{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:22:13Z","timestamp":1760149333529,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:00:00Z","timestamp":1689552000000},"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":["62174128","2022J185","21JBGSZ-OCY9-0004","22JBGS-QCY4-0006"],"award-info":[{"award-number":["62174128","2022J185","21JBGSZ-OCY9-0004","22JBGS-QCY4-0006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007834","name":"Ningbo Natural Science Foundation","doi-asserted-by":"publisher","award":["62174128","2022J185","21JBGSZ-OCY9-0004","22JBGS-QCY4-0006"],"award-info":[{"award-number":["62174128","2022J185","21JBGSZ-OCY9-0004","22JBGS-QCY4-0006"]}],"id":[{"id":"10.13039\/100007834","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Xian City Science and Technology Plan Project","award":["62174128","2022J185","21JBGSZ-OCY9-0004","22JBGS-QCY4-0006"],"award-info":[{"award-number":["62174128","2022J185","21JBGSZ-OCY9-0004","22JBGS-QCY4-0006"]}]},{"name":"Xian City Science and Technology Plan Project","award":["62174128","2022J185","21JBGSZ-OCY9-0004","22JBGS-QCY4-0006"],"award-info":[{"award-number":["62174128","2022J185","21JBGSZ-OCY9-0004","22JBGS-QCY4-0006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the advent of deep learning, significant progress has been made in low-light image enhancement methods. However, deep learning requires enormous paired training data, which is challenging to capture in real-world scenarios. To address this limitation, this paper presents a novel unsupervised low-light image enhancement method, which first introduces the frequency-domain features of images in low-light image enhancement tasks. Our work is inspired by imagining a digital image as a spatially varying metaphoric \u201cfield of light\u201d, then subjecting the influence of physical processes such as diffraction and coherent detection back onto the original image space via a frequency-domain to spatial-domain transformation (inverse Fourier transform). However, the mathematical model created by this physical process still requires complex manual tuning of the parameters for different scene conditions to achieve the best adjustment. Therefore, we proposed a dual-branch convolution network to estimate pixel-wise and high-order spatial interactions for dynamic range adjustment of the frequency feature of the given low-light image. Guided by the frequency feature from the \u201cfield of light\u201d and parameter estimation networks, our method enables dynamic enhancement of low-light images. Extensive experiments have shown that our method performs well compared to state-of-the-art unsupervised methods, and its performance approximates the level of the state-of-the-art supervised methods qualitatively and quantitatively. At the same time, the light network structure design allows the proposed method to have extremely fast inference speed (near 150 FPS on an NVIDIA 3090 Ti GPU for an image of size 600\u00d7400\u00d73). Furthermore, the potential benefits of our method to object detection in the dark are discussed.<\/jats:p>","DOI":"10.3390\/rs15143580","type":"journal-article","created":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T01:35:16Z","timestamp":1689644116000},"page":"3580","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Unsupervised Low-Light Image Enhancement via Virtual Diffraction Information in Frequency Domain"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0080-6306","authenticated-orcid":false,"given":"Xupei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Optoelectronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanlin","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Optoelectronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Optoelectronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4184-1300","authenticated-orcid":false,"given":"Xiang","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Optoelectronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8566-9226","authenticated-orcid":false,"given":"Shanglin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Optoelectronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanghao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Optoelectronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3538","DOI":"10.1109\/TIP.2013.2261309","article-title":"Naturalness preserved enhancement algorithm for non-uniform illumination images","volume":"22","author":"Wang","year":"2013","journal-title":"IEEE Trans. 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