ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2106.10437
44
1
v1v2v3v4 (latest)

One-to-many Approach for Improving Super-Resolution

19 June 2021
Sieun Park
Eunho Lee
    SupR
ArXiv (abs)PDFHTML
Abstract

Recently, there has been discussions on the ill-posed nature of super-resolution that multiple possible reconstructions exist for a given low-resolution image. Using normalizing flows, SRflow[23] achieves state-of-the-art perceptual quality by learning the distribution of the output instead of a deterministic output to one estimate. In this paper, we adapt the concepts of SRFlow to improve GAN-based super-resolution by properly implementing the one-to-many property. We modify the generator to estimate a distribution as a mapping from random noise. We improve the content loss that hampers the perceptual training objectives. We also propose additional training techniques to further enhance the perceptual quality of generated images. Using our proposed methods, we were able to improve the performance of ESRGAN[1] in x4 perceptual SR and achieve the state-of-the-art LPIPS score in x16 perceptual extreme SR by applying our methods to RFB-ESRGAN[21].

View on arXiv
Comments on this paper