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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 8
2515-2520

Lightweight image super-resolution reconstruction with multi-frequency feature and texture enhancement

Liu Yuanyuan
Zhang Yuxin
Wang Xiaoyan
Zhu Lu
College of Information Engineering, East China Jiaotong University, Nanchang 330013, China
DOI: 10.19734/j.issn.1001-3695.2023.09.0561

Abstract

Existing studies based on convolutional neural networks mainly focus on the accuracy of image reconstruction, ignoring problems such as excessive parameters, insufficient feature extraction, and resource waste. In response to the above, this paper proposed multi-frequency feature extraction network(MFEN), which designed a lightweight lattice information interaction structure and used channel segmentation with multi-mode convolution combination to reduce the number of parameters. By separating the low-frequency, mid-frequency, and high-frequency information of the image and extracting the feature heterogeneity, it improved the expressiveness and feature differentiation of the network, made the network pay more attention to the restoration of texture detail features, and reasonably allocated the computational resources. In addition, it integrated the local binary pattern(LBP) algorithm into the network to enhance texture sensitivity, which further improved the network's ability to extract details. It experimentally verifies that the proposed method balances complexity and performance well. In the 2X zooming experiments on the Set5 dataset, compared to the conventional image super-resolution algorithm(SRCNN) based on convolutional neural network and the newer algorithm(MADNet), the peak signal-to-noise ratio(PSNR) of the proposed method is improved by 1.31 dB and 0.12 dB respectively, and the number of parameters is reduced by 55% compared to MADNet.

Foundation Support

国家自然科学基金资助项目(61967007,61963016)
江西省重点研发计划重点资助项目(20201BBF61012)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.09.0561
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 8
Section: Technology of Graphic & Image
Pages: 2515-2520
Serial Number: 1001-3695(2024)08-038-2515-06

Publish History

[2024-01-24] Accepted Paper
[2024-08-05] Printed Article

Cite This Article

刘媛媛, 张雨欣, 王晓燕, 等. 基于多频特征和纹理增强的轻量化图像超分辨率重建 [J]. 计算机应用研究, 2024, 41 (8): 2515-2520. (Liu Yuanyuan, Zhang Yuxin, Wang Xiaoyan, et al. Lightweight image super-resolution reconstruction with multi-frequency feature and texture enhancement [J]. Application Research of Computers, 2024, 41 (8): 2515-2520. )

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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