Abstract
The performance of image super-resolution (SR) has been greatly improved by using convolutional neural networks. Most of the previous SR methods have been studied up to ×4 upsampling, and few were studied for ×16 upsampling. The general approach for perceptual ×4 SR is using GAN with VGG based perceptual loss, however, we found that it creates inconsistent details for perceptual ×16 SR. To this end, we have investigated loss functions and we propose to use GAN with LPIPS [23] loss for perceptual extreme SR. In addition, we use U-net structure discriminator [14] together to consider both the global and local context of an input image. Experimental results show that our method outperforms the conventional perceptual loss, and we achieved second and first place in the LPIPS and PI measures respectively for NTIRE 2020 perceptual extreme SR challenge.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020 |
| Publisher | IEEE Computer Society |
| Pages | 1705-1712 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781728193601 |
| DOIs | |
| Publication status | Published - 2020 Jun |
| Event | 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020 - Virtual, Online, United States Duration: 2020 Jun 14 → 2020 Jun 19 |
Publication series
| Name | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
|---|---|
| Volume | 2020-June |
| ISSN (Print) | 2160-7508 |
| ISSN (Electronic) | 2160-7516 |
Conference
| Conference | 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020 |
|---|---|
| Country/Territory | United States |
| City | Virtual, Online |
| Period | 20/6/14 → 20/6/19 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering