Retinex Based Condition

Retinex-based methods aim to enhance image quality, particularly in low-light conditions, 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 transformers and diffusion models, to improve noise reduction, detail preservation, and control over illumination effects during image generation or restoration. These advancements are impacting various applications, including medical imaging (e.g., CT scan artifact reduction) and computer vision tasks (e.g., object detection in low-light environments), by enabling more robust and accurate image processing.