Qing Guo, Hua Qi, Jingyang Sun, Felix Juefei-Xu, Lei Ma, Di Lin, Wei Feng, Song Wang
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Specifically, we identify spatially-variant predictive filtering (SPFilt) that adaptively predicts proper kernels via a deep network to filter different individual pixels. Since the filtering can be implemented via well-accelerated convolution, our method can be significantly efficient. We further propose the <i>EfDeRain+</i> that contains three main contributions to address residual rain traces, multi-scale, and diverse rain patterns without harming efficiency. <i>First</i>, we propose the uncertainty-aware cascaded predictive filtering (UC-PFilt) that can identify the difficulties of reconstructing clean pixels via predicted kernels and remove the residual rain traces effectively. <i>Second</i>, we design the weight-sharing multi-scale dilated filtering (WS-MS-DFilt) to handle multi-scale rain streaks without harming the efficiency. <i>Third</i>, to eliminate the gap across diverse rain patterns, we propose a novel data augmentation method (<i>i.e</i>., <i>RainMix</i>) to train our deep models. By combining all contributions with sophisticated analysis on different variants, our final method outperforms baseline methods on six single-image deraining datasets and one video-deraining dataset in terms of both recovery quality and speed. In particular, <i>EfDeRain+</i> can derain within about 6.3 ms on a <span>\\(481\\times 321\\)</span> image and is over 74 times faster than the top baseline method with even better recovery quality. We release code in https://github.com/tsingqguo/efficientderainplus.</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"68 1","pages":""},"PeriodicalIF":11.6000,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"EfficientDeRain+: Learning Uncertainty-Aware Filtering via RainMix Augmentation for High-Efficiency Deraining\",\"authors\":\"Qing Guo, Hua Qi, Jingyang Sun, Felix Juefei-Xu, Lei Ma, Di Lin, Wei Feng, Song Wang\",\"doi\":\"10.1007/s11263-024-02281-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Deraining is a significant and fundamental computer vision task, aiming to remove the rain streaks and accumulations in an image or video. Existing deraining methods usually make heuristic assumptions of the rain model, which compels them to employ complex optimization or iterative refinement for high recovery quality. However, this leads to time-consuming methods and affects the effectiveness of addressing rain patterns, deviating from the assumptions. This paper proposes a simple yet efficient deraining method by formulating deraining as a predictive filtering problem without complex rain model assumptions. Specifically, we identify spatially-variant predictive filtering (SPFilt) that adaptively predicts proper kernels via a deep network to filter different individual pixels. Since the filtering can be implemented via well-accelerated convolution, our method can be significantly efficient. We further propose the <i>EfDeRain+</i> that contains three main contributions to address residual rain traces, multi-scale, and diverse rain patterns without harming efficiency. <i>First</i>, we propose the uncertainty-aware cascaded predictive filtering (UC-PFilt) that can identify the difficulties of reconstructing clean pixels via predicted kernels and remove the residual rain traces effectively. <i>Second</i>, we design the weight-sharing multi-scale dilated filtering (WS-MS-DFilt) to handle multi-scale rain streaks without harming the efficiency. <i>Third</i>, to eliminate the gap across diverse rain patterns, we propose a novel data augmentation method (<i>i.e</i>., <i>RainMix</i>) to train our deep models. By combining all contributions with sophisticated analysis on different variants, our final method outperforms baseline methods on six single-image deraining datasets and one video-deraining dataset in terms of both recovery quality and speed. In particular, <i>EfDeRain+</i> can derain within about 6.3 ms on a <span>\\\\(481\\\\times 321\\\\)</span> image and is over 74 times faster than the top baseline method with even better recovery quality. 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EfficientDeRain+: Learning Uncertainty-Aware Filtering via RainMix Augmentation for High-Efficiency Deraining
Deraining is a significant and fundamental computer vision task, aiming to remove the rain streaks and accumulations in an image or video. Existing deraining methods usually make heuristic assumptions of the rain model, which compels them to employ complex optimization or iterative refinement for high recovery quality. However, this leads to time-consuming methods and affects the effectiveness of addressing rain patterns, deviating from the assumptions. This paper proposes a simple yet efficient deraining method by formulating deraining as a predictive filtering problem without complex rain model assumptions. Specifically, we identify spatially-variant predictive filtering (SPFilt) that adaptively predicts proper kernels via a deep network to filter different individual pixels. Since the filtering can be implemented via well-accelerated convolution, our method can be significantly efficient. We further propose the EfDeRain+ that contains three main contributions to address residual rain traces, multi-scale, and diverse rain patterns without harming efficiency. First, we propose the uncertainty-aware cascaded predictive filtering (UC-PFilt) that can identify the difficulties of reconstructing clean pixels via predicted kernels and remove the residual rain traces effectively. Second, we design the weight-sharing multi-scale dilated filtering (WS-MS-DFilt) to handle multi-scale rain streaks without harming the efficiency. Third, to eliminate the gap across diverse rain patterns, we propose a novel data augmentation method (i.e., RainMix) to train our deep models. By combining all contributions with sophisticated analysis on different variants, our final method outperforms baseline methods on six single-image deraining datasets and one video-deraining dataset in terms of both recovery quality and speed. In particular, EfDeRain+ can derain within about 6.3 ms on a \(481\times 321\) image and is over 74 times faster than the top baseline method with even better recovery quality. We release code in https://github.com/tsingqguo/efficientderainplus.
期刊介绍:
The International Journal of Computer Vision (IJCV) serves as a platform for sharing new research findings in the rapidly growing field of computer vision. It publishes 12 issues annually and presents high-quality, original contributions to the science and engineering of computer vision. The journal encompasses various types of articles to cater to different research outputs.
Regular articles, which span up to 25 journal pages, focus on significant technical advancements that are of broad interest to the field. These articles showcase substantial progress in computer vision.
Short articles, limited to 10 pages, offer a swift publication path for novel research outcomes. They provide a quicker means for sharing new findings with the computer vision community.
Survey articles, comprising up to 30 pages, offer critical evaluations of the current state of the art in computer vision or offer tutorial presentations of relevant topics. These articles provide comprehensive and insightful overviews of specific subject areas.
In addition to technical articles, the journal also includes book reviews, position papers, and editorials by prominent scientific figures. These contributions serve to complement the technical content and provide valuable perspectives.
The journal encourages authors to include supplementary material online, such as images, video sequences, data sets, and software. This additional material enhances the understanding and reproducibility of the published research.
Overall, the International Journal of Computer Vision is a comprehensive publication that caters to researchers in this rapidly growing field. It covers a range of article types, offers additional online resources, and facilitates the dissemination of impactful research.