{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T18:40:03Z","timestamp":1732646403859,"version":"3.28.2"},"reference-count":64,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T00:00:00Z","timestamp":1723593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Youth Innovation Promotion Association of the Chinese Academy of Sciences, Chinese Academy of Sciences","award":["2020438"]},{"name":"Anhui International Joint Research Center for Ancient Architecture Intellisencing and Multi-Dimensional Modeling, Anhui Provincial Department of Science and Technology","award":["GJZZX2022KF02"]},{"name":"HFIPS Director\u2019s Fund, Hefei Institutes of Physical Science","award":["YZJJ202404-CX","YZJJ202303-TS"]},{"name":"Anhui Provincial Key Research and Development Project, Anhui Provincial Department of Science and Technology","award":["202304a05020053"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Atmospheric turbulence is a key factor contributing to data distortion in mid-to-long-range target observation tasks. Neural networks have become a powerful tool for dealing with such problems due to their strong ability to fit nonlinearities in the spatial domain. However, the degradation in data is not confined solely to the spatial domain but is also present in the frequency domain. In recent years, the academic community has come to recognize the significance of frequency domain information within neural networks. There remains a gap in research on how to combine dual-domain information to reconstruct high-quality images in the field of blind turbulence image restoration. Drawing upon the close association between spatial and frequency domain degradation information, we introduce a novel neural network architecture, termed Dual-Domain Removal Turbulence Network (DDRTNet), designed to improve the quality of reconstructed images. DDRTNet incorporates multiscale spatial and frequency domain attention mechanisms, combined with a dual-domain collaborative learning strategy, effectively integrating global and local information to achieve efficient restoration of atmospheric turbulence-degraded images. Experimental findings demonstrate significant advantages in performance for DDRTNet compared to existing methods, validating its effectiveness in the task of blind turbulence image restoration.<\/jats:p>","DOI":"10.3390\/rs16162972","type":"journal-article","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T09:23:27Z","timestamp":1723627407000},"page":"2972","source":"Crossref","is-referenced-by-count":0,"title":["Dual-Domain Cooperative Recovery of Atmospheric Turbulence Degradation Images"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"http:\/\/orcid.org\/0009-0001-8315-2370","authenticated-orcid":false,"given":"Jianxiao","family":"Qiu","sequence":"first","affiliation":[{"name":"Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China"},{"name":"Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China"}]},{"given":"Runbo","family":"Jiang","sequence":"additional","affiliation":[{"name":"Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China"},{"name":"Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China"}]},{"given":"Wenwen","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}]},{"given":"Dongfeng","family":"Shi","sequence":"additional","affiliation":[{"name":"Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China"},{"name":"Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China"}]},{"given":"Bingzhang","family":"Hu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China"}]},{"given":"Yingjian","family":"Wang","sequence":"additional","affiliation":[{"name":"Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China"},{"name":"Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"074002","DOI":"10.1088\/1361-6420\/ab0e4b","article-title":"Restoration of atmospheric turbulence-distorted images via RPCA and quasiconformal maps","volume":"35","author":"Lau","year":"2019","journal-title":"Inverse Probl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1669","DOI":"10.1109\/PROC.1975.10035","article-title":"Electromagnetic beam propagation in turbulent media","volume":"63","author":"Fante","year":"1975","journal-title":"Proc. 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