Retinex Theory

Retinex theory, which models an image as the product of illumination and reflectance components, aims to separate these factors for improved image quality and analysis. Current research focuses on integrating Retinex principles with deep learning architectures, such as diffusion models and convolutional neural networks, often employing techniques like algorithm unrolling and attention mechanisms to enhance low-light image enhancement, denoising, and other image processing tasks. These advancements are improving the performance of various computer vision applications, particularly in challenging lighting conditions, and offer more interpretable and efficient solutions compared to purely data-driven approaches. The resulting models show promise for applications ranging from autonomous driving to medical imaging.