Low Light Enhancement

Low-light enhancement aims to improve the quality of images and videos captured in dimly lit conditions, addressing issues like low brightness, noise, and color distortion. Current research focuses on developing sophisticated deep learning models, including transformers and diffusion models, often incorporating Retinex theory or physics-based priors to guide the enhancement process. These advancements are driven by the need for improved visual perception in various applications, such as autonomous driving, surveillance, and remote sensing, where low-light conditions are common. The field is also exploring joint tasks, such as simultaneous deblurring and enhancement, and the use of multimodal data (e.g., event cameras) to improve results.