Dark Channel Prior Dehazing Algorithm and Its MATLAB Implementation

In the field of computer vision, the 2009 CVPR Best Paper introduced a highly effective image dehazing method known as the "Dark Channel Prior" algorithm. This approach demonstrates superior performance in removing haze from images and has gained widespread practical application. The algorithm's core principle involves estimating atmospheric light and transmission maps based on the statistical observation that haze-free image patches contain at least one color channel with very low intensity values. Key MATLAB implementation steps typically involve: 1) Computing the dark channel using minimum filters across color channels and local patches, 2) Estimating atmospheric light from the brightest pixels in the dark channel, 3) Calculating the transmission map with guided image filtering to preserve edges, and 4) Recovering the haze-free image using the atmospheric scattering model. The method has attracted significant research attention from scholars worldwide, leading to numerous improvements and variations. Furthermore, translated MATLAB implementations of the original paper have been widely shared, enabling more researchers and practitioners to study and apply this technique effectively. The code typically utilizes MATLAB's image processing toolbox functions for efficient patch-based operations and includes parameters for adjusting patch sizes and transmission refinement thresholds.