Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 1
288-295

Cascade discrete wavelet multi-band decomposition attention image denoising method

Wang Li1,2
Li Xiaoxia1,2
Qin Jiamin3
Zhu He1,2
Zhou Yingyue1,2
1. School of Information Engineering, Southwest University of Science & Technology, Mianyang Sichuan 621010, China
2. Sichuan Industrial Autonomous & Controllable Artificial Intelligence Engineering Technology Research Center, Mianyang Sichuan 621010, China
3. Sichuan Mianyang 404 Hospital, Mianyang Sichuan 621000, China
DOI: 10.19734/j.issn.1001-3695.2023.06.0245

Abstract

To address the issue of high-frequency information loss and poor detail preservation ability in image denoising networks caused by downsampling, this paper proposed a cascade discrete wavelet multi-band decomposition attention image denoising network. The multi-scale cascade discrete wavelet transform structure decomposed the original image into high and low-frequency sub-bands at multiple scales, replacing traditional downsampling and reducing high-frequency information loss. The multi-band feature enhancement module employed convolutional kernels of different scales to process high and low-frequency features in parallel. By repeating this process twice at each level of the subnetwork, it effectively enhanced both global and local key feature information. The multi-band decomposition attention module evaluated the importance of texture detail components through attention and weighted the detail features of different bands, which helped the multi-band feature enhancement module better distinguish between noise and edge details. The multi-band selective feature fusion module fused multi-scale multi-band features to enhance selective features, improving the model's ability to remove noise at different scales. The proposed method achieves PSNR/SSIM values of 39.35 dB/0.918 and 39.72 dB/0.955 on the SIDD and DND datasets, respectively. The experimental results demonstrate that the proposed method outperforms mainstream denoising methods and produces clearer visual effects, such as texture details and edges.

Foundation Support

国家自然科学基金资助项目(62071399)
四川省科技计划资助项目(2023YFG0262,2021YFG0383)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.06.0245
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 1
Section: Technology of Graphic & Image
Pages: 288-295
Serial Number: 1001-3695(2024)01-046-0288-08

Publish History

[2023-08-03] Accepted Paper
[2024-01-05] Printed Article

Cite This Article

王力, 李小霞, 秦佳敏, 等. 级联离散小波多频带分解注意力图像去噪方法 [J]. 计算机应用研究, 2024, 41 (1): 288-295. (Wang Li, Li Xiaoxia, Qin Jiamin, et al. Cascade discrete wavelet multi-band decomposition attention image denoising method [J]. Application Research of Computers, 2024, 41 (1): 288-295. )

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  • 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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