Retinex Model

The Retinex model aims to enhance image quality by separating an image into illumination and reflectance components, mimicking human visual perception. Current research focuses on integrating Retinex theory with deep learning architectures, such as convolutional neural networks (CNNs) and transformers, and state space models (SSMs), to improve low-light image enhancement, often addressing challenges like noise reduction and artifact suppression. These advancements are significantly impacting image processing applications, particularly in areas like low-light photography and video enhancement, by producing more realistic and detailed results.