RAW Image Denoising

RAW image denoising aims to remove noise from images captured by digital sensors before further processing, improving image quality for various applications. Current research emphasizes developing robust and generalizable denoising methods, focusing on architectures like transformers and generative adversarial networks, often incorporating techniques like contrastive learning and dual-domain processing to handle diverse noise patterns and camera settings. These advancements are crucial for improving the quality of images in computational photography, microscopy, and other fields where high-quality raw data is essential for accurate analysis and interpretation. The development of high-quality benchmark datasets is also a significant area of focus, enabling more rigorous evaluation of denoising algorithms.