_example(df_noised, noise_class_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see the score for each type of noise for each metric. We can notice that the type of noise that deteriorates the image the most is SpeckleNoise. We can also see that the Gaussian, Poisson noise is one of the filters that deteriorates the image the least. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create data generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats_gen = DataGenerator(df_noised[0:20], batch_size=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jonas.freiburg\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow_core\\python\\keras\\layers\\core.py:986: UserWarning: debruiteur.models.gan is not loaded, but a Lambda layer uses it. It may cause errors.\n",
      "  , UserWarning)\n"
     ]
    }
   ],
   "source": [
    "conv_ae_model = load_model(os.path.join(working_dir, \"saved_models\"), \"conv_autoencoder.h5\")\n",
    "dense_ae_model = load_model(os.path.join(working_dir, \"saved_models\"), \"dense_autoencoder.h5\")\n",
    "gan_model = load_model(os.path.join(working_dir, \"saved_models\"), \"gan_generator.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Noise reduction methods\n",
    "After the output of the neural networks we apply a filter to smooth the image. Indeed, the images at the output of the neural network are a little blurred. These filters allow to reduce the blur. The filters used are : \n",
    "* Wiener filter\n",
    "* Laplacian filter\n",
    "* Gaussian Weigthed filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "noise_reduction_methods = [('Gaussian Filter', lambda img: gaussian_filter(img * 255)),\n",
    "                           ('Mean Filter', lambda img: mean_filter(img * 255)),\n",
    "                           ('Median Filter', lambda img: median_filter(img * 255)),\n",
    "                           ('Conservative Filter', lambda img: conservative_filter(img * 255)),\n",
    "                           ('Low pass FFT Filter', lambda img: low_pass_filter(img * 255)),\n",
    "                           \n",
    "                           ('Convolutional Autoencoder None', lambda x : conv_ae_model.predict(x.reshape(1, 100, 100, 1)) * 255),\n",
    "                           ('Convolutional Autoencoder Wiener', lambda x : wiener_filter(conv_ae_model.predict(x.reshape(1, 100, 100, 1)).reshape(100,100) * 255)),\n",
    "                           ('Convolutional Autoencoder Laplacian', lambda x : laplacian_filter(conv_ae_model.predict(x.reshape(1, 100, 100, 1)).reshape(100,100) * 255)),\n",
    "                           ('Convolutional Autoencoder Gaussian Weighted', lambda x : gaussian_weighted_substract_filter(conv_ae_model.predict(x.reshape(1, 100, 100, 1)).reshape(100,100) * 255)),\n",
    "\n",
    "                           ('Dense Autoencoder', lambda x : dense_ae_model.predict(x.reshape(1, 10000)) * 255),\n",
    "\n",
    "                           ('Generative Adversarial Network None', lambda x : gan_model.predict(x.reshape(1, 100, 100, 1)) * 255),\n",
    "                           ('Generative Adversarial Network Wiener', lambda x : wiener_filter(gan_model.predict(x.reshape(1, 100, 100, 1)).reshape(100,100) * 255)),\n",
    "                           ('Generative Adversarial Network Laplacian', lambda x : laplacian_filter(gan_model.predict(x.reshape(1, 100, 100, 1)).reshape(100,100) * 255)),\n",
    "                           ('Generative Adversarial Network Weighted', lambda x : gaussian_weighted_substract_filter(gan_model.predict(x.reshape(1, 100, 100, 1)).reshape(100,100) * 255))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Noise reduction methods metrics averaged on all kind of noises"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compute finish for Gaussian Filter\n",
      "Compute finish for Mean Filter\n",
      "Compute finish for Median Filter\n",
      "Compute finish for Conservative Filter\n",
      "Compute finish for Low pass FFT Filter\n",
      "Compute finish for Convolutional Autoencoder None\n",
      "Compute finish for Convolutional Autoencoder Wiener\n",
      "Compute finish for Convolutional Autoencoder Laplacian\n",
      "Compute finish for Convolutional Autoencoder Gaussian Weighted\n",
      "Compute finish for Dense Autoencoder\n",
      "Compute finish for Generative Adversarial Network None\n",
      "Compute finish for Generative Adversarial Network Wiener\n",
      "Compute finish for Generative Adversarial Network Laplacian\n",
      "Compute finish for Generative Adversarial Network Weighted\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSE</th>\n",
       "      <th>NRMSE</th>\n",
       "      <th>PSNR</th>\n",
       "      <th>SSIM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gaussian Filter</th>\n",
       "      <td>1329.173152</td>\n",
       "      <td>0.260626</td>\n",
       "      <td>18.202631</td>\n",
       "      <td>0.553287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Filter</th>\n",
       "      <td>1591.244992</td>\n",
       "      <td>0.293272</td>\n",
       "      <td>16.924058</td>\n",
       "      <td>0.408735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median Filter</th>\n",
       "      <td>1554.300015</td>\n",
       "      <td>0.286259</td>\n",
       "      <td>17.261898</td>\n",
       "      <td>0.434598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conservative Filter</th>\n",
       "      <td>1279.248365</td>\n",
       "      <td>0.246813</td>\n",
       "      <td>18.876066</td>\n",
       "      <td>0.648794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low pass FFT Filter</th>\n",
       "      <td>1227.034459</td>\n",
       "      <td>0.246544</td>\n",
       "      <td>18.773191</td>\n",
       "      <td>0.593923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder None</th>\n",
       "      <td>2721.356234</td>\n",
       "      <td>0.391336</td>\n",
       "      <td>14.390961</td>\n",
       "      <td>0.187798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder Wiener</th>\n",
       "      <td>3600.767919</td>\n",
       "      <td>0.456471</td>\n",
       "      <td>12.832394</td>\n",
       "      <td>0.150496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder Laplacian</th>\n",
       "      <td>2758.100050</td>\n",
       "      <td>0.394767</td>\n",
       "      <td>14.301804</td>\n",
       "      <td>0.176320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder Gaussian Weighted</th>\n",
       "      <td>2762.551183</td>\n",
       "      <td>0.393755</td>\n",
       "      <td>14.335962</td>\n",
       "      <td>0.189528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dense Autoencoder</th>\n",
       "      <td>5596.324806</td>\n",
       "      <td>0.574819</td>\n",
       "      <td>10.735354</td>\n",
       "      <td>0.008347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network None</th>\n",
       "      <td>1727.361495</td>\n",
       "      <td>0.304286</td>\n",
       "      <td>16.707313</td>\n",
       "      <td>0.552804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network Wiener</th>\n",
       "      <td>2639.014338</td>\n",
       "      <td>0.386210</td>\n",
       "      <td>14.291242</td>\n",
       "      <td>0.329740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network Laplacian</th>\n",
       "      <td>2381.024116</td>\n",
       "      <td>0.366836</td>\n",
       "      <td>14.871798</td>\n",
       "      <td>0.221546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network Weighted</th>\n",
       "      <td>1757.415785</td>\n",
       "      <td>0.306085</td>\n",
       "      <td>16.587678</td>\n",
       "      <td>0.563536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     MSE     NRMSE       PSNR  \\\n",
       "Gaussian Filter                              1329.173152  0.260626  18.202631   \n",
       "Mean Filter                                  1591.244992  0.293272  16.924058   \n",
       "Median Filter                                1554.300015  0.286259  17.261898   \n",
       "Conservative Filter                          1279.248365  0.246813  18.876066   \n",
       "Low pass FFT Filter                          1227.034459  0.246544  18.773191   \n",
       "Convolutional Autoencoder None               2721.356234  0.391336  14.390961   \n",
       "Convolutional Autoencoder Wiener             3600.767919  0.456471  12.832394   \n",
       "Convolutional Autoencoder Laplacian          2758.100050  0.394767  14.301804   \n",
       "Convolutional Autoencoder Gaussian Weighted  2762.551183  0.393755  14.335962   \n",
       "Dense Autoencoder                            5596.324806  0.574819  10.735354   \n",
       "Generative Adversarial Network None          1727.361495  0.304286  16.707313   \n",
       "Generative Adversarial Network Wiener        2639.014338  0.386210  14.291242   \n",
       "Generative Adversarial Network Laplacian     2381.024116  0.366836  14.871798   \n",
       "Generative Adversarial Network Weighted      1757.415785  0.306085  16.587678   \n",
       "\n",
       "                                                 SSIM  \n",
       "Gaussian Filter                              0.553287  \n",
       "Mean Filter                                  0.408735  \n",
       "Median Filter                                0.434598  \n",
       "Conservative Filter                          0.648794  \n",
       "Low pass FFT Filter                          0.593923  \n",
       "Convolutional Autoencoder None               0.187798  \n",
       "Convolutional Autoencoder Wiener             0.150496  \n",
       "Convolutional Autoencoder Laplacian          0.176320  \n",
       "Convolutional Autoencoder Gaussian Weighted  0.189528  \n",
       "Dense Autoencoder                            0.008347  \n",
       "Generative Adversarial Network None          0.552804  \n",
       "Generative Adversarial Network Wiener        0.329740  \n",
       "Generative Adversarial Network Laplacian     0.221546  \n",
       "Generative Adversarial Network Weighted      0.563536  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_noise_reduction_method_statistics(stats_gen, noise_reduction_methods)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Noise reductions methods for each noise type\n",
    "#### Structural similarity (SSIM)\n",
    "\n",
    "A high SSIM is desired"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compute finish for Gaussian Filter\n",
      "Compute finish for Mean Filter\n",
      "Compute finish for Median Filter\n",
      "Compute finish for Conservative Filter\n",
      "Compute finish for Low pass FFT Filter\n",
      "Compute finish for Convolutional Autoencoder None\n",
      "Compute finish for Convolutional Autoencoder Wiener\n",
      "Compute finish for Convolutional Autoencoder Laplacian\n",
      "Compute finish for Convolutional Autoencoder Gaussian Weighted\n",
      "Compute finish for Dense Autoencoder\n",
      "Compute finish for Generative Adversarial Network None\n",
      "Compute finish for Generative Adversarial Network Wiener\n",
      "Compute finish for Generative Adversarial Network Laplacian\n",
      "Compute finish for Generative Adversarial Network Weighted\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GaussianNoise</th>\n",
       "      <th>PoissonNoise</th>\n",
       "      <th>UniformNoise</th>\n",
       "      <th>SaltPepperNoise</th>\n",
       "      <th>SquareMaskNoise</th>\n",
       "      <th>SpeckleNoise</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gaussian Filter</th>\n",
       "      <td>0.477822</td>\n",
       "      <td>0.594880</td>\n",
       "      <td>0.467459</td>\n",
       "      <td>0.461130</td>\n",
       "      <td>0.568738</td>\n",
       "      <td>0.423297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Filter</th>\n",
       "      <td>0.334825</td>\n",
       "      <td>0.425685</td>\n",
       "      <td>0.332730</td>\n",
       "      <td>0.352467</td>\n",
       "      <td>0.397599</td>\n",
       "      <td>0.297799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median Filter</th>\n",
       "      <td>0.360238</td>\n",
       "      <td>0.460286</td>\n",
       "      <td>0.336427</td>\n",
       "      <td>0.527357</td>\n",
       "      <td>0.441198</td>\n",
       "      <td>0.313682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conservative Filter</th>\n",
       "      <td>0.623435</td>\n",
       "      <td>0.711040</td>\n",
       "      <td>0.542507</td>\n",
       "      <td>0.514270</td>\n",
       "      <td>0.825458</td>\n",
       "      <td>0.587062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low pass FFT Filter</th>\n",
       "      <td>0.576999</td>\n",
       "      <td>0.667295</td>\n",
       "      <td>0.541167</td>\n",
       "      <td>0.465155</td>\n",
       "      <td>0.645349</td>\n",
       "      <td>0.533686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder None</th>\n",
       "      <td>0.170657</td>\n",
       "      <td>0.172802</td>\n",
       "      <td>0.167828</td>\n",
       "      <td>0.133568</td>\n",
       "      <td>0.144035</td>\n",
       "      <td>0.050906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder Wiener</th>\n",
       "      <td>0.153242</td>\n",
       "      <td>0.143579</td>\n",
       "      <td>0.151217</td>\n",
       "      <td>0.123287</td>\n",
       "      <td>0.121134</td>\n",
       "      <td>0.089652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder Laplacian</th>\n",
       "      <td>0.154062</td>\n",
       "      <td>0.160932</td>\n",
       "      <td>0.152804</td>\n",
       "      <td>0.128121</td>\n",
       "      <td>0.139952</td>\n",
       "      <td>0.047140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder Gaussian Weighted</th>\n",
       "      <td>0.182608</td>\n",
       "      <td>0.176391</td>\n",
       "      <td>0.178821</td>\n",
       "      <td>0.138539</td>\n",
       "      <td>0.145706</td>\n",
       "      <td>0.054162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dense Autoencoder</th>\n",
       "      <td>0.007715</td>\n",
       "      <td>0.008659</td>\n",
       "      <td>0.007831</td>\n",
       "      <td>0.008478</td>\n",
       "      <td>0.007742</td>\n",
       "      <td>0.007611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network None</th>\n",
       "      <td>0.484086</td>\n",
       "      <td>0.570131</td>\n",
       "      <td>0.466532</td>\n",
       "      <td>0.378775</td>\n",
       "      <td>0.542702</td>\n",
       "      <td>0.289330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network Wiener</th>\n",
       "      <td>0.347697</td>\n",
       "      <td>0.345124</td>\n",
       "      <td>0.344078</td>\n",
       "      <td>0.271541</td>\n",
       "      <td>0.269340</td>\n",
       "      <td>0.344811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network Laplacian</th>\n",
       "      <td>0.168239</td>\n",
       "      <td>0.206573</td>\n",
       "      <td>0.173174</td>\n",
       "      <td>0.171502</td>\n",
       "      <td>0.177855</td>\n",
       "      <td>0.085811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network Weighted</th>\n",
       "      <td>0.550454</td>\n",
       "      <td>0.584991</td>\n",
       "      <td>0.513413</td>\n",
       "      <td>0.356037</td>\n",
       "      <td>0.589947</td>\n",
       "      <td>0.333247</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             GaussianNoise  PoissonNoise  \\\n",
       "Gaussian Filter                                   0.477822      0.594880   \n",
       "Mean Filter                                       0.334825      0.425685   \n",
       "Median Filter                                     0.360238      0.460286   \n",
       "Conservative Filter                               0.623435      0.711040   \n",
       "Low pass FFT Filter                               0.576999      0.667295   \n",
       "Convolutional Autoencoder None                    0.170657      0.172802   \n",
       "Convolutional Autoencoder Wiener                  0.153242      0.143579   \n",
       "Convolutional Autoencoder Laplacian               0.154062      0.160932   \n",
       "Convolutional Autoencoder Gaussian Weighted       0.182608      0.176391   \n",
       "Dense Autoencoder                                 0.007715      0.008659   \n",
       "Generative Adversarial Network None               0.484086      0.570131   \n",
       "Generative Adversarial Network Wiener             0.347697      0.345124   \n",
       "Generative Adversarial Network Laplacian          0.168239      0.206573   \n",
       "Generative Adversarial Network Weighted           0.550454      0.584991   \n",
       "\n",
       "                                             UniformNoise  SaltPepperNoise  \\\n",
       "Gaussian Filter                                  0.467459         0.461130   \n",
       "Mean Filter                                      0.332730         0.352467   \n",
       "Median Filter                                    0.336427         0.527357   \n",
       "Conservative Filter                              0.542507         0.514270   \n",
       "Low pass FFT Filter                              0.541167         0.465155   \n",
       "Convolutional Autoencoder None                   0.167828         0.133568   \n",
       "Convolutional Autoencoder Wiener                 0.151217         0.123287   \n",
       "Convolutional Autoencoder Laplacian              0.152804         0.128121   \n",
       "Convolutional Autoencoder Gaussian Weighted      0.178821         0.138539   \n",
       "Dense Autoencoder                                0.007831         0.008478   \n",
       "Generative Adversarial Network None              0.466532         0.378775   \n",
       "Generative Adversarial Network Wiener            0.344078         0.271541   \n",
       "Generative Adversarial Network Laplacian         0.173174         0.171502   \n",
       "Generative Adversarial Network Weighted          0.513413         0.356037   \n",
       "\n",
       "                                             SquareMaskNoise  SpeckleNoise  \n",
       "Gaussian Filter                                     0.568738      0.423297  \n",
       "Mean Filter                                         0.397599      0.297799  \n",
       "Median Filter                                       0.441198      0.313682  \n",
       "Conservative Filter                                 0.825458      0.587062  \n",
       "Low pass FFT Filter                                 0.645349      0.533686  \n",
       "Convolutional Autoencoder None                      0.144035      0.050906  \n",
       "Convolutional Autoencoder Wiener                    0.121134      0.089652  \n",
       "Convolutional Autoencoder Laplacian                 0.139952      0.047140  \n",
       "Convolutional Autoencoder Gaussian Weighted         0.145706      0.054162  \n",
       "Dense Autoencoder                                   0.007742      0.007611  \n",
       "Generative Adversarial Network None                 0.542702      0.289330  \n",
       "Generative Adversarial Network Wiener               0.269340      0.344811  \n",
       "Generative Adversarial Network Laplacian            0.177855      0.085811  \n",
       "Generative Adversarial Network Weighted             0.589947      0.333247  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stat_ssim = compute_noise_type_statistics(stats_gen, noise_reduction_methods, noise_class_list, \"SSIM\")\n",
    "df_stat_ssim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dense Autoencoder                              0.008006\n",
       "Convolutional Autoencoder Wiener               0.130352\n",
       "Convolutional Autoencoder Laplacian            0.130502\n",
       "Convolutional Autoencoder None                 0.139966\n",
       "Convolutional Autoencoder Gaussian Weighted    0.146038\n",
       "Generative Adversarial Network Laplacian       0.163859\n",
       "Generative Adversarial Network Wiener          0.320432\n",
       "Mean Filter                                    0.356851\n",
       "Median Filter                                  0.406531\n",
       "Generative Adversarial Network None            0.455259\n",
       "Generative Adversarial Network Weighted        0.488015\n",
       "Gaussian Filter                                0.498888\n",
       "Low pass FFT Filter                            0.571608\n",
       "Conservative Filter                            0.633962\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stat_ssim.mean(axis=1).sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Peak signal-to-noise ratio (PSNR)\n",
    "\n",
    "A High PSNR is desired but not in all cases. It is often used as a reconstruction loss when compressing images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compute finish for Gaussian Filter\n",
      "Compute finish for Mean Filter\n",
      "Compute finish for Median Filter\n",
      "Compute finish for Conservative Filter\n",
      "Compute finish for Low pass FFT Filter\n",
      "Compute finish for Convolutional Autoencoder None\n",
      "Compute finish for Convolutional Autoencoder Wiener\n",
      "Compute finish for Convolutional Autoencoder Laplacian\n",
      "Compute finish for Convolutional Autoencoder Gaussian Weighted\n",
      "Compute finish for Dense Autoencoder\n",
      "Compute finish for Generative Adversarial Network None\n",
      "Compute finish for Generative Adversarial Network Wiener\n",
      "Compute finish for Generative Adversarial Network Laplacian\n",
      "Compute finish for Generative Adversarial Network Weighted\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GaussianNoise</th>\n",
       "      <th>PoissonNoise</th>\n",
       "      <th>UniformNoise</th>\n",
       "      <th>SaltPepperNoise</th>\n",
       "      <th>SquareMaskNoise</th>\n",
       "      <th>SpeckleNoise</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gaussian Filter</th>\n",
       "      <td>17.737204</td>\n",
       "      <td>21.491647</td>\n",
       "      <td>18.247063</td>\n",
       "      <td>19.310966</td>\n",
       "      <td>15.725036</td>\n",
       "      <td>13.446155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Filter</th>\n",
       "      <td>16.759011</td>\n",
       "      <td>19.313551</td>\n",
       "      <td>17.145060</td>\n",
       "      <td>18.531824</td>\n",
       "      <td>15.527770</td>\n",
       "      <td>12.942427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median Filter</th>\n",
       "      <td>17.211851</td>\n",
       "      <td>20.058714</td>\n",
       "      <td>17.467064</td>\n",
       "      <td>20.095965</td>\n",
       "      <td>15.051523</td>\n",
       "      <td>12.910238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conservative Filter</th>\n",
       "      <td>19.186584</td>\n",
       "      <td>24.178939</td>\n",
       "      <td>18.640867</td>\n",
       "      <td>15.864977</td>\n",
       "      <td>16.731168</td>\n",
       "      <td>13.856069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low pass FFT Filter</th>\n",
       "      <td>18.652396</td>\n",
       "      <td>22.632896</td>\n",
       "      <td>18.817833</td>\n",
       "      <td>17.733438</td>\n",
       "      <td>16.663705</td>\n",
       "      <td>13.754239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder None</th>\n",
       "      <td>13.394846</td>\n",
       "      <td>13.375175</td>\n",
       "      <td>13.340671</td>\n",
       "      <td>12.106139</td>\n",
       "      <td>12.054274</td>\n",
       "      <td>8.845925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder Wiener</th>\n",
       "      <td>13.009035</td>\n",
       "      <td>12.285785</td>\n",
       "      <td>13.007490</td>\n",
       "      <td>11.540726</td>\n",
       "      <td>11.082185</td>\n",
       "      <td>10.757584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder Laplacian</th>\n",
       "      <td>13.274382</td>\n",
       "      <td>13.270463</td>\n",
       "      <td>13.239311</td>\n",
       "      <td>12.120894</td>\n",
       "      <td>12.355106</td>\n",
       "      <td>8.708507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder Gaussian Weighted</th>\n",
       "      <td>13.577523</td>\n",
       "      <td>13.347194</td>\n",
       "      <td>13.436870</td>\n",
       "      <td>12.114653</td>\n",
       "      <td>11.738948</td>\n",
       "      <td>8.830486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dense Autoencoder</th>\n",
       "      <td>10.751270</td>\n",
       "      <td>10.719879</td>\n",
       "      <td>10.754181</td>\n",
       "      <td>10.731339</td>\n",
       "      <td>10.729974</td>\n",
       "      <td>10.671110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network None</th>\n",
       "      <td>13.742000</td>\n",
       "      <td>15.637640</td>\n",
       "      <td>14.275434</td>\n",
       "      <td>13.552867</td>\n",
       "      <td>13.582706</td>\n",
       "      <td>9.524040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network Wiener</th>\n",
       "      <td>15.367379</td>\n",
       "      <td>14.230581</td>\n",
       "      <td>15.320523</td>\n",
       "      <td>12.494241</td>\n",
       "      <td>11.851094</td>\n",
       "      <td>13.368718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network Laplacian</th>\n",
       "      <td>12.837883</td>\n",
       "      <td>14.061768</td>\n",
       "      <td>13.270876</td>\n",
       "      <td>13.587154</td>\n",
       "      <td>12.577098</td>\n",
       "      <td>9.279503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network Weighted</th>\n",
       "      <td>14.029319</td>\n",
       "      <td>15.926594</td>\n",
       "      <td>14.662762</td>\n",
       "      <td>12.502930</td>\n",
       "      <td>13.307536</td>\n",
       "      <td>9.792591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             GaussianNoise  PoissonNoise  \\\n",
       "Gaussian Filter                                  17.737204     21.491647   \n",
       "Mean Filter                                      16.759011     19.313551   \n",
       "Median Filter                                    17.211851     20.058714   \n",
       "Conservative Filter                              19.186584     24.178939   \n",
       "Low pass FFT Filter                              18.652396     22.632896   \n",
       "Convolutional Autoencoder None                   13.394846     13.375175   \n",
       "Convolutional Autoencoder Wiener                 13.009035     12.285785   \n",
       "Convolutional Autoencoder Laplacian              13.274382     13.270463   \n",
       "Convolutional Autoencoder Gaussian Weighted      13.577523     13.347194   \n",
       "Dense Autoencoder                                10.751270     10.719879   \n",
       "Generative Adversarial Network None              13.742000     15.637640   \n",
       "Generative Adversarial Network Wiener            15.367379     14.230581   \n",
       "Generative Adversarial Network Laplacian         12.837883     14.061768   \n",
       "Generative Adversarial Network Weighted          14.029319     15.926594   \n",
       "\n",
       "                                             UniformNoise  SaltPepperNoise  \\\n",
       "Gaussian Filter                                 18.247063        19.310966   \n",
       "Mean Filter                                     17.145060        18.531824   \n",
       "Median Filter                                   17.467064        20.095965   \n",
       "Conservative Filter                             18.640867        15.864977   \n",
       "Low pass FFT Filter                             18.817833        17.733438   \n",
       "Convolutional Autoencoder None                  13.340671        12.106139   \n",
       "Convolutional Autoencoder Wiener                13.007490        11.540726   \n",
       "Convolutional Autoencoder Laplacian             13.239311        12.120894   \n",
       "Convolutional Autoencoder Gaussian Weighted     13.436870        12.114653   \n",
       "Dense Autoencoder                               10.754181        10.731339   \n",
       "Generative Adversarial Network None             14.275434        13.552867   \n",
       "Generative Adversarial Network Wiener           15.320523        12.494241   \n",
       "Generative Adversarial Network Laplacian        13.270876        13.587154   \n",
       "Generative Adversarial Network Weighted         14.662762        12.502930   \n",
       "\n",
       "                                             SquareMaskNoise  SpeckleNoise  \n",
       "Gaussian Filter                                    15.725036     13.446155  \n",
       "Mean Filter                                        15.527770     12.942427  \n",
       "Median Filter                                      15.051523     12.910238  \n",
       "Conservative Filter                                16.731168     13.856069  \n",
       "Low pass FFT Filter                                16.663705     13.754239  \n",
       "Convolutional Autoencoder None                     12.054274      8.845925  \n",
       "Convolutional Autoencoder Wiener                   11.082185     10.757584  \n",
       "Convolutional Autoencoder Laplacian                12.355106      8.708507  \n",
       "Convolutional Autoencoder Gaussian Weighted        11.738948      8.830486  \n",
       "Dense Autoencoder                                  10.729974     10.671110  \n",
       "Generative Adversarial Network None                13.582706      9.524040  \n",
       "Generative Adversarial Network Wiener              11.851094     13.368718  \n",
       "Generative Adversarial Network Laplacian           12.577098      9.279503  \n",
       "Generative Adversarial Network Weighted            13.307536      9.792591  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stat_psnr = compute_noise_type_statistics(stats_gen, noise_reduction_methods, noise_class_list, \"PSNR\")\n",
    "df_stat_psnr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dense Autoencoder                              10.726292\n",
       "Convolutional Autoencoder Wiener               11.947134\n",
       "Convolutional Autoencoder Laplacian            12.161444\n",
       "Convolutional Autoencoder Gaussian Weighted    12.174279\n",
       "Convolutional Autoencoder None                 12.186172\n",
       "Generative Adversarial Network Laplacian       12.602380\n",
       "Generative Adversarial Network Weighted        13.370288\n",
       "Generative Adversarial Network None            13.385781\n",
       "Generative Adversarial Network Wiener          13.772089\n",
       "Mean Filter                                    16.703274\n",
       "Median Filter                                  17.132559\n",
       "Gaussian Filter                                17.659679\n",
       "Low pass FFT Filter                            18.042418\n",
       "Conservative Filter                            18.076434\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stat_psnr.mean(axis=1).sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean squared error (MSE)\n",
    "\n",
    "A low MSE is desired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compute finish for Gaussian Filter\n",
      "Compute finish for Mean Filter\n",
      "Compute finish for Median Filter\n",
      "Compute finish for Conservative Filter\n",
      "Compute finish for Low pass FFT Filter\n",
      "Compute finish for Convolutional Autoencoder None\n",
      "Compute finish for Convolutional Autoencoder Wiener\n",
      "Compute finish for Convolutional Autoencoder Laplacian\n",
      "Compute finish for Convolutional Autoencoder Gaussian Weighted\n",
      "Compute finish for Dense Autoencoder\n",
      "Compute finish for Generative Adversarial Network None\n",
      "Compute finish for Generative Adversarial Network Wiener\n",
      "Compute finish for Generative Adversarial Network Laplacian\n",
      "Compute finish for Generative Adversarial Network Weighted\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GaussianNoise</th>\n",
       "      <th>PoissonNoise</th>\n",
       "      <th>UniformNoise</th>\n",
       "      <th>SaltPepperNoise</th>\n",
       "      <th>SquareMaskNoise</th>\n",
       "      <th>SpeckleNoise</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gaussian Filter</th>\n",
       "      <td>1124.570495</td>\n",
       "      <td>493.254421</td>\n",
       "      <td>993.518736</td>\n",
       "      <td>779.093287</td>\n",
       "      <td>1541.567639</td>\n",
       "      <td>3150.321034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Filter</th>\n",
       "      <td>1433.028501</td>\n",
       "      <td>803.763127</td>\n",
       "      <td>1284.446356</td>\n",
       "      <td>955.902100</td>\n",
       "      <td>1831.839209</td>\n",
       "      <td>3295.935631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median Filter</th>\n",
       "      <td>1231.553650</td>\n",
       "      <td>695.524695</td>\n",
       "      <td>1186.071972</td>\n",
       "      <td>687.841845</td>\n",
       "      <td>2250.392668</td>\n",
       "      <td>3350.129802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conservative Filter</th>\n",
       "      <td>809.867014</td>\n",
       "      <td>252.853703</td>\n",
       "      <td>897.172107</td>\n",
       "      <td>1686.537083</td>\n",
       "      <td>1556.788410</td>\n",
       "      <td>2889.059361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low pass FFT Filter</th>\n",
       "      <td>913.941910</td>\n",
       "      <td>366.405746</td>\n",
       "      <td>865.722041</td>\n",
       "      <td>1085.902576</td>\n",
       "      <td>1592.090731</td>\n",
       "      <td>2859.570051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder None</th>\n",
       "      <td>3129.040444</td>\n",
       "      <td>3034.636450</td>\n",
       "      <td>3039.936811</td>\n",
       "      <td>4022.361929</td>\n",
       "      <td>4308.368148</td>\n",
       "      <td>8725.844037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder Wiener</th>\n",
       "      <td>3417.657489</td>\n",
       "      <td>3929.002621</td>\n",
       "      <td>3396.565342</td>\n",
       "      <td>4663.474941</td>\n",
       "      <td>5110.703151</td>\n",
       "      <td>5725.055511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder Laplacian</th>\n",
       "      <td>3180.626613</td>\n",
       "      <td>3109.815786</td>\n",
       "      <td>3127.314652</td>\n",
       "      <td>4088.605817</td>\n",
       "      <td>3860.595761</td>\n",
       "      <td>8659.940213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder Gaussian Weighted</th>\n",
       "      <td>2963.625984</td>\n",
       "      <td>3050.478009</td>\n",
       "      <td>2931.232611</td>\n",
       "      <td>4060.741866</td>\n",
       "      <td>4525.568912</td>\n",
       "      <td>8597.734878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dense Autoencoder</th>\n",
       "      <td>5570.849309</td>\n",
       "      <td>5616.525660</td>\n",
       "      <td>5569.376098</td>\n",
       "      <td>5604.436884</td>\n",
       "      <td>5629.916640</td>\n",
       "      <td>5675.435008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network None</th>\n",
       "      <td>2713.915019</td>\n",
       "      <td>1802.821201</td>\n",
       "      <td>2454.066660</td>\n",
       "      <td>2858.391789</td>\n",
       "      <td>3013.587130</td>\n",
       "      <td>7185.218307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network Wiener</th>\n",
       "      <td>1895.293035</td>\n",
       "      <td>2543.015241</td>\n",
       "      <td>1992.690779</td>\n",
       "      <td>3809.090004</td>\n",
       "      <td>4376.064938</td>\n",
       "      <td>3135.386103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network Laplacian</th>\n",
       "      <td>3398.804498</td>\n",
       "      <td>2599.701527</td>\n",
       "      <td>3112.796434</td>\n",
       "      <td>2910.812208</td>\n",
       "      <td>3515.235354</td>\n",
       "      <td>7927.480693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network Weighted</th>\n",
       "      <td>2577.352507</td>\n",
       "      <td>1705.731965</td>\n",
       "      <td>2278.020233</td>\n",
       "      <td>3598.573524</td>\n",
       "      <td>3500.919258</td>\n",
       "      <td>7183.320236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             GaussianNoise  PoissonNoise  \\\n",
       "Gaussian Filter                                1124.570495    493.254421   \n",
       "Mean Filter                                    1433.028501    803.763127   \n",
       "Median Filter                                  1231.553650    695.524695   \n",
       "Conservative Filter                             809.867014    252.853703   \n",
       "Low pass FFT Filter                             913.941910    366.405746   \n",
       "Convolutional Autoencoder None                 3129.040444   3034.636450   \n",
       "Convolutional Autoencoder Wiener               3417.657489   3929.002621   \n",
       "Convolutional Autoencoder Laplacian            3180.626613   3109.815786   \n",
       "Convolutional Autoencoder Gaussian Weighted    2963.625984   3050.478009   \n",
       "Dense Autoencoder                              5570.849309   5616.525660   \n",
       "Generative Adversarial Network None            2713.915019   1802.821201   \n",
       "Generative Adversarial Network Wiener          1895.293035   2543.015241   \n",
       "Generative Adversarial Network Laplacian       3398.804498   2599.701527   \n",
       "Generative Adversarial Network Weighted        2577.352507   1705.731965   \n",
       "\n",
       "                                             UniformNoise  SaltPepperNoise  \\\n",
       "Gaussian Filter                                993.518736       779.093287   \n",
       "Mean Filter                                   1284.446356       955.902100   \n",
       "Median Filter                                 1186.071972       687.841845   \n",
       "Conservative Filter                            897.172107      1686.537083   \n",
       "Low pass FFT Filter                            865.722041      1085.902576   \n",
       "Convolutional Autoencoder None                3039.936811      4022.361929   \n",
       "Convolutional Autoencoder Wiener              3396.565342      4663.474941   \n",
       "Convolutional Autoencoder Laplacian           3127.314652      4088.605817   \n",
       "Convolutional Autoencoder Gaussian Weighted   2931.232611      4060.741866   \n",
       "Dense Autoencoder                             5569.376098      5604.436884   \n",
       "Generative Adversarial Network None           2454.066660      2858.391789   \n",
       "Generative Adversarial Network Wiener         1992.690779      3809.090004   \n",
       "Generative Adversarial Network Laplacian      3112.796434      2910.812208   \n",
       "Generative Adversarial Network Weighted       2278.020233      3598.573524   \n",
       "\n",
       "                                             SquareMaskNoise  SpeckleNoise  \n",
       "Gaussian Filter                                  1541.567639   3150.321034  \n",
       "Mean Filter                                      1831.839209   3295.935631  \n",
       "Median Filter                                    2250.392668   3350.129802  \n",
       "Conservative Filter                              1556.788410   2889.059361  \n",
       "Low pass FFT Filter                              1592.090731   2859.570051  \n",
       "Convolutional Autoencoder None                   4308.368148   8725.844037  \n",
       "Convolutional Autoencoder Wiener                 5110.703151   5725.055511  \n",
       "Convolutional Autoencoder Laplacian              3860.595761   8659.940213  \n",
       "Convolutional Autoencoder Gaussian Weighted      4525.568912   8597.734878  \n",
       "Dense Autoencoder                                5629.916640   5675.435008  \n",
       "Generative Adversarial Network None              3013.587130   7185.218307  \n",
       "Generative Adversarial Network Wiener            4376.064938   3135.386103  \n",
       "Generative Adversarial Network Laplacian         3515.235354   7927.480693  \n",
       "Generative Adversarial Network Weighted          3500.919258   7183.320236  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stat_mse = compute_noise_type_statistics(stats_gen, noise_reduction_methods, noise_class_list, \"MSE\")\n",
    "df_stat_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low pass FFT Filter                            1280.605509\n",
       "Gaussian Filter                                1347.054269\n",
       "Conservative Filter                            1348.712947\n",
       "Median Filter                                  1566.919105\n",
       "Mean Filter                                    1600.819154\n",
       "Generative Adversarial Network Wiener          2958.590017\n",
       "Generative Adversarial Network None            3338.000018\n",
       "Generative Adversarial Network Weighted        3473.986287\n",
       "Generative Adversarial Network Laplacian       3910.805119\n",
       "Convolutional Autoencoder Laplacian            4337.816474\n",
       "Convolutional Autoencoder Gaussian Weighted    4354.897043\n",
       "Convolutional Autoencoder Wiener               4373.743176\n",
       "Convolutional Autoencoder None                 4376.697970\n",
       "Dense Autoencoder                              5611.089933\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stat_mse.mean(axis=1).sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalized root mean squared error (NRMSE)\n",
    "\n",
    "A low NRMSE is desired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compute finish for Gaussian Filter\n",
      "Compute finish for Mean Filter\n",
      "Compute finish for Median Filter\n",
      "Compute finish for Conservative Filter\n",
      "Compute finish for Low pass FFT Filter\n",
      "Compute finish for Convolutional Autoencoder None\n",
      "Compute finish for Convolutional Autoencoder Wiener\n",
      "Compute finish for Convolutional Autoencoder Laplacian\n",
      "Compute finish for Convolutional Autoencoder Gaussian Weighted\n",
      "Compute finish for Dense Autoencoder\n",
      "Compute finish for Generative Adversarial Network None\n",
      "Compute finish for Generative Adversarial Network Wiener\n",
      "Compute finish for Generative Adversarial Network Laplacian\n",
      "Compute finish for Generative Adversarial Network Weighted\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GaussianNoise</th>\n",
       "      <th>PoissonNoise</th>\n",
       "      <th>UniformNoise</th>\n",
       "      <th>SaltPepperNoise</th>\n",
       "      <th>SquareMaskNoise</th>\n",
       "      <th>SpeckleNoise</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gaussian Filter</th>\n",
       "      <td>0.254472</td>\n",
       "      <td>0.169755</td>\n",
       "      <td>0.243258</td>\n",
       "      <td>0.214495</td>\n",
       "      <td>0.326090</td>\n",
       "      <td>0.425347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Filter</th>\n",
       "      <td>0.284917</td>\n",
       "      <td>0.217319</td>\n",
       "      <td>0.275639</td>\n",
       "      <td>0.238112</td>\n",
       "      <td>0.325120</td>\n",
       "      <td>0.443833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median Filter</th>\n",
       "      <td>0.271618</td>\n",
       "      <td>0.200910</td>\n",
       "      <td>0.265566</td>\n",
       "      <td>0.201460</td>\n",
       "      <td>0.342025</td>\n",
       "      <td>0.450126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conservative Filter</th>\n",
       "      <td>0.219445</td>\n",
       "      <td>0.121468</td>\n",
       "      <td>0.230254</td>\n",
       "      <td>0.319423</td>\n",
       "      <td>0.289252</td>\n",
       "      <td>0.407096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low pass FFT Filter</th>\n",
       "      <td>0.235073</td>\n",
       "      <td>0.146780</td>\n",
       "      <td>0.227272</td>\n",
       "      <td>0.254640</td>\n",
       "      <td>0.293645</td>\n",
       "      <td>0.402684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder None</th>\n",
       "      <td>0.425577</td>\n",
       "      <td>0.425200</td>\n",
       "      <td>0.422716</td>\n",
       "      <td>0.489187</td>\n",
       "      <td>0.495308</td>\n",
       "      <td>0.718522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder Wiener</th>\n",
       "      <td>0.448570</td>\n",
       "      <td>0.481554</td>\n",
       "      <td>0.443707</td>\n",
       "      <td>0.523390</td>\n",
       "      <td>0.542175</td>\n",
       "      <td>0.577330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder Laplacian</th>\n",
       "      <td>0.426345</td>\n",
       "      <td>0.429614</td>\n",
       "      <td>0.428934</td>\n",
       "      <td>0.493857</td>\n",
       "      <td>0.485077</td>\n",
       "      <td>0.713890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Autoencoder Gaussian Weighted</th>\n",
       "      <td>0.419573</td>\n",
       "      <td>0.425602</td>\n",
       "      <td>0.419047</td>\n",
       "      <td>0.490133</td>\n",
       "      <td>0.508238</td>\n",
       "      <td>0.708841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dense Autoencoder</th>\n",
       "      <td>0.573488</td>\n",
       "      <td>0.575723</td>\n",
       "      <td>0.573323</td>\n",
       "      <td>0.575206</td>\n",
       "      <td>0.578540</td>\n",
       "      <td>0.578559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network None</th>\n",
       "      <td>0.402913</td>\n",
       "      <td>0.325044</td>\n",
       "      <td>0.377213</td>\n",
       "      <td>0.409559</td>\n",
       "      <td>0.434152</td>\n",
       "      <td>0.652320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network Wiener</th>\n",
       "      <td>0.336372</td>\n",
       "      <td>0.383227</td>\n",
       "      <td>0.339222</td>\n",
       "      <td>0.469042</td>\n",
       "      <td>0.490783</td>\n",
       "      <td>0.428971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network Laplacian</th>\n",
       "      <td>0.443175</td>\n",
       "      <td>0.392270</td>\n",
       "      <td>0.425841</td>\n",
       "      <td>0.413033</td>\n",
       "      <td>0.464257</td>\n",
       "      <td>0.669226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generative Adversarial Network Weighted</th>\n",
       "      <td>0.375721</td>\n",
       "      <td>0.315197</td>\n",
       "      <td>0.364289</td>\n",
       "      <td>0.462724</td>\n",
       "      <td>0.435356</td>\n",
       "      <td>0.634400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             GaussianNoise  PoissonNoise  \\\n",
       "Gaussian Filter                                   0.254472      0.169755   \n",
       "Mean Filter                                       0.284917      0.217319   \n",
       "Median Filter                                     0.271618      0.200910   \n",
       "Conservative Filter                               0.219445      0.121468   \n",
       "Low pass FFT Filter                               0.235073      0.146780   \n",
       "Convolutional Autoencoder None                    0.425577      0.425200   \n",
       "Convolutional Autoencoder Wiener                  0.448570      0.481554   \n",
       "Convolutional Autoencoder Laplacian               0.426345      0.429614   \n",
       "Convolutional Autoencoder Gaussian Weighted       0.419573      0.425602   \n",
       "Dense Autoencoder                                 0.573488      0.575723   \n",
       "Generative Adversarial Network None               0.402913      0.325044   \n",
       "Generative Adversarial Network Wiener             0.336372      0.383227   \n",
       "Generative Adversarial Network Laplacian          0.443175      0.392270   \n",
       "Generative Adversarial Network Weighted           0.375721      0.315197   \n",
       "\n",
       "                                             UniformNoise  SaltPepperNoise  \\\n",
       "Gaussian Filter                                  0.243258         0.214495   \n",
       "Mean Filter                                      0.275639         0.238112   \n",
       "Median Filter                                    0.265566         0.201460   \n",
       "Conservative Filter                              0.230254         0.319423   \n",
       "Low pass FFT Filter                              0.227272         0.254640   \n",
       "Convolutional Autoencoder None                   0.422716         0.489187   \n",
       "Convolutional Autoencoder Wiener                 0.443707         0.523390   \n",
       "Convolutional Autoencoder Laplacian              0.428934         0.493857   \n",
       "Convolutional Autoencoder Gaussian Weighted      0.419047         0.490133   \n",
       "Dense Autoencoder                                0.573323         0.575206   \n",
       "Generative Adversarial Network None              0.377213         0.409559   \n",
       "Generative Adversarial Network Wiener            0.339222         0.469042   \n",
       "Generative Adversarial Network Laplacian         0.425841         0.413033   \n",
       "Generative Adversarial Network Weighted          0.364289         0.462724   \n",
       "\n",
       "                                             SquareMaskNoise  SpeckleNoise  \n",
       "Gaussian Filter                                     0.326090      0.425347  \n",
       "Mean Filter                                         0.325120      0.443833  \n",
       "Median Filter                                       0.342025      0.450126  \n",
       "Conservative Filter                                 0.289252      0.407096  \n",
       "Low pass FFT Filter                                 0.293645      0.402684  \n",
       "Convolutional Autoencoder None                      0.495308      0.718522  \n",
       "Convolutional Autoencoder Wiener                    0.542175      0.577330  \n",
       "Convolutional Autoencoder Laplacian                 0.485077      0.713890  \n",
       "Convolutional Autoencoder Gaussian Weighted         0.508238      0.708841  \n",
       "Dense Autoencoder                                   0.578540      0.578559  \n",
       "Generative Adversarial Network None                 0.434152      0.652320  \n",
       "Generative Adversarial Network Wiener               0.490783      0.428971  \n",
       "Generative Adversarial Network Laplacian            0.464257      0.669226  \n",
       "Generative Adversarial Network Weighted             0.435356      0.634400  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stat_nrmse = compute_noise_type_statistics(stats_gen, noise_reduction_methods, noise_class_list, \"NRMSE\")\n",
    "df_stat_nrmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low pass FFT Filter                            0.260016\n",
       "Conservative Filter                            0.264490\n",
       "Gaussian Filter                                0.272236\n",
       "Median Filter                                  0.288617\n",
       "Mean Filter                                    0.297490\n",
       "Generative Adversarial Network Wiener          0.407936\n",
       "Generative Adversarial Network Weighted        0.431281\n",
       "Generative Adversarial Network None            0.433533\n",
       "Generative Adversarial Network Laplacian       0.467967\n",
       "Convolutional Autoencoder Gaussian Weighted    0.495239\n",
       "Convolutional Autoencoder None                 0.496085\n",
       "Convolutional Autoencoder Laplacian            0.496286\n",
       "Convolutional Autoencoder Wiener               0.502788\n",
       "Dense Autoencoder                              0.575807\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stat_nrmse.mean(axis=1).sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "As we can observe the conservative filter has the best score on all most all type of noises.  \n",
    "It may be because the changes in the images are very small so it the denoised image is similar to the original image.  \n",
    "This is particularly true when the images are not very noised and the performance would be different if the images had more noise.  \n",
    "\n",
    "The generative adversial neural network reduces the noise quite well, but as the image is generated, the colors are sometimes different which is not the case of other filters.  \n",
    "When we sum all these small differences pixelwise it is expected to have a high error.  \n",
    "Even though the results are visually good.  \n",
    "\n",
    "For each filter the metrics where computed on all kind of noises.  \n",
    "Usually we should use a specific filter on a particular noise as they perform better on the approriate noise.  \n",
    "\n",
    "Comparing the neural networks, the generative adversial neural network has the best performances.  \n",
    "If we had a GPU with more RAM, we could increase the depth of the GAN by putting more convolutional blocks.  \n",
    "This is also the reason why we reduced the image's size to 100x100 and used grayscale.  \n",
    "An other improvement would be to implement the style loss in order to keep better texture details and so improve the performance.  \n",
    "\n",
    "Another point to note is that the structural similarity has been criticized for not being more corelated to the human's perception as the mean squarred error.  \n",
    "Comparing the images visually is sometimes more valuable than comparing metrics.  \n",
    "\n",
    "This conclusion shouldn't be used to say that classical filters (gaussian etc) perform always better than neural networks in all cases.  "
   ]
  }
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