Robust Reference-based Super-Resolution with Similarity-Aware Deformable Convolution

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In this paper, we propose a novel and efficient reference feature extraction module referred to as the Similarity Search and Extraction Network (SSEN) for reference-based super-resolution (RefSR) tasks. The proposed module extracts aligned relevant features from a reference image to increase the performance over single image super-resolution (SISR) methods. In contrast to conventional algorithms which utilize brute-force searches or optical flow estimations, the proposed algorithm is end-to-end trainable without any additional supervision or heavy computation, predicting the best match with a single network forward operation. Moreover, the proposed module is aware of not only the best matching position but also the relevancy of the best match. This makes our algorithm substantially robust when irrelevant reference images are given, overcoming the major cause of the performance degradation when using existing RefSR methods. Furthermore, our module can be utilized for self-similarity SR if no reference image is available. Experimental results demonstrate the superior performance of the proposed algorithm compared to previous works both quantitatively and qualitatively.
Publisher
IEEE Conference on Computer Vision and Pattern Recognition
Issue Date
2020-06
Language
English
Citation

IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, pp.8422 - 8431

ISSN
1063-6919
DOI
10.1109/CVPR42600.2020.00845
URI
http://hdl.handle.net/10203/278670
Appears in Collection
EE-Conference Papers(학술회의논문)
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