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.
Bibliographical noteFunding Information:
This work was supported by the Korean Government (MSIT) through the National Research Foundation of Korea (NRF) under Grant NRF-2018R1A2B3003896 and Grant NRF-2021R1A4A1031864.
© 2013 IEEE.
- Face super-resolution
- convolutional neural network
- patch matching
- reference-based super-resolution
ASJC Scopus subject areas
- General Computer Science
- General Materials Science
- General Engineering
- Electrical and Electronic Engineering