Blind Image

Blind image deblurring and super-resolution aim to recover sharp images from blurred ones without knowing the blurring process, a challenging inverse problem. Current research focuses on developing efficient algorithms, often employing deep neural networks with architectures like U-Net variations and incorporating techniques such as alternating optimization, sparsity priors, and iterative methods, sometimes integrated with classical deconvolution approaches. These advancements improve image quality and processing speed, impacting various applications including microscopy, photography, and medical imaging. Furthermore, federated learning is emerging as a promising approach to address privacy concerns while leveraging real-world degradation data for improved model training.