Image Restoration Network

Image restoration networks aim to recover high-quality images from degraded versions, addressing issues like noise, blur, compression artifacts, and adverse weather conditions. Current research focuses on developing efficient and versatile "all-in-one" models capable of handling multiple degradation types simultaneously, often employing transformer-based architectures, hypernetworks for dynamic parameter adaptation, or prompt-learning techniques to guide the restoration process. These advancements are significant for improving image quality across various applications, from enhancing low-light photos to restoring images from compressed formats, and are driving efficiency improvements through techniques like structured sparsity and binarization.