Variational AutoEncoder for Reference based Image Super-Resolution
- URL: http://arxiv.org/abs/2106.04090v1
- Date: Tue, 8 Jun 2021 04:12:38 GMT
- Title: Variational AutoEncoder for Reference based Image Super-Resolution
- Authors: Zhi-Song Liu and Wan-Chi Siu and Li-Wen Wang
- Abstract summary: We propose a reference based image super-resolution, for which any arbitrary image can act as a reference for super-resolution.
Even using random map or low-resolution image itself, the proposed RefVAE can transfer the knowledge from the reference to the super-resolved images.
- Score: 27.459299640768773
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we propose a novel reference based image super-resolution
approach via Variational AutoEncoder (RefVAE). Existing state-of-the-art
methods mainly focus on single image super-resolution which cannot perform well
on large upsampling factors, e.g., 8$\times$. We propose a reference based
image super-resolution, for which any arbitrary image can act as a reference
for super-resolution. Even using random map or low-resolution image itself, the
proposed RefVAE can transfer the knowledge from the reference to the
super-resolved images. Depending upon different references, the proposed method
can generate different versions of super-resolved images from a hidden
super-resolution space. Besides using different datasets for some standard
evaluations with PSNR and SSIM, we also took part in the NTIRE2021 SR Space
challenge and have provided results of the randomness evaluation of our
approach. Compared to other state-of-the-art methods, our approach achieves
higher diverse scores.
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