Single Image Dehazing MATLAB Code Implementation and Algorithms

This documentation provides detailed information about implementing single image dehazing using MATLAB code. Single image dehazing refers to the process of removing haze from a single foggy image to reveal a clearer version. In MATLAB, we can implement various algorithms for single image dehazing, with prominent approaches including the dark channel prior method and physics-based model techniques. The dark channel prior algorithm operates on the principle that in most non-sky patches of outdoor haze-free images, at least one color channel has very low intensity at some pixels. This prior allows us to estimate the transmission map and atmospheric light effectively. In MATLAB implementation, this involves calculating the dark channel using minimum filters across color channels, followed by soft matting for transmission refinement. Physics-based methods employ the atmospheric scattering model: I(x) = J(x)t(x) + A(1-t(x)), where I is the observed haze image, J is the scene radiance (clear image), t is the transmission map, and A is the atmospheric light. The implementation requires accurate estimation of both transmission map and global atmospheric light parameters. We will demonstrate how to implement these algorithms in MATLAB, including key functions such as: - Dark channel calculation using im2double and min filter operations - Atmospheric light estimation through brightest pixel identification - Transmission map refinement using guided filter or soft matting - Final image reconstruction using the inverse atmospheric model The documentation includes sample code demonstrating application on various image types, showing how to handle different haze densities and lighting conditions. We'll also discuss practical applications of single image dehazing in surveillance systems, autonomous vehicles, and aerial photography, along with optimization techniques for achieving better results across different scenarios.