Low Light Video Enhancement

Low-light video enhancement aims to improve the quality of videos captured in dimly lit conditions, focusing on restoring detail, color accuracy, and temporal consistency. Current research emphasizes the use of deep learning models, including convolutional neural networks and transformers, often incorporating techniques like 4D lookup tables, event camera data fusion, and unpaired learning to address challenges posed by noise, motion blur, and limited illumination. These advancements are significant for various applications, such as improving the performance of computer vision systems in low-light environments and enhancing the visual quality of user-generated content. The development of large, high-quality datasets specifically designed for low-light video enhancement is also a key area of focus, enabling more robust and accurate model training.