Single Image Haze Removal Using Dark Channel Prior - Algorithm and Implementation

In Kaiming He's seminal paper "Single Image Haze Removal Using Dark Channel Prior," the author presents an innovative image dehazing algorithm that demonstrates outstanding performance on outdoor images. The core innovation lies in the dark channel prior, which serves as an effective statistical prior that enables more accurate haze estimation in images. The algorithm implementation follows a systematic approach: first, it calculates the dark channel prior for each pixel by finding the minimum intensity across color channels within a local patch. This dark channel map effectively captures haze concentration information. The algorithm then estimates atmospheric light by selecting the top brightest pixels from this dark channel map. Using these parameters, it computes the transmission map that represents how much haze is present at each pixel location. Key algorithmic steps include: - Dark channel computation using minimum filtering across RGB channels - Atmospheric light estimation from the brightest 0.1% pixels in the dark channel - Transmission map estimation with soft matting for refinement - Final haze-free image reconstruction using the atmospheric scattering model The method has been proven highly effective for diverse outdoor image scenarios, significantly enhancing image quality through precise haze removal. The implementation typically involves MATLAB or Python code that processes color images through these sequential operations, with parameters adjustable for different haze conditions.