Degraded Image

Degraded image restoration aims to recover high-quality images from those marred by various imperfections like blur, noise, rain, or low light. Current research heavily emphasizes developing universal models, often based on diffusion models, transformers, or CNNs, capable of handling multiple degradation types simultaneously, rather than relying on task-specific architectures. This field is crucial for improving the quality of images across numerous applications, from medical imaging and autonomous driving to cultural heritage preservation and enhancing the performance of downstream computer vision tasks. The development of robust and efficient restoration methods is driving significant advancements in both theoretical understanding and practical capabilities.