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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 11
3509-3514

Dual U-shaped network with non-local prior fusion for image compressed sensing

Lin Lepinga,b
Hu Shangjunb
Ouyang Ninga,b
a. Key Laboratory of Cognitive Radio & Information Processing Ministry of Education, b. School of Information & Communication, Guilin University of Electronic Technology, Guilin Guangxi 541004, China
DOI: 10.19734/j.issn.1001-3695.2023.12.0635

Abstract

Aiming at the problem that insufficient detail processing ability and inadequate use of measurement values in current non-iterative network-based image compressed sensing methods, this paper proposed a novel dual U-shaped gated network(DUGN) for image compressed sensing. This network improved upon the existing U-shaped network structure and enhanced the learning ability in compressed sensing tasks. This paper proposed a measurements non-local fusion(MNLF) module based on cross-attention mechanism, MNLF integrated non-local prior information from measurements into the deep network and guided the network reconstruction. Furthermore, this paper proposed a window gated network(WGN) module as the basic blocks to enable the network to learn larger ranges of image information and enhance the ability to process details. Experimental results demonstrate that compared with existing compressed sensing reconstruction methods, DUGN has higher PSNR and SSIM on Set11 and better performance in the authenticity of image reconstruction.

Foundation Support

国家自然科学基金资助项目(62001133,61661017,61362021)
广西科技基地和人才专项资助项目(桂科AD19110060)
广西高等学校千名中青年骨干教师培育计划资助项目
广西自然科学基金资助项目(2017GXNSFBA198212)
广西无线宽带通信与信号处理重点实验室基金资助项目(GXKL06200114)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.12.0635
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 11
Section: Technology of Graphic & Image
Pages: 3509-3514
Serial Number: 1001-3695(2024)11-045-3509-06

Publish History

[2024-03-28] Accepted Paper
[2024-11-05] Printed Article

Cite This Article

林乐平, 胡尚鋆, 欧阳宁. 双U型门控网络融合非局部先验的图像压缩感知重建方法 [J]. 计算机应用研究, 2024, 41 (11): 3509-3514. (Lin Leping, Hu Shangjun, Ouyang Ning. Dual U-shaped network with non-local prior fusion for image compressed sensing [J]. Application Research of Computers, 2024, 41 (11): 3509-3514. )

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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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