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Robust and Portable Fuzzy Image Detection Algorithm

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EsFFT

When using the classic FFT method to detect blurry images, clear images with sparse textures are often classified as blurry, while blurry images with rich textures are classified as clear.

To address this difficulty, we propose an improved version of the ESFFT fuzzy detection method, with the core idea of only considering the frequency response intensity near the texture.

The detection results of the traditional FFT method are as follows:

Clear images with sparse textures

9ad8ac80594f5949070337a9cd69661

Blurred images with rich textures

ecab2bb658c5882389ca529d69295dc

The detection results of the ESFFT method are as follows:

Clear images with sparse textures and it effective response area

e4622134ff55b7838c54a70a92dd23f a4ba840476af46a659689994b7c0134

Blurred images with rich textures and it effective response area

5f9385bccd0feebef4b02d34ea1c17c 237368f9bed2913843c674327774153

Run Demo

numpy_demo.py is a demo file based on numpy, through which you can reproduce the effect shown in the above figure.

Install

pip install matplotlib==3.5
pip install opencv-python
pip install numpy

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