Dark Channel Prior Algorithm for Image Dehazing

The MATLAB-implemented Dark Channel Prior algorithm effectively performs image dehazing through sophisticated image processing techniques. This algorithm operates by analyzing the dark channel of an input image to estimate atmospheric light and transmission maps, which represent the haze density distribution. The implementation typically involves calculating the minimum color channel values within local patches using morphological operations or sliding window approaches. Key functions may include im2double for image normalization, min filter operations for dark channel computation, and guided image filtering for transmission map refinement. Following the atmospheric scattering model, the algorithm reconstructs the haze-free image by inversely applying the estimated parameters, effectively removing haze while preserving image details. Widely applied in computer vision and image processing domains, this method significantly enhances image quality by improving visibility, restoring natural colors, and revealing obscured textures. The dehazing process produces more authentic, visually appealing results with boosted contrast and clarity, making it valuable for applications requiring enhanced image perceptibility and aesthetic quality.