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I Understand Computer Science > Computer Vision and Pattern Recognition arXiv:2106.01863 (cs) [Submitted on 3 Jun 2021] Title:Robust Reference-based Super-Resolution via C2-Matching Authors:Yuming Jiang, Kelvin C.K. Chan, Xintao Wang, Chen Change Loy, Ziwei Liu View a PDF of the paper titled Robust Reference-based Super-Resolution via C2-Matching, by Yuming Jiang and 4 other authors View PDF

Abstract:Reference-based Super-Resolution (Ref-SR) has recently emerged as a promising paradigm to enhance a low-resolution (LR) input image by introducing an additional high-resolution (HR) reference image. Existing Ref-SR methods mostly rely on implicit correspondence matching to borrow HR textures from reference images to compensate for the information loss in input images. However, performing local transfer is difficult because of two gaps between input and reference images: the transformation gap (e.g. scale and rotation) and the resolution gap (e.g. HR and LR). To tackle these challenges, we propose C2-Matching in this work, which produces explicit robust matching crossing transformation and resolution. 1) For the transformation gap, we propose a contrastive correspondence network, which learns transformation-robust correspondences using augmented views of the input image. 2) For the resolution gap, we adopt a teacher-student correlation distillation, which distills knowledge from the easier HR-HR matching to guide the more ambiguous LR-HR matching. 3) Finally, we design a dynamic aggregation module to address the potential misalignment issue. In addition, to faithfully evaluate the performance of Ref-SR under a realistic setting, we contribute the Webly-Referenced SR (WR-SR) dataset, mimicking the practical usage scenario. Extensive experiments demonstrate that our proposed C2-Matching significantly outperforms state of the arts by over 1dB on the standard CUFED5 benchmark. Notably, it also shows great generalizability on WR-SR dataset as well as robustness across large scale and rotation transformations.

Comments:	To appear in CVPR2021. The source code is available at this https URL
Subjects:	Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
Cite as:	arXiv:2106.01863 [cs.CV]
(or arXiv:2106.01863v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2106.01863 Focus to learn more arXiv-issued DOI via DataCite

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From: Yuming Jiang [view email] [v1] Thu, 3 Jun 2021 16:40:36 UTC (10,347 KB) Full-text links:

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