Image Restoration

Image restoration aims to recover high-quality images from degraded versions, addressing issues like noise, blur, and missing data. Current research emphasizes developing universal models capable of handling multiple degradation types simultaneously, often employing diffusion models, transformers, and plug-and-play architectures alongside techniques like low-rank adaptation and multi-expert selection to improve efficiency and performance. These advancements are significant for various applications, including medical imaging, remote sensing, and enhancing the quality of digital photos and videos, driving improvements in both image fidelity and perceptual quality.