Energy-oriented Diffusion Bridge for Image Restoration with Foundational Diffusion Models
Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；Department of Computer Science, City University of Hong Kong (Dongguan)（香港城市大学（东莞）计算机科学系）
Diffusion bridge models have shown great promise in image restoration by explicitly connecting clean and degraded image distributions. However, they often rely on complex and high-cost trajectories, which limit both sampling efficiency and final restoration quality. To address this, we propose an Energy-oriented diffusion Bridge (E-Bridge) framework to approximate a set of low-cost manifold geodesic trajectories to boost the performance of the proposed method. We achieve this by designing a novel bridge process that evolves over a shorter time horizon and makes the reverse process start from an entropy-regularized point that mixes the degraded image and Gaussian noise, which theoretically reduces the required trajectory energy. To solve this process efficiently, we draw inspiration from consistency models to learn a single-step mapping function, optimized via a continuous-time consistency objective tailored for our trajectory, so as to analytically map any state on the trajectory to the target image. Notably, the trajectory length in our framework becomes a tunable task-adaptive knob, allowing the model to adaptively balance information preservation against generative power for tasks of varying degradation, such as denoising versus super-resolution. Extensive experiments demonstrate that our E-Bridge achieves state-of-the-art performance across various image restoration tasks while enabling high-quality recovery with a single or fewer sampling steps. Our project page is https://jinnh.github.io/E-Bridge/.
RCL-Mamba: A Dual-domain State Space Model for Measurement-oriented Image Restoration in Rotational Sparse-View Scanning Computed Laminography
Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University（重庆大学光电技术及系统教育部重点实验室）；Engineering Research Center of Industrial CT Nondestructive Testing, Ministry of Education, Chongqing University（重庆大学工业CT无损检测教育部工程研究中心）
Distribution-aware Dataset Distillation for Efficient Image Restoration
Sun Yat‑sen University（中山大学）；Fuzhou University（福州大学）；University of Science and Technology of China（中国科学技术大学）；Nanjing University of Science and Technology（南京理工大学）
How far have we gone in Generative Image Restoration? A study on its capability, limitations and evaluation practices
Fudan University（复旦大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；University of the Chinese Academy of Sciences（中国科学院大学）；Multimedia Laboratory, The Chinese University of Hong Kong（香港中文大学多媒体实验室）；Shenzhen University of Advanced Technology（深圳先进技术大学）
Learning Efficient and Effective Trajectories for Differential Equation-based Image Restoration
Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；School of Computing and Information Sciences, Saint Francis University（圣芳济大学计算与信息科学学院）；School of Engineering, Huaqiao University（华侨大学工程学院）；School of Information Science and Engineering, Huaqiao University（华侨大学信息科学与工程学院）
IRPO: Boosting Image Restoration via Post-training GRPO
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tsinghua University（清华大学）；Technical University of Munich（慕尼黑技术大学）；Zhejiang University（浙江大学）；Shanghai Jiao Tong University（上海交通大学）；Fudan University（复旦大学）；National University of Singapore（新加坡国立大学）
All-in-one Weather-degraded Image Restoration via Adaptive Degradation-aware Self-prompting Model
School of Computing, Australian National University（澳大利亚国立大学计算学院）；School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）计算机科学与技术学院）
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）
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
Deep Convolutional Neural Network (CNN) features have been demonstrated to be effective perceptual quality features. The perceptual loss, based on feature maps of pre-trained CNN's has proven to be remarkably effective for CNN based perceptual image restoration problems. In this work, taking inspiration from the the Human Visual System (HVS) and visual perception, we propose a spatial attention mechanism based on the dependency human contrast sensitivity on spatial frequency. We identify regions in input images, based on the underlying spatial frequency, which are not generally well reconstruc…
Korea Advanced Institute of Science and Technology（韩国科学技术院）
Blind super-resolution (SR) methods aim to generate a high quality high resolution image from a low resolution image containing unknown degradations. However, natural images contain various types and amounts of blur: some may be due to the inherent degradation characteristics of the camera, but some may even be intentional, for aesthetic purposes (e.g. Bokeh effect). In the case of the latter, it becomes highly difficult for SR methods to disentangle the blur to remove, and that to leave as is. In this paper, we propose a novel blind SR framework based on kernel-oriented adaptive local adjustm…
Lightweight Medical Image Restoration via Integrating Reliable Lesion-Semantic Driven Prior
University of Electronic Science and Technology of China（电子科技大学）；City University of Hong Kong（香港城市大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Shenzhen Institute For Advanced Study, University of Electronic Science and Technology of China（电子科技大学深圳研究院）
Shenzhen University（深圳大学）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）；Alibaba Group（阿里巴巴集团）
With the spread of tampered images, locating the tampered regions in digital images has drawn increasing attention. The existing image tampering localization methods, however, suffer from severe performance degradation when the tampered images are subjected to some post-processing, as the tampering traces would be distorted by the post-processing operations. The poor robustness against post-processing has become a bottleneck for the practical applications of image tampering localization techniques. In order to address this issue, this paper proposes a novel restoration-assisted framework for i…
AuthFace: Towards Authentic Blind Face Restoration with Face-oriented Generative Diffusion Prior
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；vivo Mobile Communication Co., Ltd（vivo移动通信有限公司）；Nanyang Technological University（南洋理工大学）
TTTIR: Unlocking Instance-Specific State Evolution via Test-Time Training for Image Restoration
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Tsinghua University（清华大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Shenzhen University（深圳大学）；Peng Cheng Laboratory（鹏城实验室）
Bridging the Gap Between Image Restoration and Navigational Safety in Hazy Conditions: A New Visibility Estimation Metric for Maritime Surveillance
Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572000, China（武汉理工大学三亚科学教育创新园，三亚572000，中国）；School of Navigation, Wuhan University of Technology, Wuhan 430063, China（武汉理工大学航海学院，武汉430063，中国）；State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China（武汉理工大学海洋技术与安全国家重点实验室，武汉430063，中国）；Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Göteborg 41296, Sweden（楚科奇大学机械与航海科学系，哥德堡41296，瑞典）
Restoration-Oriented Video Frame Interpolation with Region-Distinguishable Priors from SAM
Zhejiang Lab（浙江实验室）；Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程系）；University of California at Merced（加州大学默塞德分校）
Holistic Dynamic Frequency Transformer for Image Fusion and Exposure Correction
School of Biomedical Engineering and Instrument Science, Zhejiang University（浙江大学生物医学工程与仪器科学学院）；School of Software Technology, Dalian University of Technology（大连理工大学软件学院）；School of Mechanical Engineering, Dalian University of Technology（大连理工大学机械工程学院）
Nanyang Technological University（南洋理工大学）；Tencent PCG（腾讯平台与内容事业群）；Shanghai AI Laboratory（上海人工智能实验室）
We show that pre-trained Generative Adversarial Networks (GANs) such as StyleGAN and BigGAN can be used as a latent bank to improve the performance of image super-resolution. While most existing perceptual-oriented approaches attempt to generate realistic outputs through learning with adversarial loss, our method, Generative LatEnt bANk (GLEAN), goes beyond existing practices by directly leveraging rich and diverse priors encapsulated in a pre-trained GAN. But unlike prevalent GAN inversion methods that require expensive image-specific optimization at runtime, our approach only needs a single …
Automotive Software Innovation Center（汽车软件创新中心）；Institute of Intelligent Software（智能软件研究所）；Saarland University（萨尔大学）；Hangzhou Institute for Advanced Study（杭州高等研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Science and Technology of China（中国科学技术大学）
A Specific Task-oriented Semantic Image Communication System for substation patrol inspection
Beijing University of Posts and Telecommunications（北京邮电大学）；Peng Cheng Laboratory（鹏城实验室）；Beijing Smart-chip Microelectronics Technology Co.,Ltd.（北京智芯微电子科技有限公司）
Position: Evaluation of Visual Processing Should Be Human-Centered, Not Metric-Centered
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；The University of Hong Kong（香港大学）；INSAIT, Sofia University ``St. Kliment Ohridski''（INSAIT，索菲亚大学『圣克莱孟·奥赫里迪斯』）；Shenzhen Loop Area Institute（深圳河套学院）；The Chinese University of Hong Kong（香港中文大学）；University of the Chinese Academy of Sciences（中国科学院大学）；Fudan University（复旦大学）；Shenzhen University of Advanced Technology（深圳先进技术大学）；Shanghai AI Laboratory（上海人工智能实验室）
The University of Hong Kong（香港大学）；SenseTime Research（商汤科技研究院）
Blind face restoration is to recover a high-quality face image from unknown degradations. As face image contains abundant contextual information, we propose a method, RestoreFormer, which explores fully-spatial attentions to model contextual information and surpasses existing works that use local operators. RestoreFormer has several benefits compared to prior arts. First, unlike the conventional multi-head self-attention in previous Vision Transformers (ViTs), RestoreFormer incorporates a multi-head cross-attention layer to learn fully-spatial interactions between corrupted queries and high-qu…
Joint Degradation-Aware Arbitrary-Scale Super-Resolution for Variable-Rate Extreme Image Compression
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University（上海交通大学信息科学与电子工程学院）；Department of Electronic Engineer and Information Science, University of Science and Technology of China（中国科学技术大学电子工程与信息科学系）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能教育部重点实验室）
StomaD2: An All-in-One System for Intelligent Stomatal Phenotype Analysis via Diffusion-Based Restoration Detection Network
Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms（河南人工智能理论与算法工程研究中心）；School of Mathematics and Statistics（数学与统计学学院）；International Joint Research Laboratory for Global Change Ecology（全球变化生态学联合研究实验室）；School of Life Sciences（生命科学学院）；State Key Laboratory of Cotton Biology（棉花生物学国家重点实验室）
