Enhanced MDRCR Algorithm for Image Defogging

Researchers have developed SSR (Single Scale Retinex) and MSR (Multi-Scale Retinex) methods to address the challenge of partially foggy images, but these approaches have shown limited effectiveness in practical applications. To overcome these limitations, an enhanced MDRCR (Modified Dark Channel Prior based Retinex) algorithm has been proposed, which demonstrates substantially improved performance in enhancing the quality of partially obscured images. The MDRCR method employs a multi-stage processing pipeline that includes several key computational steps: 1. Preprocessing: Image normalization and noise reduction operations 2. Dark Channel Prior: Calculating the minimum channel values using a sliding window approach (typically implemented with 15×15 pixel patches) 3. Global Atmospheric Light Estimation: Determining atmospheric light components through statistical analysis of the brightest pixels in the dark channel 4. Image Recovery: Applying the atmospheric scattering model to reconstruct the fog-free image using transmission maps This comprehensive approach combines the strengths of physical haze modeling with advanced image processing techniques. The algorithm implementation typically involves MATLAB or Python code structures that sequentially process each stage, with special attention to parameter optimization for different fog density levels. Through this systematic combination of computational steps, the MDRCR method achieves significantly better performance when processing partially foggy images compared to traditional Retinex-based approaches.