Non Blind Denoising
Non-blind image denoising focuses on removing noise from images where the noise characteristics are known or assumed, a crucial task in image processing and computer vision. Current research emphasizes deep convolutional neural networks (CNNs), often employing U-Net architectures or variations thereof, with some incorporating noise estimation networks for improved adaptability to diverse noise types. These advancements aim to improve denoising accuracy and efficiency, impacting applications ranging from medical imaging to video conferencing, where robust noise reduction is essential for high-quality results. Furthermore, research is actively addressing the vulnerability of these deep learning models to adversarial attacks, seeking more robust and reliable solutions.