Low Light

Low-light image and video enhancement aims to improve the quality of visual data captured in poorly illuminated environments, addressing challenges like noise, low contrast, and color distortion. Current research heavily utilizes deep learning, employing various architectures such as transformers, diffusion models, and convolutional neural networks, often incorporating techniques like Retinex decomposition and vector quantization for improved efficiency and robustness. These advancements have significant implications for numerous applications, including autonomous driving, medical imaging, and surveillance, where reliable visual perception in low-light conditions is crucial.