Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 9
2873-2880

Multiple description coded image enhancement method with joint learning of side-decoding and central-decoding features

Zhao Lijun1
Cao Congying1
Zhang Jinjing2
Bai Huihui3
Zhao Yao3
Wang Anhong1
1. College of Electronic Information Engineering, Taiyuan University of Science & Technology, Taiyuan 030024, China
2. College of Big Science & Technology, North University of China, Taiyuan 030051, China
3. Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China
DOI: 10.19734/j.issn.1001-3695.2022.02.0061

Abstract

This paper proposed MDC image enhancement method by using joint learning of side-decoding and central-decoding features, which considered the problems of side decoding image enhancement and central decoding image enhancement at the same time, so it could realize better network training by optimizing central decoding and side decoding features through joint learning. First, considering side independent decoding and central joint decoding features for MDC, this paper proposed a network-sharing side low-resolution feature extraction network to effectively extract features from two-side decoded images with the same content and different details, while designed a residual recursive compensation network structure and applied it into both side and central low-resolution feature extraction network. Secondly, it designed a multiple description up-sampling reconstruction network, which adopted parameter sharing strategy for partial layers of network, which could reduce parameter number of network model and improve network generalization ability. Finally, it proposed multiple description central up-sampling reconstruction network to perform deep feature fusion with two low-resolution side features and central features to enhance multiple description compressed images. A large number of experimental results show that the proposed method is superior to several image enhancement methods such as ARCNN, FastARCNN, DnCNN, WSR and DWCNN in terms of model complexity, objective quality and visual quality assessment.

Foundation Support

太原科技大学博士科研启动基金资助项目(20192023)
山西省基础研究计划资助项目(202103021223284)
来晋工作优秀博士奖励资金资助项目(20192055)
太原科技大学研究生教育创新项目(XCX212029)
国家自然基金资助项目(61972023,62072325)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.02.0061
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 9
Section: Technology of Graphic & Image
Pages: 2873-2880
Serial Number: 1001-3695(2022)09-049-2873-08

Publish History

[2022-04-21] Accepted Paper
[2022-09-05] Printed Article

Cite This Article

赵利军, 曹聪颖, 张晋京, 等. 联合边路和中路解码特征学习的多描述编码图像增强方法 [J]. 计算机应用研究, 2022, 39 (9): 2873-2880. (Zhao Lijun, Cao Congying, Zhang Jinjing, et al. Multiple description coded image enhancement method with joint learning of side-decoding and central-decoding features [J]. Application Research of Computers, 2022, 39 (9): 2873-2880. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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