Joint Back Projection and Residual Networks for Efficient Image Super-Resolution

Zhi Song Liu, Wan Chi Siu, Yui Lam Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Benefiting from the great power of graphic processor units, researchers can now come up with more and more sophisticated and complex deep learning structures to solve computer vision problems in various fields with excellent results. However, real-time performance is the bottleneck for deep learning in some applications, like image super-resolution. In this paper, we propose an image super-resolution making use of both the advantages of Back Projection and Residual Networks (BPRN). It generalizes the residual networks as a hierarchical back projection process. We use both convolution and deconvolution to down- and up-sample images to feedback the residues for super-resolution. Furthermore, we come up with a Lighter BPRN (L-BPRN) model to achieve similar state-of-the-art PSNR but fewer network parameters. The testing process is much faster and also accurate for image super-resolution with different scaling factors. Compared with recent deep learning based image super-resolution approaches, experimental results show that our proposed methods can achieve the state-of-the-art PSNR and SSIM performance as well as fast realization.

Original languageEnglish
Title of host publication2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1054-1060
Number of pages7
ISBN (Electronic)9789881476852
DOIs
Publication statusPublished - 12 Nov 2018
Event10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Honolulu, United States
Duration: 12 Nov 201815 Nov 2018

Publication series

Name2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings

Conference

Conference10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018
Country/TerritoryUnited States
CityHonolulu
Period12/11/1815/11/18

Keywords

  • back projection
  • deep learning
  • image super-resolution
  • residual networks

ASJC Scopus subject areas

  • Information Systems

Cite this