Residual magnifier: A dense information flow network for super resolution

Zhan Shu, Mengcheng Cheng, Biao Yang, Zhuo Su, Xiangjian He

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Recently, deep learning methods have been successfully applied to single image super-resolution tasks. However, some networks with extreme depth failed to achieve better performance because of the insufficient utilization of the local residual information extracted at each stage. To solve the above question, we propose a Dense Information Flow Network (DIF-Net), which can fully extract and utilize the local residual information at each stage to accomplish a better reconstruction. Specifically, we present a Two-stage Residual Extraction Block (TREB) to extract the shallow and deep local residual information at each stage. The dense connection mechanism is introduced throughout the model and within TREBs to dramatically increase the information flow. Meanwhile this mechanism prevents the shallow features extracted earlier from being diluted. Finally, we propose a lightweight subnet (residual enhancer) to efficiently recycle the overflow residual information from the backbone net for detail enhancement of the residual image. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods with relatively-less parameters.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Multimedia and Expo, ICME 2019
PublisherIEEE Computer Society
Pages646-651
Number of pages6
ISBN (Electronic)9781538695524
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes
Event2019 IEEE International Conference on Multimedia and Expo, ICME 2019 - Shanghai, China
Duration: 8 Jul 201912 Jul 2019

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
Volume2019-July
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2019 IEEE International Conference on Multimedia and Expo, ICME 2019
Country/TerritoryChina
CityShanghai
Period8/07/1912/07/19

Keywords

  • Dense connection
  • Enhanced residual information
  • Single image super-resolution

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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