International Journal of Computational Intelligence Systems

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Volume 12, Issue 2, 2019, Pages 1592 - 1601

An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution

Authors
Wang Wei1, *, Jiang Yongbin1, Luo Yanhong2, Li Ji1, Wang Xin1, Zhang Tong1, *
1School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
2Hunan Children's Hospital, Changsha 410000, China
*Corresponding authors. Email: wangwei@csust.edu.cn, 1214304762@qq.com
Corresponding Authors
Wang Wei, Zhang Tong
Received 7 August 2019, Accepted 2 December 2019, Available Online 14 December 2019.
DOI
10.2991/ijcis.d.191209.001How to use a DOI?
Keywords
Deep residual dense network (DRDN); Single image super-resolution; Fusion reconstruction; Residual dense connection; Multi-hop connection
Abstract

In recent years, more and more attention has been paid to single image super-resolution reconstruction (SISR) by using deep learning networks. These networks have achieved good reconstruction results, but how to make better use of the feature information in the image, how to improve the network convergence speed, and so on still need further study. According to the above problems, a novel deep residual dense network (DRDN) is proposed in this paper. In detail, DRDN uses the residual-dense structure for local feature fusion, and finally carries out global residual fusion reconstruction. Residual-dense connection can make full use of the features of low-resolution images from shallow to deep layers, and provide more low-resolution image information for super-resolution reconstruction. Multi-hop connection can make errors spread to each layer of the network more quickly, which can alleviate the problem of difficult training caused by deepening network to a certain extent. The experiments show that DRDN not only ensure good training stability and successfully converge but also has less computing cost and higher reconstruction efficiency.

Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
12 - 2
Pages
1592 - 1601
Publication Date
2019/12/14
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.191209.001How to use a DOI?
Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Wang Wei
AU  - Jiang Yongbin
AU  - Luo Yanhong
AU  - Li Ji
AU  - Wang Xin
AU  - Zhang Tong
PY  - 2019
DA  - 2019/12/14
TI  - An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution
JO  - International Journal of Computational Intelligence Systems
SP  - 1592
EP  - 1601
VL  - 12
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.191209.001
DO  - 10.2991/ijcis.d.191209.001
ID  - Wei2019
ER  -