Image Processing Techniques for Histogram-Based Dehazing

In the field of computer vision, image dehazing technology remains an active research area. One prominent approach involves histogram-optimized image dehazing techniques. This methodology utilizes image histograms to estimate depth information and atmospheric light conditions, which are subsequently applied to remove haze from images. The implementation typically involves calculating luminance histograms to determine atmospheric veil parameters, followed by applying inverse atmospheric scattering models. While this technique has demonstrated effective results in haze removal, it presents certain limitations, particularly when handling images with color distortion and halo artifacts. To address these challenges, researchers are continuously developing enhanced algorithms that incorporate weighted histogram equalization and multi-scale depth refinement. Current implementations often employ MATLAB or Python with key functions including histogram analysis, atmospheric light estimation via brightest pixel selection, and guided filtering for depth map refinement. These advancements aim to overcome existing limitations while improving both the effectiveness and quality of image dehazing results.