Image Dehazing

In this paper, we present a code implementation and simulation of Kaiming He's Dark Channel Prior algorithm, which represents an advanced technique for image dehazing. To facilitate deeper understanding of the algorithm's underlying principles, we conducted comprehensive research and analysis, subsequently developing a Python/Matlab-based implementation that carefully follows the mathematical framework. Our implementation incorporates critical algorithmic components including dark channel computation, atmospheric light estimation, and transmission map refinement. We employed sophisticated mathematical models and computational methods, extensively testing the algorithm on diverse image datasets to validate its effectiveness and accuracy. The implementation workflow includes key functions for calculating the dark channel prior (using minimum filtering operations), estimating atmospheric light intensity from the brightest pixels in the dark channel, and refining the transmission map through soft matting techniques. This work provides a more efficient and precise approach to image dehazing while offering new perspectives and directions for related research fields.