Low Light Image

Low-light image enhancement (LLIE) aims to improve the quality of images taken in dimly lit conditions, addressing issues like low brightness, noise, and color distortion. Current research focuses on developing efficient and effective deep learning models, including those based on transformers, diffusion models, and convolutional neural networks, often incorporating techniques like Retinex theory and frequency-domain processing to achieve better results. These advancements are significant for various applications, such as improving the performance of computer vision tasks (object detection, classification) in low-light environments and enhancing the capabilities of mobile phone cameras and other imaging devices. A key challenge remains aligning enhancements for optimal human perception with the needs of machine vision algorithms.