Gluing Reference Patches Together for Face Super-Resolution
- Authors
- Kim, Ji-Soo; Ko, Keunsoo; Kim, Chang-Su
- Issue Date
- 2021
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Face super-resolution; convolutional neural network; patch matching; reference-based super-resolution
- Citation
- IEEE ACCESS, v.9, pp.169321 - 169334
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE ACCESS
- Volume
- 9
- Start Page
- 169321
- End Page
- 169334
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/138485
- DOI
- 10.1109/ACCESS.2021.3138442
- ISSN
- 2169-3536
- Abstract
- Face super-resolution is a domain-specific super-resolution task to generate a high-resolution facial image from a low-resolution one. In this paper, we propose a novel face super-resolution network, called CollageNet, to super-resolve an input image by exploiting a reference image of an identical person at the patch level. First, we extract feature pyramids from input and reference images to exploit multi-scale information hierarchically. Next, we compute the patch-wise similarities between input and reference feature pyramids and select the K most similar reference patches to each input patch. Then, we compose a collaged feature pyramid by gluing those selected patches together. Finally, we obtain a super-resolved image by blending the collaged feature pyramid and the input feature. Experimental results demonstrate that the proposed CollageNet yields state-of-the-art performances.
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Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
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