Low Light Datasets

Low-light datasets are crucial for developing computer vision algorithms that can robustly process images and videos captured in challenging lighting conditions. Current research focuses on improving image quality through techniques like denoising, color correction, and contrast enhancement, often employing deep learning models such as transformers and diffusion models, sometimes operating directly on RAW image data. These advancements are vital for applications ranging from surveillance and autonomous driving to wildlife monitoring, where reliable image processing in low-light scenarios is essential. The development of new datasets, including those with misaligned or synthetically generated data, is also a key area of focus, enabling more effective training and evaluation of these algorithms.