SDNet: mutil-branch for single image deraining using swin

• URL: http://arxiv.org/abs/2105.15077v1
• Date: Mon, 31 May 2021 16:06:02 GMT
• Title: SDNet: mutil-branch for single image deraining using swin
• Authors: Fuxiang Tan, YuTing Kong, Yingying Fan, Feng Liu, Daxin Zhou, Hao zhang, Long Chen, Liang Gao and Yurong Qian
• Abstract summary: We introduce Swin-transformer into the field of image deraining for the first time. Specifically, we improve the basic module of Swin-transformer and design a three-branch model to implement single-image rain removal. Our proposed method has performance and inference speed advantages over the current mainstream single-image rain streaks removal models.
• Score: 14.574622548559269
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rain streaks degrade the image quality and seriously affect the performance of subsequent computer vision tasks, such as autonomous driving, social security, etc. Therefore, removing rain streaks from a given rainy images is of great significance. Convolutional neural networks(CNN) have been widely used in image deraining tasks, however, the local computational characteristics of convolutional operations limit the development of image deraining tasks. Recently, the popular transformer has global computational features that can further facilitate the development of image deraining tasks. In this paper, we introduce Swin-transformer into the field of image deraining for the first time to study the performance and potential of Swin-transformer in the field of image deraining. Specifically, we improve the basic module of Swin-transformer and design a three-branch model to implement single-image rain removal. The former implements the basic rain pattern feature extraction, while the latter fuses different features to further extract and process the image features. In addition, we employ a jump connection to fuse deep features and shallow features. In terms of experiments, the existing public dataset suffers from image duplication and relatively homogeneous background. So we propose a new dataset Rain3000 to validate our model. Therefore, we propose a new dataset Rain3000 for validating our model. Experimental results on the publicly available datasets Rain100L, Rain100H and our dataset Rain3000 show that our proposed method has performance and inference speed advantages over the current mainstream single-image rain streaks removal models.The source code will be available at https://github.com/H-tfx/SDNet.

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