Article
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R-PreNet: Deraining Network Based on Image Background Prior
Version 1
: Received: 26 September 2023 / Approved: 27 September 2023 / Online: 27 September 2023 (05:38:23 CEST)
A peer-reviewed article of this Preprint also exists.
Jiao, C.; Meng, F.; Li, T.; Cao, Y. R-PreNet: Deraining Network Based on Image Background Prior. Appl. Sci. 2023, 13, 11970. Jiao, C.; Meng, F.; Li, T.; Cao, Y. R-PreNet: Deraining Network Based on Image Background Prior. Appl. Sci. 2023, 13, 11970.
Abstract
Single image deraining (SID) has shown its importance in many advanced computer vision tasks. Though many CNN based image deraining methods have been proposed, how to effectively remove raindrops while maintaining background structure remains a challenge that needs to be overcome. Most of the deraining work focuses on removing rain streaks, but in heavy rain images, the dense accumulation of rainwater or the rain curtain effect significantly interferes with the effective removal of rain streaks, and often introduces some artifacts that make the scene more blurry. In this paper, we propose a new network structure R-PReNet for single image deraining with good background structure maintaining. This framework fully utilizes the cyclic recursive structure of PReNet. Moreover, we introduce residual channel prior (RCP) and feature fusion modules for better deraining performance by focusing on background feature information. Compared with the previous methods, our method has significantly improvement effect on the rainstorm image with the artifacts removing and good visual detail restoring.
Keywords
single image deraining; residual channel prior; interactive fusion
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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