New techniques for preserving global structure and denoising with low information loss in single-image super-resolution

Conference Paper

This work identifies and addresses two important technical challenges in single-image super-resolution: (1) how to upsample an image without magnifying noise and (2) how to preserve large scale structure when upsampling. We summarize the techniques we developed for our second place entry in Track 1 (Bicubic Downsampling), seventh place entry in Track 2 (Realistic Adverse Conditions), and seventh place entry in Track 3 (Realistic difficult) in the 2018 NTIRE Super-Resolution Challenge. Furthermore, we present new neural network architectures that specifically address the two challenges listed above: denoising and preservation of large-scale structure.

Full Text

Duke Authors

Cited Authors

  • Bei, Y; Damian, A; Hu, S; Menon, S; Ravi, N; Rudin, C

Published Date

  • December 13, 2018

Published In

Volume / Issue

  • 2018-June /

Start / End Page

  • 987 - 994

Electronic International Standard Serial Number (EISSN)

  • 2160-7516

International Standard Serial Number (ISSN)

  • 2160-7508

International Standard Book Number 13 (ISBN-13)

  • 9781538661000

Digital Object Identifier (DOI)

  • 10.1109/CVPRW.2018.00132

Citation Source

  • Scopus