App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 8

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 Jiangxi 330013, China
DOI: 10.19734/j.issn.1001-3695.2023.09.0561

Abstract

With the advancement of deep learning in recent years, image super-resolution has accomplished astonishing feats. However, 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, Multi-frequency Feature Extraction Network (MFEN) is proposed, which designs a lightweight lattice information interaction structure and uses 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, improves the expressiveness and feature differentiation of the network, makes the network pay more attention to the restoration of texture detail features, and reasonably allocates the computational resources. In addition, the Local Binary Pattern (LBP) algorithm is integrated into the network to enhance texture sensitivity, which further improves the network's ability to extract details. It is experimentally verified 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项目(20201BBF61012)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.09.0561
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 8

Publish History

[2024-01-24] Accepted Paper

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). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.09.0561. (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). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.09.0561. )

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  • Application Research of Computers Monthly Journal
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    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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