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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 11
3472-3477,3508

Image super-resolution based on adaptive attention fusion feature extraction network

Wang Tuoran
Cheng Na
Ding Shijia
Wang Hongyu
School of Information & Communication Engineering, Dalian University of Technology, Dalian Liaoning 116024, China
DOI: 10.19734/j.issn.1001-3695.2023.03.0129

Abstract

To address the issue of large image super-resolution models with excessive parameters that are difficult to deploy, as well as the poor performance of existing lightweight image super-resolution models, this paper proposed an image super-resolution model based on adaptive attention fusion feature extraction network(AAFFEN). The model consisted primarily of a large kernel attention block and multiple efficient attention fusion feature extraction blocks. Firstly, the model extracted the shallow feature information using the large kernel attention block, and then a cascaded series of efficient attention fusion feature extraction block performed deep feature extraction, enhancement, refinement, and redistribution of the aggregated operations on the extracted shallow feature information. The efficient attention fusion feature extraction block consisted of three parts, such as progressive residual feature extraction module, channel contrast-aware attention module, and channel-spatial joint attention module. The proposed network could achieve better image super-resolution performance with fewer parameters, making it an excellent lightweight image super-resolution model. By evaluating the proposed method on popular benchmark datasets and comparing it with existing methods, the results show that the proposed method has more superior performance.

Foundation Support

大连市科技创新基金资助项目(2022JJ11CG002)
大连市人工智能重点实验室资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.03.0129
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 11
Section: Technology of Graphic & Image
Pages: 3472-3477,3508
Serial Number: 1001-3695(2023)11-042-3472-06

Publish History

[2023-06-08] Accepted Paper
[2023-11-05] Printed Article

Cite This Article

王拓然, 程娜, 丁士佳, 等. 基于自适应注意力融合特征提取网络的图像超分辨率 [J]. 计算机应用研究, 2023, 40 (11): 3472-3477,3508. (Wang Tuoran, Cheng Na, Ding Shijia, et al. Image super-resolution based on adaptive attention fusion feature extraction network [J]. Application Research of Computers, 2023, 40 (11): 3472-3477,3508. )

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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