Deep learning based image super-resolution with coupled backpropagation

Tiantong Guo, Hojjat S. Mousavi, Vishal Monga

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

Recently deep learning methods have been applied to image super-resolution (SR). Typically, these approaches involve training a single convolutional neural network that is trained to perform resolution enhancement. We propose a new low-complexity but effective algorithm called Superresolution with Coupled Backpropagation (SR-CBP) which builds two Coupled Auto-encoder Networks (CAN), resp. the high-resolution (HR) and low-resolution (LR) networks, that capture the features of both high and low resolution images. The two networks in CAN have the ability to self-reconstruct its own input. Specifically, SR-CBP allows joint training of the LR and HR networks to have middle layer representations that agree for a pair of images (high-resolution image and its corresponding low-resolution version). For an LR input image, its middle layer representation obtained via the trained LR network can be fed into the HR network to generate the SR result. Preliminary experiments show that SR-CBP can produce better results than state of the art single image superresolution methods based on sparse representations. The memory and storage requirements of CAN are lesser than existing deep learning based SR methods.

Original languageEnglish (US)
Title of host publication2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages237-241
Number of pages5
ISBN (Electronic)9781509045457
DOIs
StatePublished - Apr 19 2017
Event2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States
Duration: Dec 7 2016Dec 9 2016

Publication series

Name2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings

Other

Other2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
Country/TerritoryUnited States
CityWashington
Period12/7/1612/9/16

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications

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