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Survey on rain removal from videos or a single image

Science China(Information Sciences) 2022年第1期65卷 72-94页

作者： Hong WANG Yichen WU Minghan LI Qian ZHAO Deyu MENG School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University Pazhou Lab

rain can cause performance degradation of outdoor computer vision tasks. Thus, the exploration of rain removal from videos or a single image has drawn considerable attention in the field of image processing. Recently, various deraining methodologies have been proposed. However, no comprehensive survey work has yet been conducted to summarize existing deraining algorithms and quantitatively compare their generalization ability, and especially, no off-the-shelf toolkit exists for accumulating and categorizing recent representative methods for easy performance reproduction and deraining capability evaluation. In this regard, herein, we present a comprehensive overview of existing video and single image deraining methods as well as reproduce and evaluate current state-of-the-art deraining methods. In particular, these approaches are mainly classified into model-and deep-learning-based methods, and more elaborate branches of each method are presented. Inherent abilities, especially generalization performance, of the state-of-the-art methods have been both quantitatively and visually analyzed through thorough experiments conducted on synthetic and real benchmark datasets. Moreover, to facilitate the reproduction of existing deraining methods for general users, we present a comprehensive repository with detailed classification, including direct links to 85 deraining papers, 24 relevant project pages, source codes of 12 and 25 algorithms for video and single image deraining, respectively, 5 and 10 real and synthesized datasets, respectively, and 7 frequently used image quality evaluation metrics, along with the corresponding computation codes. Research limitations worthy of further exploration have also been discussed for future research along this direction.

关键词： rain removal maximum a posterior estimation deep learning generalization performance comprehensive repository

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Towards Robust rain removal with Unet

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Computers, Materials & Continua 2023年第4期 879-890页

作者： Boxia Hu Yaqi Sun Yufei Yang Ze Ouyang Feng Zhang School of Mathematics Hunan UniversityChangsha410082China College of Mathematics and Statistics Hengyang Normal UniversityHengyang421002China College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China School of Mathematics Changsha UniversityChangsha410022China

Image deraining has become a hot topic in the field of computervision. It is the process of removing rain streaks from an image to reconstructa high-quality background. This study aims at improving the performance ofimage rain streak removal and reducing the disruptive effects caused by *** better fit the rain removal task, an innovative image deraining method isproposed, where a kernel prediction network with Unet++ is designed andused to filter rainy images, and rainy-day images are used to estimate thepixel-level kernel for rain removal. To minimize the gap between synthetic andreal data and improve the performance in real rainy image handling, a lossfunction and an effective data optimization method are suggested. In contrastwith other methods, the loss function consists of Structural Similarity Indexloss, edge loss, and L1 loss, and it is adopted to improve performance. Theproposed algorithm can improve the Peak Signal-to-Noise ratio by 1.3% whencompared to conventional approaches. Experimental results indicate that theproposed method can achieve a better efficiency and preserve more imagestructure than several classical methods.

关键词： rain removal edge optimization robust Unet++

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rain Detection and removal of Sequential Images

Rain Detection and Removal of Sequential Images

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2011 Chinese Control and Decision Conference（CCDC）

作者： Ming Zhou is with School of Computer and Information,Hefei University of Technology,Hefei 230009,China.Zhichao Zhu is with School of Computer and Information,Hefei University of Technology,Hefei 230009,China.Rong Deng is with School of Computer and Information,Hefei University of Technology,Hefei 230009,China.Shuai Fang is with School of Computer and Information,Hefei University of Technology,Hefei 230009,China

Detection and removal of rain in image is a difficult and crucial problem due to the complexity of rain and its negative effects on *** spatio-temporal property and the chromatic property of rain are comprehensively *** the two properties,a simple but effective algorithm is proposed to detect and remove the rain of sequential *** time complexity of k-means is reduced by our optimized *** each pixel is classified by the optimized k-means according to the spatio-temporal *** the chromatic constraint is used to identify accurately the *** the blending parameter α is computed and the rain in each frame is *** method can handle both light and heavy rain in *** results prove that our algorithm performs simply but effectively.

关键词： rain detection rain removal image restoration image procession chromatic property K-means

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