Real World Image Denoising

Real-world image denoising aims to recover clean images from noisy observations, a crucial task hampered by the complex and often unknown nature of real-world noise. Current research focuses on developing robust deep learning models, including transformers, variational autoencoders, and diffusion models, often employing self-supervised or semi-supervised learning techniques to overcome data limitations and address spatially correlated noise. These advancements are improving the quality of denoised images, particularly in challenging low-light conditions, with applications ranging from computational photography to medical imaging. The development of efficient and effective denoising methods is vital for improving the quality and reliability of image-based analyses across numerous fields.