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[STATUS - ACTIVE]

[DIDH] - Towards Domain Invariant Single Image Dehazing

Official Pytorch based implementation.

To-Do List

  • Paper Link
  • Training Code
  • Trained Models
  • Trained Models corresponding to experiments
  • Citation

Results

Standard Performance Evaluation of different Dehazing algorithms on NTIRE-19 dataset

Blind Evaluation of different Dehazing algorithms on NTIRE-18 dataset

Dependencies and Installation

  • python=3.8
  • PyTorch=1.6
  • tqdm
  • numpy
  • matplotlib
  • PIL
  • piq
  • OpenCV=4.4

Dataset Details

Dataset Name PSNR / SSIM Resolution Type Paper Link
I-Haze NTIRE-18 14.60 / 0.67 4177 x 3134 Real paper
O-Haze NTIRE-18 14.60 / 0.67 4177 x 3134 Real paper
NTIRE-19 9.11 / 0.49 1600 x 1200 Real paper
NTIRE-20 10.42 / 0.46 1600 x 1200 Real paper
SOTS-IN 11.97 / 0.69 620 x 460 Synthetic paper
SOTS-OUT 15.92 / 0.81 550 x 478 Synthetic paper
HazeRD 14.60 / 0.67 3492 x 2558 Synthetic paper

MODEL ZOO - Checkpoints of Networks

Trained on Synthetic (RESIDE-Indoor) dataset -> Trained_Models

Model SOTS-IN (PSNR / SSIM) SOTS-OUT (PSNR / SSIM) NTIRE-19 (PSNR / SSIM) NTIRE-20 (PSNR / SSIM)
DuRN-US 32.12 / 0.98 19.55 / 0.83 10.81 / 0.51 11.27 / 0.51
FFA-Net 36.36 / 0.98 20.05 / 0.84 10.97 / 0.42 10.70 / 0.44
Wavelet-UNet 20.02 / 0.75 17.75 / 0.67 11.48 / 0.47 10.88 / 0.36
MSNet 32.04 / 0.98 20.70 / 0.86 9.90 / 0.51 11.16 / 0.51
SNDN 24.68 / 0.91 16.02 / 0.69 10.13 / 0.45 10.64 / 0.43
GridDehazeNet 32.14 / 0.98 16.22 / 0.76 9.50 / 0.49 9.01 / 0.40
PFFNet 26.58 / 0.92 14.63 / 0.65 11.38 / 0.51 11.14 / 0.43
Ours 38.91 / 0.98 25.75 / 0.84 16.21 / 0.78 16.28 / 0.67

Trained on Synthetic (RESIDE-Outdoor) dataset -> Trained_Models

Model SOTS-IN (PSNR / SSIM) SOTS-OUT (PSNR / SSIM) NTIRE-19 (PSNR / SSIM) NTIRE-20 (PSNR / SSIM)
DuRN-US 15.95 / 0.76 19.41 / 0.81 11.04 / 0.51 11.73 / 0.46
FFA-Net 18.96 / 0.86 30.88 / 0.93 9.64 / 0.50 10.90 / 0.48
Wavelet-UNet 16.26 / 0.73 21.95 / 0.76 10.36 / 0.49 11.05 / 0.43
MSNet 21.75 / 0.88 29.80 / 0.93 09.56 / 0.48 11.35 / 0.51
SNDN 25.30 / 0.91 24.31 / 0.88 11.74 / 0.49 11.95 / 0.52
GridDehazeNet 20.99 / 0.89 29.18 / 0.93 10.16 / 0.50 11.23 / 0.49
PFFNet 20.32 / 0.85 27.65 / 0.91 10.75 / 0.50 11.55 / 0.52
Ours 26.90 / 0.76 30.40 / 0.94 13.36 / 0.52 12.68 / 0.52

Trained on Real (NTIRE-19) dataset -> Trained_Models

Model SOTS-IN (PSNR / SSIM) SOTS-OUT (PSNR / SSIM) NTIRE-19 (PSNR / SSIM) NTIRE-20 (PSNR / SSIM)
DuRN-US 11.44 / 0.59 13.05 / 0.61 13.63 / 0.57 12.97 / 0.52
FFA-Net 12.16 / 0.55 14.36 / 0.59 14.01 / 0.56 14.71 / 0.57
Wavelet-UNet 13.57 / 0.41 13.05 / 0.44 12.85 / 0.39 12.08 / 0.24
MSNet 13.33 / 0.55 13.85 / 0.56 13.32 / 0.53 12.63 / 0.32
SNDN 12.56 / 0.66 14.11 / 0.70 13.54 / 0.54 14.93 / 0.51
GridDehazeNet 14.57 / 0.59 13.47 / 0.60 12.96 / 0.50 12.07 / 0.32
PFFNet 13.51 / 0.50 14.57 / 0.53 13.29 / 0.52 12.99 / 0.31
Ours 19.28 / 0.66 18.17 / 0.87 19.47 / 0.75 20.33 / 0.77

Trained on Real (NTIRE-20) dataset -> Trained_Models

Model SOTS-IN (PSNR / SSIM) SOTS-OUT (PSNR / SSIM) NTIRE-19 (PSNR / SSIM) NTIRE-20 (PSNR / SSIM)
DuRN-US 9.43 / 0.63 11.92 / 0.66 11.63 / 0.52 15.27 / 0.50
FFA-Net 9.96 / 0.63 14.88 / 0.75 12.43 / 0.52 18.11 / 0.66
Wavelet-UNet 12.04 / 0.32 13.85 / 0.41 11.46 / 0.28 12.08 / 0.21
MSNet 9.16 / 0.51 10.66 / 0.56 12.04 / 0.50 14.06 / 0.50
SNDN 12.03 / 0.67 14.14 / 0.73 11.73 / 0.52 13.93 / 0.52
GridDehazeNet 11.60 / 0.58 12.75 / 0.72 13.39 / 0.52 15.32 / 0.60
PFFNet 8.82 / 0.47 12.00 / 0.53 11.54 / 0.49 14.50 / 0.36
Ours 19.53 / 0.71 18.69 / 0.79 17.24 / 0.66 21.17 / 0.78

Trained on Aggregated dataset -> Trained_Models

Model SOTS-IN (PSNR / SSIM) SOTS-OUT (PSNR / SSIM) NTIRE-19 (PSNR / SSIM) NTIRE-20 (PSNR / SSIM)
DuRN-US 26.80 / 0.95 29.59 / 0.91 15.96 / 0.61 19.88 / 0.69
FFA-Net 19.15 / 0.85 25.53 / 0.89 14.06 / 0.54 15.93 / 0.59
Wavelet-UNet 15.46 / 0.65 19.21 / 0.67 12.23 / 0.51 12.66 / 0.42
MSNet 24.38 / 0.90 26.79 / 0.90 14.65 / 0.59 15.17 / 0.63
SNDN 22.94 / 0.88 25.49 / 0.89 14.66 / 0.59 18.65 / 0.67
GridDehazeNet 22.87 / 0.91 27.18 / 0.91 13.97 / 0.56 17.02 / 0.68
PFFNet 23.39 / 0.87 25.80 / 0.87 13.50 / 0.48 14.77 / 0.57
Ours 25.39 / 0.80 19.47 / 0.75 22.80 / 0.82 23.66 / 0.88

Blind Evaluation on Haze-RD and NTIRE-18 dataset

Algorithm Haze-RD (PSNR/SSIM) NTIRE-18 (PSNR/SSIM)
DuRN-US 15.26 / 0.83 18.85 / 0.71
FFA-Net 15.77 / 0.83 16.16 / 0.64
Wavelet 16.30 / 0.82 16.01 / 0.70
MSNet 14.42 / 0.80 17.42 / 0.66
SNDN 16.05 / 0.82 18.08 / 0.70
GridDehazeNet 14.58 / 0.81 18.26 / 0.74
PFFNet 15.20 / 0.77 15.66 / 0.58
DA-Dehaze 16.21 / 0.78 16.28 / 0.67
Ours 21.42 / 0.81 24.14 / 0.80

Code Details

Create a 1-1 list corresponding to ground truth and hazy image

  1. Update location of datasets in gen_paired_datasets.py
  2. Comment out datasets that are not needed and run,
python gen_paired_dataset.py

Working of Greedy Localized Data Augmentation

To see how localized data augmentation works,

python dataloader.py

Train

python train.py

Test

python eval.py