Image Restoration Task

Image restoration aims to recover high-quality images from degraded versions, addressing issues like noise, blur, and missing data. Current research heavily utilizes deep learning, focusing on diffusion models, transformers, and convolutional neural networks, often incorporating techniques like attention mechanisms and state-space models to improve efficiency and accuracy. These advancements are crucial for various applications, including medical imaging, photography, and computer vision, where high-quality images are essential for accurate analysis and interpretation. The field is also exploring improved training paradigms and more generalized models capable of handling multiple degradation types simultaneously.