Cross-view geo-localization in GNSS-denied environments aims to determine an unknown location by matching drone-view images with the correct geo-tagged satellite-view images from a large gallery. Recent research shows that learning discriminative image representations under specific weather conditions can significantly enhance performance. However, the frequent occurrence of unseen extreme weather conditions hinders progress. This paper introduces MCGF, a Multi-weather Cross-view Geo-localization Framework designed to dynamically adapt to unseen weather conditions. MCGF establishes a joint optimization between image restoration and geo-localization using denoising diffusion models. For image restoration, MCGF incorporates a shared encoder and a lightweight restoration module to help the backbone eliminate weather-specific information. For geo-localization, MCGF uses EVA-02 as a backbone for feature extraction, with cross-entropy loss for training and cosine distance for testing. Extensive experiments on University160k-WX demonstrate that MCGF achieves competitive results for geo-localization in varying weather conditions.
SemiDDM-Weather: A Semi-supervised Learning Framework for All-in-one Adverse Weather Removal
School of Computer Science and Cyber Engineering, Guangzhou University（广州大学计算机科学与网络工程学院）；College of Engineering, Huaqiao University（华侨大学工学院）；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安全学院）
Semantic-Aware, Physics-Informed, Geometry-Grounded Weather Video Synthesis
University of Leeds, UK（利兹大学，英国）；National University of Singapore, Singapore（新加坡国立大学）；University of California, Los Angeles, USA（美国加州大学洛杉矶分校）；Hefei University of Technology, China（合肥工业大学）
Reliable point cloud data is essential for perception tasks \textit{e.g.} in robotics and autonomous driving applications. Adverse weather causes a specific type of noise to light detection and ranging (LiDAR) sensor data, which degrades the quality of the point clouds significantly. To address this issue, this letter presents a novel point cloud adverse weather denoising deep learning algorithm (4DenoiseNet). Our algorithm takes advantage of the time dimension unlike deep learning adverse weather denoising methods in the literature. It performs about 10\% better in terms of intersection over …
VRAE: Vertical Residual Autoencoder for License Plate Denoising and Deblurring
Ha Noi University of Science and Technology（河内科学技术大学）；Center of Environmental Intelligence（环境智能中心）；VinUniversity（文大学）；University of Southern California（南加州大学）
Estimating Atmospheric Variables from Digital Typhoon Satellite Images via Conditional Denoising Diffusion Models
Imperial College London（帝国理工学院）；University of Cambridge（剑桥大学）；Grantham Institute for Climate Change and the Environment, Imperial College London（帝国理工学院格兰瑟姆气候变化与环境研究所）；National Center for Artificial Intelligence CENIA, Chile（智利国家人工智能中心CENIA）
Diffusion-Based Restoration for Multi-Modal 3D Object Detection in Adverse Weather
School of Xingzhi College, South China Normal University（星智学院，华南师范大学）；College of Big Data and Internet, Shenzhen Technology University（大数据与互联网学院，深圳科技大学）；School of Data Science and Engineering, Xingzhi College, South China Normal University（数据科学与工程学院，星智学院，华南师范大学）；Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology（电子与计算机工程系，香港科技大学）；College of Intelligence Science and Technology, National University of Defense Technology（智能科学与技术学院，国防科技大学）
When Color-Space Decoupling Meets Diffusion for Adverse-Weather Image Restoration
School of Computer Science and Engineering, Nanjing University of Science and Technology（计算机科学与工程学院，南京理工大学）；School of Electrical Engineering, Southeast University（电气工程学院，东南大学）；School of Intelligence Science and Technology, Nanjing University（智能科学与技术学院，南京大学）
CORENet: Cross-Modal 4D Radar Denoising Network with LiDAR Supervision for Autonomous Driving
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Institute of Control Engineering（北京控制工程研究所）
WeatherGen: A Unified Diverse Weather Generator for LiDAR Point Clouds via Spider Mamba Diffusion
PCA Lab, Nanjing University of Science and Technology（南京理工大学光电信息科学与工程实验室）；School of Automation, Southeast University（东南大学自动化学院）；State Key Laboratory for Novel Software Technology, Nanjing University（南京大学软件新技术国家重点实验室）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；University of Oxford（牛津大学）；University of Waterloo（滑铁卢大学）
The intricacy of 3D surfaces often results cutting-edge point cloud denoising (PCD) models in surface degradation including remnant noise, wrongly-removed geometric details. Although using multi-scale patches to encode the geometry of a point has become the common wisdom in PCD, we find that simple aggregation of extracted multi-scale features can not adaptively utilize the appropriate scale information according to the geometric information around noisy points. It leads to surface degradation, especially for points close to edges and points on complex curved surfaces. We raise an intriguing q…
ProvRain: Rain-Adaptive Denoising and Vehicle Detection via MobileNet-UNet and Faster R-CNN
School of Computer Science Engineering Vellore Institute of Technology Chennai, India（计算机科学工程学院 维洛雷技术学院 印度钦奈）；School Of Electronics Engineering Vellore Institute of Technology Chennai, India（电子工程学院 维洛雷技术学院 印度钦奈）
WeatherPrompt: Multi-modality Representation Learning for All-Weather Drone Visual Geo-Localization
School of Computer Engineering and Science, Shanghai University, China（上海大学计算机工程与科学学院）；Faculty of Science and Technology and Institute of Collaborative Innovation, University of Macau, China（澳门大学科技学院和协同创新研究院）
Modern applications such as self-driving cars and drones rely heavily upon robust object detection techniques. However, weather corruptions can hinder the object detectability and pose a serious threat to their navigation and reliability. Thus, there is a need for efficient denoising, deraining, and restoration techniques. Generative adversarial networks and transformers have been widely adopted for image restoration. However, the training of these methods is often unstable and time-consuming. Furthermore, when used for object detection (OD), the output images generated by these methods may pr…
Optical image data have been used by the Remote Sensing workforce to study land use and cover since such data is easily interpretable. Synthetic Aperture Radar (SAR) has the characteristic of obtaining images during all-day, all-weather and provides object information that is different from visible and infrared sensors. However, SAR images have more speckle noise and fewer dimensions. This paper presents a method for denoising, feature extraction and compares classifications of Optical and SAR images. The image was denoised using K-Singular Value Decomposition (K-SVD) algorithm. A method to ma…
NimbusGS: Unified 3D Scene Reconstruction under Hybrid Weather
School of Computer Science and Technology, Ocean University of China（中国海洋大学计算机科学与技术学院）；Singapore Management University（新加坡管理大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Sanya Oceanographic Institution, Ocean University of China（中国海洋大学三亚海洋研究院）
Gachon University（嘉泉大学）；Hanyang University (ERICA)（汉阳大学（ERICA））
Recently, the application of low rank minimization to image denoising has shown remarkable denoising results which are equivalent or better than those of the existing state-of-the-art algorithms. However, due to iterative nature of low rank optimization, estimation of residual noise is an essential requirement after each iteration. Currently, this noise is estimated by using the filtered noise in the previous iteration without considering the geometric structure of the given image. This estimate may be affected in the presence of moderate and severe levels of noise. To obtain a more reliable e…
As quotidian use of sophisticated cameras surges, people in modern society are more interested in capturing fine-quality images. However, the quality of the images might be inferior to people's expectations due to the noise contamination in the images. Thus, filtering out the noise while preserving vital image features is an essential requirement. Current existing denoising methods have their own assumptions on the probability distribution in which the contaminated noise is sampled for the method to attain its expected denoising performance. In this paper, we utilize our recent Gramian-based f…
University of Missouri-Columbia（密苏里大学哥伦比亚分校）；Instituto de Telecomunicações（电信研究所）；University of Beira Interior（贝拉内陆大学）
In this letter we propose to denoise digital color images via an improved geometric diffusion scheme. By introducing edges detected from all three color channels into the diffusion the proposed scheme avoids color smearing artifacts. Vector valued diffusion is used to control the smoothing and the geometry of color images are taken into consideration. Color edge strength function computed from different planes is introduced and it stops the diffusion spread across chromatic edges. Experimental results indicate that the scheme achieves good denoising with edge preservation when compared to othe…
JOANNEUM RESEARCH Forschungsgesellschaft mbH（约阿内研究有限责任公司）；AIT Austrian Institute of Technology GmbH（奥地利技术研究所有限责任公司）；Austro Control, Österreichische Gesellschaft für Zivilluftfahrt mbH（奥斯特罗管制奥地利民用航空有限责任公司）；MeteoServe Wetterdienst GmbH（MeteoServe气象服务有限责任公司）
In air traffic management (ATM) all necessary operations (tactical planing, sector configuration, required staffing, runway configuration, routing of approaching aircrafts) rely on accurate measurements and predictions of the current weather situation. An essential basis of information is delivered by weather radar images (WXR), which, unfortunately, exhibit a vast amount of disturbances. Thus, the improvement of these datasets is the key factor for more accurate predictions of weather phenomena and weather conditions. Image processing methods based on texture analysis and geometric operators …
Towards Generalized Range-View LiDAR Segmentation in Adverse Weather
University of Electronic Science and Technology of China（电子科技大学）；Dalian University of Technology（大连理工大学）；Lancaster University（兰卡斯特大学）；Nanyang Technological University（南洋理工大学）；Tongji University（同济大学）
针对距离视图 LiDAR 分割在恶劣天气下泛化不足的问题，提出轻量模块化 GAS 与 RDC 双分支框架，在不改动核心架构的情况下显著提升鲁棒性且推理开销极小。
