IR-Flow: Bridging Discriminative and Generative Image Restoration via Rectified Flow
MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, School of Information Science and Technology, University of Science and Technology of China（脑启发智能感知与认知联合实验室，信息科学与技术学院，中国科学技术大学）
Zihao Fan, Xin Lu, Jie Xiao, Dong Li, Jie Huang, Xueyang Fu
In image restoration, single-step discriminative mappings often lack fine details via expectation learning, whereas generative paradigms suffer from inefficient multi-step sampling and noise-residual coupling. To address this dilemma, we propose IR-Flow, a novel image restoration method based on Rectified Flow that serves as a unified framework bridging the gap between discriminative and generative paradigms. Specifically, we first construct multilevel data distribution flows, which expand the ability of models to learn from and adapt to various levels of degradation. Subsequently, cumulative velocity fields are proposed to learn transport trajectories across varying degradation levels, guiding intermediate states toward the clean target, while a multi-step consistency constraint is presented to enforce trajectory coherence and boost few-step restoration performance. We show that directly establishing a linear transport flow between degraded and clean image domains not only enables fast inference but also improves adaptability to out-of-distribution degradations. Extensive evaluations on deraining, denoising and raindrop removal tasks demonstrate that IR-Flow achieves competitive quantitative results with only a few sampling steps, offering an efficient and flexible framework that maintains an excellent distortion-perception balance. Our code is available at https://github.com/fanzh03/IR-Flow.
Bridging Degradation Discrimination and Generation for Universal Image Restoration
Institute of Medical Technology, Peking University（北京大学医学技术学院）；Biomedical Engineering Department, College of Future Technology, Peking University（北京大学未来技术学院生物医学工程系）；National Biomedical Imaging Center, Peking University（北京大学国家生物医学成像中心）；China Mobile Research Institute（中国移动研究院）
V-Bridge: Bridging Video Generative Priors to Versatile Few-shot Image Restoration
The Hong Kong University of Science and Technology（香港科学与技术大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；The Chinese University of Hong Kong（香港中文大学）
Unsupervised Variational Translator for Bridging Image Restoration and High-Level Vision Tasks
School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区智能系统工程学院）；Guangdong Provincial Key Laboratory of Fire Science and Technology（广东省火灾科学与技术重点实验室）
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Lingnan University（岭南大学）；Hong Kong Metropolitan University（香港都会大学）
Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios. In this paper, we raise an intriguing question - if the combination of image restoration and object detection, can boost the performance of cutting-edge detectors in adverse weather conditions. To answer it, we propose an effective yet unified detection paradigm that bridges these two subtasks together via dynamic enhancement learning to discern objects in adverse weather conditions, called TogetherNet.…
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）
State-of-the-art algorithms for imaging inverse problems (namely deblurring and reconstruction) are typically iterative, involving a denoising operation as one of its steps. Using a state-of-the-art denoising method in this context is not trivial, and is the focus of current work. Recently, we have proposed to use a class-adapted denoiser (patch-based using Gaussian mixture models) in a so-called plug-and-play scheme, wherein a state-of-the-art denoiser is plugged into an iterative algorithm, leading to results that outperform the best general-purpose algorithms, when applied to an image of a …
Shanghai Jiao Tong University（上海交通大学）；Texas A&M University（得克萨斯农工大学）；University of Macau（澳门大学）
Liquify is a common technique for image editing, which can be used for image distortion. Due to the uncertainty in the distortion variation, restoring distorted images caused by liquify filter is a challenging task. To edit images in an efficient way, distorted images are expected to be restored automatically. This paper aims at the distorted image restoration, which is characterized by seeking the appropriate warping and completion of a distorted image. Existing methods focus on the hardware assistance or the geometric principle to solve the specific regular deformation caused by natural phen…
University of Notre Dame（圣母大学）；Honeywell ACST（霍尼韦尔先进概念与科学技术（ACST））；Northwestern University（西北大学）；Zendar Co.（Zendar公司）；Texas A&M University（德克萨斯农工大学）；Peking University（北京大学）；National University of Singapore（新加坡国立大学）；National Tsing Hua University（国立清华大学）；Johns Hopkins University（约翰霍普金斯大学）；Noblis（诺布利斯（Noblis））
What is the current state-of-the-art for image restoration and enhancement applied to degraded images acquired under less than ideal circumstances? Can the application of such algorithms as a pre-processing step to improve image interpretability for manual analysis or automatic visual recognition to classify scene content? While there have been important advances in the area of computational photography to restore or enhance the visual quality of an image, the capabilities of such techniques have not always translated in a useful way to visual recognition tasks. Consequently, there is a pressi…
MeInTime: Bridging Age Gap in Identity-Preserving Face Restoration
Beijing University of Posts and Telecommunications（北京邮电大学）；Department of Computer Science and Technology, Institute for AI, Tsinghua University（清华大学计算机科学与技术系，人工智能研究院）；Minzu University of China（民族大学）；Xiaomi Corporation（小米公司）
UniV2D: Bridging Visual Restoration and Semantic Perception for Underwater Salient Object Detection
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Computer Science, The University of Sydney（悉尼大学计算机学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）
Bridging Information Asymmetry: A Hierarchical Framework for Deterministic Blind Face Restoration
Biomedical Engineering Department, College of Future Technology, Peking University（北京大学未来技术学院生物医学工程系）；Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术研究所）；National Biomedical Imaging Center, Peking University（北京大学国家生物医学成像中心）
Beijing Jiaotong University（北京交通大学）；Hefei University of Technology（合肥工业大学）；Nanyang Technological University（南洋理工大学）
Convolutional Neural Network (CNN)-based image super-resolution (SR) has exhibited impressive success on known degraded low-resolution (LR) images. However, this type of approach is hard to hold its performance in practical scenarios when the degradation process is unknown. Despite existing blind SR methods proposed to solve this problem using blur kernel estimation, the perceptual quality and reconstruction accuracy are still unsatisfactory. In this paper, we analyze the degradation of a high-resolution (HR) image from image intrinsic components according to a degradation-based formulation mo…
Korea Advanced Institute of Science and Technology（韩国科学技术院）
Rectified linear units (ReLU) are well-known to be helpful in obtaining faster convergence and thus higher performance for many deep-learning-based applications. However, networks with ReLU tend to perform poorly when the number of filter parameters is constrained to a small number. To overcome it, in this paper, we propose a novel network utilizing maxout units (MU), and show its effectiveness on super-resolution (SR) applications. In general, the MU has been known to make the filter sizes doubled in generating the feature maps of the same sizes in classification problems. In this paper, we f…
Delta Rectified Flow Sampling for Text-to-Image Editing
Harvard AI and Robotics Lab, Harvard University（哈佛大学人工智能与机器人实验室）；Computer Science Department, Harvard University（哈佛大学计算机科学系）；Multimodal Intelligence and Perception Lab, DGIST（大邱庆北科学技术院多模态智能与感知实验室）；Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University（哈佛大学肯普纳自然与人工智能研究所）
Universidad de la República（乌拉圭共和国大学）；Universitat Pompeu Fabra（庞培法布拉大学）
Motion blur estimation remains an important task for scene analysis and image restoration. In recent years, the removal of motion blur in photographs has seen impressive progress in the hands of deep learning-based methods, trained to map directly from blurry to sharp images. Characterization of the motion blur, on the other hand, has received less attention, and progress in model-based methods for deblurring lags behind that of data-driven end-to-end approaches. In this work we revisit the problem of characterizing dense, non-uniform motion blur in a single image and propose a general non-par…
Bridging Semantic Logic Gaps: A Cognition Inspired Multimodal Boundary Preserving Network for Image Manipulation Localization
School of Computer Science and Technology, Xinjiang University（新疆大学计算机科学与技术学院）；School of Electronic and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机科学与信息工程学院）
Text-guided Image Restoration and Semantic Enhancement for Text-to-Image Person Retrieval
School of Artificial Intelligence（人工智能学院）；Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Key Laboratory of Network System and Network Culture（网络系统与网络文化北京市重点实验室）
Bridging Knowledge Gap Between Image Inpainting and Large-Area Visible Watermark Removal
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；Research Institute of Sun Yat-sen University in Shenzhen（中山大学深圳研究院）；Afirstsoft
