A joint deep-network-based image restoration algorithm for multi-degradations

Xu Sun, Xiaoguang Li, Li Zhuo, Kin Man Lam, Jiafeng Li

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

1 Citation (Scopus)

Abstract

In the procedures of image acquisition, compression, and transmission, captured images usually suffer from various degradations, such as low-resolution and compression distortion. Although there have been a lot of research done on image restoration, they usually aim to deal with a single degraded factor, ignoring the correlation of different degradations. To establish a restoration framework for multiple degradations, a joint deep-network-based image restoration algorithm is proposed in this paper. The proposed convolutional neural network is composed of two stages. Firstly, a de-blocking subnet is constructed, using two cascaded neural network. Then, super-resolution is carried out by a 20-layer very deep network with skipping links. Cascading these two stages forms a novel deep network. Experimental results on the Set5, Setl4 and BSD100 benchmarks demonstrate that the proposed method can achieve better results, in terms of both the subjective and objective performances.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Multimedia and Expo, ICME 2017
PublisherIEEE Computer Society
Pages301-306
Number of pages6
ISBN (Electronic)9781509060672
DOIs
Publication statusPublished - 28 Aug 2017
Event2017 IEEE International Conference on Multimedia and Expo, ICME 2017 - Hong Kong, Hong Kong
Duration: 10 Jul 201714 Jul 2017

Conference

Conference2017 IEEE International Conference on Multimedia and Expo, ICME 2017
CountryHong Kong
CityHong Kong
Period10/07/1714/07/17

Keywords

  • Image restoration
  • Joint deep network
  • Multi-degradations

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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