Restoration Task

Image restoration aims to recover high-quality images from degraded versions, addressing issues like noise, blur, weather effects, and age-related damage. Recent research focuses on developing generalist models capable of handling multiple degradation types simultaneously, often employing novel architectures such as multi-branch networks, transformer-based approaches, and multimodal large language models (MLLMs) to guide the restoration process. These advancements improve efficiency and performance compared to task-specific methods, with significant implications for applications like autonomous driving, document processing, and historical photo preservation. The integration of techniques like optimal transport and feature-guidance further enhances the accuracy and robustness of restoration algorithms.