Rain streaks in the air appear in various blurring degrees and resolutions due to different distances from their positions to the camera. Similar rain patterns are visible in a rain image as well as its multi-scale (or multi-resolution) versions, which makes it possible to exploit such complementary information for rain streak representation. In this work, we explore the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features in a unified framework, termed multi-scale progressive fusion network (MSPFN) for single image rain streak removal. For similar rain streaks at different positions, we employ recurrent calculation to capture the global texture, thus allowing to explore the complementary and redundant information at the spatial dimension to characterize target rain streaks. Besides, we construct multi-scale pyramid structure, and further introduce the attention mechanism to guide the fine fusion of this correlated information from different scales. This multi-scale progressive fusion strategy not only promotes the cooperative representation, but also boosts the end-to-end training. Our proposed method is extensively evaluated on several benchmark datasets and achieves state-of-the-art results. Moreover, we conduct experiments on joint deraining, detection, and segmentation tasks, and inspire a new research direction of vision task-driven image deraining. The source code is available at \url{https://github.com/kuihua/MSPFN}.
Tianjin University（天津大学）；Harbin Institute of Technology（哈尔滨工业大学）；Xi’an Jiaotong University（西安交通大学）
Along with the deraining performance improvement of deep networks, their structures and learning become more and more complicated and diverse, making it difficult to analyze the contribution of various network modules when developing new deraining networks. To handle this issue, this paper provides a better and simpler baseline deraining network by considering network architecture, input and output, and loss functions. Specifically, by repeatedly unfolding a shallow ResNet, progressive ResNet (PRN) is proposed to take advantage of recursive computation. A recurrent layer is further introduced …
ASUS Intelligent Cloud Services（华硕智能云服务）；Georgia Institute of Technology（佐治亚理工学院）；National Taiwan University（台湾大学）
Single image deraining is a crucial problem because rain severely degenerates the visibility of images and affects the performance of computer vision tasks like outdoor surveillance systems and intelligent vehicles. In this paper, we propose the new convolutional neural network (CNN) called the wavelet channel attention module with a fusion network. Wavelet transform and the inverse wavelet transform are substituted for down-sampling and up-sampling so feature maps from the wavelet transform and convolutions contain different frequencies and scales. Furthermore, feature maps are integrated by …
Deep learning algorithms have recently achieved promising deraining performances on both the natural and synthetic rainy datasets. As an essential low-level pre-processing stage, a deraining network should clear the rain streaks and preserve the fine semantic details. However, most existing methods only consider low-level image restoration. That limits their performances at high-level tasks requiring precise semantic information. To address this issue, in this paper, we present a segmentation-aware progressive network (SAPNet) based upon contrastive learning for single image deraining. We star…
Xiamen University（厦门大学）；University of Science and Technology of China（中国科学技术大学）；Columbia University（哥伦比亚大学）
We propose a simple yet effective deep tree-structured fusion model based on feature aggregation for the deraining problem. We argue that by effectively aggregating features, a relatively simple network can still handle tough image deraining problems well. First, to capture the spatial structure of rain we use dilated convolutions as our basic network block. We then design a tree-structured fusion architecture which is deployed within each block (spatial information) and across all blocks (content information). Our method is based on the assumption that adjacent features contain redundant info…
University of Science and Technology of China（中国科学技术大学）；KDDI Research, Inc.（KDDI研究所）
Image deraining is an important image processing task as rain streaks not only severely degrade the visual quality of images but also significantly affect the performance of high-level vision tasks. Traditional methods progressively remove rain streaks via different recurrent neural networks. However, these methods fail to yield plausible rain-free images in an efficient manner. In this paper, we propose a residual squeeze-and-excitation network called RSEN for fast image deraining as well as superior deraining performance compared with state-of-the-art approaches. Specifically, RSEN adopts a …
Shenyang Aerospace University（沈阳航空航天大学）；Shenyang Fire Science and Technology Research Institute of MEM（沈阳消防科学研究所）
Rain streaks bring serious blurring and visual quality degradation, which often vary in size, direction and density. Current CNN-based methods achieve encouraging performance, while are limited to depict rain characteristics and recover image details in the poor visibility environment. To address these issues, we present a Multi-scale Hourglass Hierarchical Fusion Network (MH2F-Net) in end-to-end manner, to exactly captures rain streak features with multi-scale extraction, hierarchical distillation and information aggregation. For better extracting the features, a novel Multi-scale Hourglass E…
Single image rain streaks removal is extremely important since rainy images adversely affect many computer vision systems. Deep learning based methods have found great success in image deraining tasks. In this paper, we propose a novel residual-guide feature fusion network, called ResGuideNet, for single image deraining that progressively predicts highquality reconstruction. Specifically, we propose a cascaded network and adopt residuals generated from shallower blocks to guide deeper blocks. By using this strategy, we can obtain a coarse to fine estimation of negative residual as the blocks g…
We present the Cross-stitched Multi-task Unified Dual Recursive Network (CMUDRN) model targeting the task of unified deraining and desnowing in a multi-task learning setting. This unified model borrows from the basic Dual Recursive Network (DRN) architecture developed by Cai et al. The proposed model makes use of cross-stitch units that enable multi-task learning across two separate DRN models, each tasked for single image deraining and desnowing, respectively. By fixing cross-stitch units at several layers of basic task-specific DRN networks, we perform multi-task learning over the two separa…
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Inception Institute of Artificial Intelligence（启元人工智能研究院）；Meituan Group（美团集团）；Australian National University（澳大利亚国立大学）；Tencent（腾讯）；Beijing Institute of Technology（北京理工大学）
Rain is a common natural phenomenon. Taking images in the rain however often results in degraded quality of images, thus compromises the performance of many computer vision systems. Most existing de-rain algorithms use only one single input image and aim to recover a clean image. Few work has exploited stereo images. Moreover, even for single image based monocular deraining, many current methods fail to complete the task satisfactorily because they mostly rely on per pixel loss functions and ignore semantic information. In this paper, we present a Paired Rain Removal Network (PRRNet), which ex…
RainDancer: RGB-Event Video Deraining with Rain-Oriented Spiking Dynamics
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）；School of Computer Science and Technology, Harbin University of Science and Technology（哈尔滨理工大学计算机科学与技术学院）
University of Electronic Science and Technology of China（电子科技大学）；Tencent AI Lab（腾讯人工智能实验室）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院教育部人工智能重点实验室）
Single image deraining regards an input image as a fusion of a background image, a transmission map, rain streaks, and atmosphere light. While advanced models are proposed for image restoration (i.e., background image generation), they regard rain streaks with the same properties as background rather than transmission medium. As vapors (i.e., rain streaks accumulation or fog-like rain) are conveyed in the transmission map to model the veiling effect, the fusion of rain streaks and vapors do not naturally reflect the rain image formation. In this work, we reformulate rain streaks as transmissio…
East China Normal University（华东师范大学）；The Chinese University of Hong Kong（香港中文大学）
Single image deraining is important for many high-level computer vision tasks since the rain streaks can severely degrade the visibility of images, thereby affecting the recognition and analysis of the image. Recently, many CNN-based methods have been proposed for rain removal. Although these methods can remove part of the rain streaks, it is difficult for them to adapt to real-world scenarios and restore high-quality rain-free images with clear and accurate structures. To solve this problem, we propose a Structure-Preserving Deraining Network (SPDNet) with RCP guidance. SPDNet directly genera…
Nanyang Technological University（南洋理工大学）；Kyushu University（九州大学）；Alibaba Group（阿里巴巴集团）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）
Single-image deraining is rather challenging due to the unknown rain model. Existing methods often make specific assumptions of the rain model, which can hardly cover many diverse circumstances in the real world, making them have to employ complex optimization or progressive refinement. This, however, significantly affects these methods' efficiency and effectiveness for many efficiency-critical applications. To fill this gap, in this paper, we regard the single-image deraining as a general image-enhancing problem and originally propose a model-free deraining method, i.e., EfficientDeRain, whic…
We develop a new physical model for the rain effect and show that the well-known atmosphere scattering model (ASM) for the haze effect naturally emerges as its homogeneous continuous limit. Via depth-aware fusion of multi-layer rain streaks according to the camera imaging mechanism, the new model can better capture the sophisticated non-deterministic degradation patterns commonly seen in real rainy images. We also propose a Densely Scale-Connected Attentive Network (DSCAN) that is suitable for both deraining and dehazing tasks. Our design alleviates the bottleneck issue existent in conventiona…
PRE-Mamba: A 4D State Space Model for Ultra-High-Frequent Event Camera Deraining
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Harbin Institute of Technology（哈尔滨工业大学）；Carnegie Mellon University（卡内基梅隆大学）；Pengcheng Laboratory（鹏城实验室）
PRISM: Progressive Rain removal with Integrated State-space Modeling
Faculty of Data Science, City University of Macau, SAR Macao, China（澳门城市大学数据科学学院）；College of Oceanography and Space Informatics, China University of Petroleum (East China), China（中国石油大学（华东）海洋学院）；Zhuhai 4Dage Network Technology, China（珠海4Dage网络技术）
Learning Dual-Domain Multi-Scale Representations for Single Image Deraining
Nanjing Agricultural University（南京农业大学）；Xiangtan University（湘潭大学）；Nanjing University（南京大学）；Northeast Forestry University（东北林业大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Westlake University（西湖大学）
Cross Paradigm Representation and Alignment Transformer for Image Deraining
Nanjing Agricultural University（南京农业大学）；Soochow University（苏州大学）；Xiangtan University（湘潭大学）；Shanghai University（上海大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Westlake University（西湖大学）
Cross-Scenario Deraining Adaptation with Unpaired Data: Superpixel Structural Priors and Multi-Stage Pseudo-Rain Synthesis
School of Information Engineering, Guangdong University of Technology（广东工业大学信息工程学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）
Deepwise AI Lab（深睿人工智能实验室）；The University of Hong Kong（香港大学）；Sun Yat-sen University（中山大学）
Recently 3D volumetric organ segmentation attracts much research interest in medical image analysis due to its significance in computer aided diagnosis. This paper aims to address the pancreas segmentation task in 3D computed tomography volumes. We propose a novel end-to-end network, Globally Guided Progressive Fusion Network, as an effective and efficient solution to volumetric segmentation, which involves both global features and complicated 3D geometric information. A progressive fusion network is devised to extract 3D information from a moderate number of neighboring slices and predict a p…
A Hybrid Transformer-Mamba Network for Single Image Deraining
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络空间科学与技术学院）；Yunnan Normal University（云南师范大学）
Hong Kong Baptist University（香港浸会大学）；Institute of High Performance Computing, A*STAR（新加坡科技研究局高性能计算研究院）；vivo Mobile Communication Co. Ltd（维沃移动通信有限公司）；City University of Hong Kong（香港城市大学）；Nanyang Technological University（南洋理工大学）
High Dynamic Range (HDR) imaging via multi-exposure fusion is an important task for most modern imaging platforms. In spite of recent developments in both hardware and algorithm innovations, challenges remain over content association ambiguities caused by saturation, motion, and various artifacts introduced during multi-exposure fusion such as ghosting, noise, and blur. In this work, we propose an Attention-guided Progressive Neural Texture Fusion (APNT-Fusion) HDR restoration model which aims to address these issues within one framework. An efficient two-stream structure is proposed which sep…
Semi-Supervised State-Space Model with Dynamic Stacking Filter for Real-World Video Deraining
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区计算机科学与技术学院）；MoE Key Laboratory of Information Technology（教育部信息科技重点实验室）；Guangdong Provincial Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）；Key Laboratory of Educational Information for Nationalities, Yunnan Normal University（民族教育信息重点实验室，云南师范大学）；School of Mechanical Engineering and Automation, Fuzhou University（福州大学机械工程与自动化学院）
DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University（复旦大学脑智能科学与技术研究院）；School of Computer Science, Fudan University（复旦大学计算机科学技术学院）
Britton Chance Center for Biomedical Photonics（布里顿·钱斯生物医学光子学研究中心）；Wuhan National Laboratory for Optoelectronics（武汉光电国家研究中心）；MoE Key Laboratory for Biomedical Photonics（教育部生物医学光子学重点实验室）；Collaborative Innovation Center for Biomedical Engineering（生物医学工程协同创新中心）；School of Engineering Sciences, Huazhong University of Science and Technology（华中科技大学工程科学学院）
Registration of brain MRI images requires to solve a deformation field, which is extremely difficult in aligning intricate brain tissues, e.g., subcortical nuclei, etc. Existing efforts resort to decomposing the target deformation field into intermediate sub-fields with either tiny motions, i.e., progressive registration stage by stage, or lower resolutions, i.e., coarse-to-fine estimation of the full-size deformation field. In this paper, we argue that those efforts are not mutually exclusive, and propose a unified framework for robust brain MRI registration in both progressive and coarse-to-…
South China University of Technology（华南理工大学）；University of Manchester（曼彻斯特大学）
Limited by the cost and technology, the resolution of depth map collected by depth camera is often lower than that of its associated RGB camera. Although there have been many researches on RGB image super-resolution (SR), a major problem with depth map super-resolution is that there will be obvious jagged edges and excessive loss of details. To tackle these difficulties, in this work, we propose a multi-scale progressive fusion network for depth map SR, which possess an asymptotic structure to integrate hierarchical features in different domains. Given a low-resolution (LR) depth map and its a…
Mask-adaptive Gated Convolution and Bi-directional Progressive Fusion Network for Depth Completion
College of Information Science and Engineering, Northeastern University（东北大学信息科学与工程学院）；Foshan Graduate School of Innovation, Northeastern University（东北大学佛山创新研究生院）；National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University（东北大学工业智能与系统优化国家前沿科学中心）
Imperial College London（伦敦帝国学院）；University of Liverpool（利物浦大学）
Salient object detection(SOD) aims at locating the most significant object within a given image. In recent years, great progress has been made in applying SOD on many vision tasks. The depth map could provide additional spatial prior and boundary cues to boost the performance. Combining the depth information with image data obtained from standard visual cameras has been widely used in recent SOD works, however, introducing depth information in a suboptimal fusion strategy may have negative influence in the performance of SOD. In this paper, we discuss about the advantages of the so-called prog…
Inception Institute of AI（Inception人工智能研究所）；Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Monash University（莫纳什大学）；University of California, Merced（加州大学默塞德分校）；Yonsei University（延世大学）；Google Research（谷歌研究院）
Image restoration tasks demand a complex balance between spatial details and high-level contextualized information while recovering images. In this paper, we propose a novel synergistic design that can optimally balance these competing goals. Our main proposal is a multi-stage architecture, that progressively learns restoration functions for the degraded inputs, thereby breaking down the overall recovery process into more manageable steps. Specifically, our model first learns the contextualized features using encoder-decoder architectures and later combines them with a high-resolution branch t…
Jiangxi Normal University（江西师范大学）；East China Normal University（华东师范大学）
Single image dehazing is a challenging ill-posed restoration problem. Various prior-based and learning-based methods have been proposed. Most of them follow a classic atmospheric scattering model which is an elegant simplified physical model based on the assumption of single-scattering and homogeneous atmospheric medium. The formulation of haze in realistic environment is more complicated. In this paper, we propose to take its essential mechanism as "black box", and focus on learning an input-adaptive trainable end-to-end dehazing model. An U-Net like encoder-decoder deep network via progressi…
EPOFusion: Exposure aware Progressive Optimization Method for Infrared and Visible Image Fusion
College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院）；College of Information Science and Technology, Zhejiang Shuren University（浙江树人大学信息科技学院）；Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电机电子工程系）
PCaM: A Progressive Focus Attention-Based Information Fusion Method for Improving Vision Transformer Domain Adaptation
Westlake Institute for Advanced Studies（西湖研究学院）；Centre for Artificial Intelligence（人工智能中心）；State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS)（多模态人工智能系统国家重点实验室）；Institute of Automation（自动化研究所）；Chinese Academy of Sciences (CASIA)（中国科学院）；School of Artificial Intelligence（人工智能学院）；University of Chinese Academy of Sciences (UCAS)（中国科学院大学）；Alibaba Group（阿里巴巴集团）；School of Computer Science（计算机学院）；Engineering, the Faculty of Innovation Engineering, Macau University of Science（工程学院，创新工程 faculty，澳门科学大学）；Microsoft（微软公司）
University of Trento（特伦托大学）；University of Oxford（牛津大学）；Fondazione Bruno Kessler（布鲁诺·凯塞勒基金会）；Huawei Technologies Ireland（华为技术爱尔兰）
Recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance. However, they require costly ground truth annotations during training. To cope with this issue, in this paper we present a novel unsupervised deep learning approach for predicting depth maps. We introduce a new network architecture, named Progressive Fusion Network (PFN), that is specifically designed for binocular stereo depth estimation. This network is based on a multi-scale refinement strategy that combines the information provided by both stereo views. In addition, we pro…
Nanjing University of Science and Technology（南京理工大学）
To solve the issue of video dehazing, there are two main tasks to attain: how to align adjacent frames to the reference frame; how to restore the reference frame. Some papers adopt explicit approaches (e.g., the Markov random field, optical flow, deformable convolution, 3D convolution) to align neighboring frames with the reference frame in feature space or image space, they then use various restoration methods to achieve the final dehazing results. In this paper, we propose a progressive alignment and restoration method for video dehazing. The alignment process aligns consecutive neighboring …
Tohoku University（东北大学）；The Hong Kong Polytechnic University（香港理工大学）；RIKEN Center for AIP（理化学研究所革新智能统合研究中心）
This paper considers the problem of generating an HDR image of a scene from its LDR images. Recent studies employ deep learning and solve the problem in an end-to-end fashion, leading to significant performance improvements. However, it is still hard to generate a good quality image from LDR images of a dynamic scene captured by a hand-held camera, e.g., occlusion due to the large motion of foreground objects, causing ghosting artifacts. The key to success relies on how well we can fuse the input images in their feature space, where we wish to remove the factors leading to low-quality image ge…
