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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>\n                    Light field (LF) contains abundant spatial geometric information of the real-world scenes, and it can enhance the performance of the computer vision tasks. However, it is challenging to acquire LF images with high spatial resolution. So far, super-resolution (SR) techniques based on deep learning make insufficient use of frequency information, which limits the performance of LFSR. To address this issue, we propose a frequency-guided feature fusion network (i.e., LF-F\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\({}^{3}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    Net) for LFSR. To be specific, the proposed LF-F\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\({}^{3}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    Net is a dual-branch network. One branch employs the multi-dimensional frequency feature extraction (MFFE) module to capture frequency information from individual views, while the other branch further integrates the extracted frequency information with spatial features through the multi-dimensional spatial\u2013frequency fusion (MSFF) module. Furthermore, we introduce a frequency loss to prevent the loss of critical frequency content during training, thereby maximizing the potential performance of network. The experimental results show that the LF-F\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\({}^{3}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    Net can significantly improve the SR performance and outperforms the state-of-the-art methods.\n                  <\/jats:p>","DOI":"10.1145\/3787967","type":"journal-article","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T14:07:04Z","timestamp":1768831624000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["LF-F\n                    <sup>3<\/sup>\n                    Net: Frequency-Guided Feature Fusion Network for Light Field Image Super-Resolution"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8644-3549","authenticated-orcid":false,"given":"Yulei","family":"Yang","sequence":"first","affiliation":[{"name":"Chongqing University of Technology, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8286-538X","authenticated-orcid":false,"given":"Zongju","family":"Peng","sequence":"additional","affiliation":[{"name":"Chongqing University of Technology, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9857-6113","authenticated-orcid":false,"given":"Huabo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chongqing University of Technology, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3921-9152","authenticated-orcid":false,"given":"Fen","family":"Chen","sequence":"additional","affiliation":[{"name":"Chongqing University of Technology, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4567-1091","authenticated-orcid":false,"given":"Qianliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chongqing University of Technology, Chongqing, China"}]}],"member":"320","published-online":{"date-parts":[[2026,2,10]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Stanford University. 2021. 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