Existing unified methods typically treat multi-degradation image restoration as a multi-task learning problem. Despite performing effectively compared to single degradation restoration methods, they overlook the utilization of commonalities and specificities within multi-task restoration, thereby impeding the model's performance. Inspired by the success of deep generative models and fine-tuning techniques, we proposed a universal image restoration framework based on multiple low-rank adapters (LoRA) from multi-domain transfer learning. Our framework leverages the pre-trained generative model as the shared component for multi-degradation restoration and transfers it to specific degradation image restoration tasks using low-rank adaptation. Additionally, we introduce a LoRA composing strategy based on the degradation similarity, which adaptively combines trained LoRAs and enables our model to be applicable for mixed degradation restoration. Extensive experiments on multiple and mixed degradations demonstrate that the proposed universal image restoration method not only achieves higher fidelity and perceptual image quality but also has better generalization ability than other unified image restoration models. Our code is available at https://github.com/Justones/UIR-LoRA.
CoRE-UIR: Prior-guided common and residual experts for efficient all-in-one remote sensing image restoration
School of Geodesy and Geomatics, Wuhan University（武汉大学测绘学院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）
Southwest Jiaotong University（西南交通大学）；Nanyang Technological University（南洋理工大学）；Tongji University（同济大学）；National University of Singapore（新加坡国立大学）；Tsinghua University（清华大学）
Underwater Image Restoration via Polymorphic Large Kernel CNNs
University of Macau（澳门大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen Polytechnic University（深圳职业技术大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
UniUIR: Considering Underwater Image Restoration as An All-in-One Learner
National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University（国家多媒体软件工程研究中心，计算机学院，武汉大学）；School of Information Engineering, Guangdong University of Technology（信息工程学院，广东工业大学）；Horizon Robotics
LoRA-IR: Taming Low-Rank Experts for Efficient All-in-One Image Restoration
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Indian Institute of Technology Guwahati（印度理工学院古瓦哈提分校）
Background: Underwater images, in general, suffer from low contrast and high color distortions due to the non-uniform attenuation of the light as it propagates through the water. In addition, the degree of attenuation varies with the wavelength resulting in the asymmetric traversing of colors. Despite the prolific works for underwater image restoration (UIR) using deep learning, the above asymmetricity has not been addressed in the respective network engineering. Contributions: As the first novelty, this paper shows that attributing the right receptive field size (context) based on the travers…
Deep LoRA-Unfolding Networks for Image Restoration
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（计算机科学与技术学院，哈尔滨工业大学（深圳））；Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University（图像通信与网络工程院，上海交通大学）；School of Intelligence Science and Technology, Nanjing University（智能科学与技术学院，南京大学）；School of Electronics and Information Engineering, Harbin Institute of Technology (Shenzhen)（电子与信息工程学院，哈尔滨工业大学（深圳））
Universal Image Restoration Pre-training via Masked Degradation Classification
Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China（北京大学北京校区医学技术研究院）；Biomedical Engineering Department, College of Future Technology, Peking University, Beijing, China（北京大学未来技术学院生物医学工程系）；National Biomedical Imaging Center, Peking University, Beijing, China（北京大学国家生物医学成像中心）；JIUTIAN Research, Beijing, China（吉天研究）；College of Electronic Engineering, National University of Defense Technology, Changsha, China（国防科技大学电子工程学院）
FADRA: Frequency-Aware Diffusion with Residual Adaptation for Video Face Restoration
United Arab Emirates University (UAEU)（阿联酋大学）；Ocean University of China (OUC)（中国海洋大学）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）
