Dark Channel Prior-Based Image Dehazing Algorithm

In this article, we will discuss a dark channel prior-based image dehazing algorithm implemented according to the CVPR best paper award-winning approach. The implementation involves estimating atmospheric light using the brightest pixels in the dark channel and calculating transmission maps through minimum filtering operations. We will detail the algorithm's implementation process, including the core mathematical formulation: I(x) = J(x)t(x) + A(1-t(x)), where I is the observed hazy image, J is the scene radiance, t is the transmission map, and A is the atmospheric light. The algorithm improvement section will focus on color distortion correction through color balance techniques and a fast guided filtering approach that approximates soft matting effects while reducing computational complexity from O(N^2) to O(N). Additionally, we will explore the algorithm's practical application potential and limitations in real-world scenarios, including performance considerations for handling high-resolution images and edge-preserving characteristics.