{"id":2767,"date":"2026-04-06T16:37:54","date_gmt":"2026-04-06T08:37:54","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=2767"},"modified":"2026-06-05T18:02:41","modified_gmt":"2026-06-05T10:02:41","slug":"image-denoising-based-on-deep-feature-fusion-and-u-net-network","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=image-denoising-based-on-deep-feature-fusion-and-u-net-network","title":{"rendered":"Image denoising based on deep feature fusion and U-Net network"},"content":{"rendered":"\n
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<\/i> 2025<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 28, Issue 10<\/a><\/p>\n<\/div>\n\n\n\n

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Yong Zhang<\/i><\/a><\/p>\n\n\n\n

Software College, Shenyang Normal University Shenyang 110034 China<\/p>\n<\/div>\n\n\n\n

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Received: November 12, 2024
Accepted: December 21, 2024
Publication Date: April 6, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Proposed image denoising method<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202510_28(10).0020<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Image noise hinders the understanding of images by advanced visual tasks, and removing image noise is a challenging task. The traditional denoising methods can not only destroy the texture of the image, but can not save the image texture after removing the noise. Therefore, we propose a novel image denoising method based on deep feature fusion and U-Net network. This new method uses a two-branch U-Net network to fuse features and preserve image texture. In this paper, two encoders with independent parameters are proposed to extract more useful information respectively, and a fusion module with series connection is proposed to make better use of the extracted information and remove redundant information. Finally, the decoder is used to reconstruct the image, and the U-Net peer connection is used on the symmetric convolutional layer in the network. A large number of experimental results show that the proposed algorithm can effectively remove synthetic noise and real noise, and the reconstructed image has a good effect on both subjective visual effect and objective evaluation index.<\/p>\n\n\n\n

Keywords: Image denoising, deep feature fusion, U-Net network, symmetric convolutional layer<\/em><\/p>\n\n\n\n

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