Blind Denoising

Blind image denoising aims to remove noise from images without prior knowledge of the noise characteristics, a crucial preprocessing step for many computer vision tasks. Recent research focuses on deep learning approaches, employing architectures like convolutional neural networks (CNNs), often incorporating attention mechanisms, and diffusion models to achieve robust denoising across various noise types (Gaussian, Poisson, real-world noise). These advancements improve the accuracy and efficiency of denoising, particularly for complex, spatially varying noise, impacting applications ranging from medical imaging to astronomical data analysis. Furthermore, research explores self-supervised learning techniques and adversarial training to enhance model robustness and generalization capabilities.