{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T15:09:14Z","timestamp":1781536154307,"version":"3.54.5"},"reference-count":38,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,23]],"date-time":"2023-07-23T00:00:00Z","timestamp":1690070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072256"],"award-info":[{"award-number":["62072256"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, a unified optimization model for medical image fusion based on tensor decomposition and the non-subsampled shearlet transform (NSST) is proposed. The model is based on the NSST method and the tensor decomposition method to fuse the high-frequency (HF) and low-frequency (LF) parts of two source images to obtain a mixed-frequency fused image. In general, we integrate low-frequency and high-frequency information from the perspective of tensor decomposition (TD) fusion. Due to the structural differences between the high-frequency and low-frequency representations, potential information loss may occur in the fused images. To address this issue, we introduce a joint static and dynamic guidance (JSDG) technique to complement the HF\/LF information. To improve the result of the fused images, we combine the alternating direction method of multipliers (ADMM) algorithm with the gradient descent method for parameter optimization. Finally, the fused images are reconstructed by applying the inverse NSST to the fused high-frequency and low-frequency bands. Extensive experiments confirm the superiority of our proposed TDFusion over other comparison methods.<\/jats:p>","DOI":"10.3390\/s23146616","type":"journal-article","created":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T03:03:25Z","timestamp":1690167805000},"page":"6616","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["TDFusion: When Tensor Decomposition Meets Medical Image Fusion in the Nonsubsampled Shearlet Transform Domain"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6665-9738","authenticated-orcid":false,"given":"Rui","family":"Zhang","sequence":"first","affiliation":[{"name":"Jiangsu Province Key Lab on Image Processing and Image Communication, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Jiangsu Province Key Lab on Image Processing and Image Communication, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoze","family":"Sun","sequence":"additional","affiliation":[{"name":"Jiangsu Province Key Lab on Image Processing and Image Communication, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4494-9918","authenticated-orcid":false,"given":"Lizhen","family":"Deng","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Jiangsu Province Key Lab on Image Processing and Image Communication, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105253","DOI":"10.1016\/j.compbiomed.2022.105253","article-title":"A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics","volume":"144","author":"Azam","year":"2022","journal-title":"Comput. 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