MATLAB Implementation of Dark Channel Prior Dehazing Algorithm

In this article, we will explore how to implement the Dark Channel Prior dehazing algorithm using MATLAB, a powerful method with broad applications in computer vision and pattern recognition. The Dark Channel Prior algorithm is a weakly supervised approach that extracts depth information and perspective cues from images through straightforward image processing operations. Originally introduced in a seminal CVPR paper, we adapt and discuss the implementation based on this research. Our discussion will cover the fundamental algorithm principles along with practical MATLAB implementation techniques. We will explain key implementation aspects including the dark channel computation using min-filtering operations, atmospheric light estimation methods, and transmission map refinement using soft matting or guided filtering. Additionally, we'll share practical tips to enhance computational efficiency when processing image data. The implementation demonstrates how to handle RGB channel operations and pixel-wise calculations efficiently in MATLAB. Finally, we will present real-world application examples to showcase the algorithm's practical utility and potential in image enhancement and vision systems.