Existing learning-based atmospheric particle-removal approaches such as those used for rainy and hazy images are designed with strong assumptions regarding spatial frequency, trajectory, and translucency. However, the removal of snow particles is more complicated because it possess the additional attributes of particle size and shape, and these attributes may vary within a single image. Currently, hand-crafted features are still the mainstream for snow removal, making significant generalization difficult to achieve. In response, we have designed a multistage network codenamed DesnowNet to in turn deal with the removal of translucent and opaque snow particles. We also differentiate snow into attributes of translucency and chromatic aberration for accurate estimation. Moreover, our approach individually estimates residual complements of the snow-free images to recover details obscured by opaque snow. Additionally, a multi-scale design is utilized throughout the entire network to model the diversity of snow. As demonstrated in experimental results, our approach outperforms state-of-the-art learning-based atmospheric phenomena removal methods and one semantic segmentation baseline on the proposed Snow100K dataset in both qualitative and quantitative comparisons. The results indicate our network would benefit applications involving computer vision and graphics.
Xidian University（西安电子科技大学）；Beijing Electronic Science and Technology Institute（北京电子科技学院）；The Chinese University of Hong Kong（香港中文大学）；Guangxi Normal University（广西师范大学）
Image restoration under severe weather is a challenging task. Most of the past works focused on removing rain and haze phenomena in images. However, snow is also an extremely common atmospheric phenomenon that will seriously affect the performance of high-level computer vision tasks, such as object detection and semantic segmentation. Recently, some methods have been proposed for snow removing, and most methods deal with snow images directly as the optimization object. However, the distribution of snow location and shape is complex. Therefore, failure to detect snowflakes / snow streak effecti…
Varying weather conditions, including rainfall and snowfall, are generally regarded as a challenge for computer vision algorithms. One proposed solution to the challenges induced by rain and snowfall is to artificially remove the rain from images or video using rain removal algorithms. It is the promise of these algorithms that the rain-removed image frames will improve the performance of subsequent segmentation and tracking algorithms. However, rain removal algorithms are typically evaluated on their ability to remove synthetic rain on a small subset of images. Currently, their behavior is un…
Seoul National University（首尔国立大学）；Agency for Defense Development（国防发展局）；Inha University（仁荷大学）
LiDAR is widely used to capture accurate 3D outdoor scene structures. However, LiDAR produces many undesirable noise points in snowy weather, which hamper analyzing meaningful 3D scene structures. Semantic segmentation with snow labels would be a straightforward solution for removing them, but it requires laborious point-wise annotation. To address this problem, we propose a novel self-supervised learning framework for snow points removal in LiDAR point clouds. Our method exploits the structural characteristic of the noise points: low spatial correlation with their neighbors. Our method consis…
In 2021, Japan recorded more than three times as much snowfall as usual, so road user maybe come across dangerous situation. The poor visibility caused by snow triggers traffic accidents. For example, 2021 January 19, due to the dry snow and the strong wind speed of 27 m / s, blizzards occurred and the outlook has been ineffective. Because of the whiteout phenomenon, multiple accidents with 17 casualties occurred, and 134 vehicles were stacked up for 10 hours over 1 km. At the night time zone, the temperature drops and the road surface tends to freeze. CCTV images on the road surface have the …
Australian National University（澳大利亚国立大学）；Beijing Institute of Technology（北京理工大学）；Tencent（腾讯）
Images captured in snowy days suffer from noticeable degradation of scene visibility, which degenerates the performance of current vision-based intelligent systems. Removing snow from images thus is an important topic in computer vision. In this paper, we propose a Deep Dense Multi-Scale Network (\textbf{DDMSNet}) for snow removal by exploiting semantic and geometric priors. As images captured in outdoor often share similar scenes and their visibility varies with depth from camera, such semantic and geometric information provides a strong prior for snowy image restoration. We incorporate the s…
University of Dayton（代顿大学）；Ford Motor Company（福特汽车公司）
We propose an Event-Based Snow Removal algorithm called EBSnoR. We developed a technique to measure the dwell time of snowflakes on a pixel using event-based camera data, which is used to carry out a Neyman-Pearson hypothesis test to partition event stream into snowflake and background events. The effectiveness of the proposed EBSnoR was verified on a new dataset called UDayton22EBSnow, comprised of front-facing event-based camera in a car driving through snow with manually annotated bounding boxes around surrounding vehicles. Qualitatively, EBSnoR correctly identifies events corresponding to …
In winter scenes, the degradation of images taken under snow can be pretty complex, where the spatial distribution of snowy degradation is varied from image to image. Recent methods adopt deep neural networks to directly recover clean scenes from snowy images. However, due to the paradox caused by the variation of complex snowy degradation, achieving reliable High-Definition image desnowing performance in real time is a considerable challenge. We develop a novel Efficient Pyramid Network with asymmetrical encoder-decoder architecture for real-time HD image desnowing. The general idea of our pr…
Xi’an Jiaotong University（西安交通大学）；The Hong Kong Polytechnic University（香港理工大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of many surveillance system. Various methods have been investigated extensively, but most only consider consistent rain/snow under stable background scenes. Rain/snow captured from practical surveillance camera, however, is always highly dynamic in time with the background scene transformed occasionally. To this issue, this paper proposes a novel rain/snow removal approach, which fully considers dynamic statistics of bo…
Compared to other severe weather image restoration tasks, single image desnowing is a more challenging task. This is mainly due to the diversity and irregularity of snow shape, which makes it extremely difficult to restore images in snowy scenes. Moreover, snow particles also have a veiling effect similar to haze or mist. Although current works can effectively remove snow particles with various shapes, they also bring distortion to the restored image. To address these issues, we propose a novel single image desnowing network called Star-Net. First, we design a Star type Skip Connection (SSC) t…
Jimei University（集美大学）；Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing（福建省海洋信息感知与智能处理重点实验室）
Snow removal causes challenges due to its characteristic of complex degradations. To this end, targeted treatment of multi-scale snow degradations is critical for the network to learn effective snow removal. In order to handle the diverse scenes, we propose a multi-scale projection transformer (MSP-Former), which understands and covers a variety of snow degradation features in a multi-path manner, and integrates comprehensive scene context information for clean reconstruction via self-attention operation. For the local details of various snow degradations, the local capture module is introduce…
Wavelet-Enhanced Desnowing: A Novel Single Image Restoration Approach for Traffic Surveillance under Adverse Weather Conditions
Automatic Control Research Institute, China Nuclear Power Engineering Co., Ltd.（中国核电工程有限公司自动控制研究院）；CNNC Engineering Research Center for Fuel Reprocessing（中核集团燃料后处理工程研究中心）；Chinese Aeronautical Establishment, Aviation Industry Corporation of China, Ltd（中国航空工业集团有限公司中国航空研究院）
LiDARs have been widely adopted to modern self-driving vehicles, providing 3D information of the scene and surrounding objects. However, adverser weather conditions still pose significant challenges to LiDARs since point clouds captured during snowfall can easily be corrupted. The resulting noisy point clouds degrade downstream tasks such as mapping. Existing works in de-noising point clouds corrupted by snow are based on nearest-neighbor search, and thus do not scale well with modern LiDARs which usually capture $100k$ or more points at 10Hz. In this paper, we introduce an unsupervised de-noi…
Semi-Supervised Video Desnowing Network via Temporal Decoupling Experts and Distribution-Driven Contrastive Regularization
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Cambridge（剑桥大学）；The Hong Kong University of Science and Technology（香港科技大学）
WeatherRemover: All-in-one Adverse Weather Removal with Multi-scale Feature Map Compression
Guangdong University of Technology（广东工业大学）；Sanda University（上海杉达学院）；University of Queensland（昆士兰大学）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；Zhejiang college of Security Technology（浙江安防职业技术学院）；Northwest China Research Institute of Electronic Equipment（西北电子设备研究所）；Shenzhen Research Institute of Big Data（深圳市大数据研究院）；Shenzhen University（深圳大学）；Misr Higher Institute for Commerce and Computers（米斯尔商业与计算机高等学院）
CFMW: Cross-modality Fusion Mamba for Robust Object Detection under Adverse Weather
School of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）；USC Viterbi School of Engineering, the University of Southern California（南加州大学维特比工程学院）；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 Robotics, Hunan University（湖南大学机器人学院）
DA2Diff: Exploring Degradation-aware Adaptive Diffusion Priors for All-in-One Weather Restoration
School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；Collaborative Innovation Center of Novel Software Technology and Industrialization（新型软件技术与产业化协同创新中心）；College of Computer Science and Technology, Anhui University of Technology（安徽工业大学计算机科学与技术学院）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
