Image Dehazing Based on RETINEX Theory

Image dehazing based on RETINEX theory represents an image processing methodology that enhances image clarity and visibility through comprehensive processing of 2D images. This approach proves effective for majority of image types and assists in eliminating or mitigating quality degradation caused by atmospheric factors like haze, fog, and smoke. By implementing RETINEX theory algorithms - typically involving multi-scale retinal processing and color constancy mechanisms - we can significantly improve image contrast, color saturation, and detail preservation. The core implementation often utilizes Gaussian pyramid decomposition for multi-scale analysis and center-surround retinal modeling to estimate atmospheric light and transmission maps. These algorithmic components work collectively to restore image authenticity and sharpness. Consequently, RETINEX-based image dehazing stands as an effective computational imaging technique that substantially enhances visual perception quality, with practical applications ranging from computer vision preprocessing to photographic enhancement pipelines.