Normal Light Image

Normal-light image generation aims to enhance images suffering from underexposure, often captured in low-light conditions. Current research focuses on developing unsupervised methods, avoiding the need for paired low-light/normal-light datasets, and employing various architectures including diffusion models, variational autoencoders, and transformer-based networks to achieve this. These advancements leverage techniques like Retinex theory and multi-resolution feature extraction to improve image quality, addressing issues such as noise amplification and artifact generation. The resulting improvements have significant implications for various applications, including improving the quality of images captured in challenging lighting conditions and enhancing the performance of computer vision systems.