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Dual-Domain Cooperative Recovery of Atmospheric Turbulence Degradation Images

文献类型：期刊论文

作者Qiu, Jianxiao1,2,3; Jiang, Runbo1,2,3; Meng, Wenwen4; Shi, Dongfeng1,2,3; Hu, Bingzhang2,3; Wang, Yingjian1,2,3
刊名REMOTE SENSING
出版日期2024-08-01
卷号16
关键词atmospheric turbulence image restoration deep learning image processing
DOI10.3390/rs16162972
通讯作者Shi, Dongfeng(dfshi@aiofm.ac.cn)
英文摘要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.
资助项目Youth Innovation Promotion Association of the Chinese Academy of Sciences, Chinese Academy of Sciences ; Anhui International Joint Research Center for Ancient Architecture Intellisencing and Multi-Dimensional Modeling, Anhui Provincial Department of Science and Technology[GJZZX2022KF02] ; HFIPS Director's Fund ; Hefei Institutes of Physical Science[YZJJ202404-CX] ; Hefei Institutes of Physical Science[YZJJ202303-TS] ; Anhui Provincial Key Research and Development Project, Anhui Provincial Department of Science and Technology[202304a05020053] ; [2020438]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001305994400001
出版者MDPI
资助机构Youth Innovation Promotion Association of the Chinese Academy of Sciences, Chinese Academy of Sciences ; Anhui International Joint Research Center for Ancient Architecture Intellisencing and Multi-Dimensional Modeling, Anhui Provincial Department of Science and Technology ; HFIPS Director's Fund ; Hefei Institutes of Physical Science ; Anhui Provincial Key Research and Development Project, Anhui Provincial Department of Science and Technology
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/135194]  
专题中国科学院合肥物质科学研究院
通讯作者Shi, Dongfeng
作者单位1.Univ Sci & Technol China, Sci Isl Branch, Grad Sch, Hefei 230026, Peoples R China
2.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Hefei Inst Phys Sci, Key Lab Atmospher Opt, Hefei 230031, Peoples R China
3.Adv Laser Technol Lab Anhui Prov, Hefei 230037, Peoples R China
4.Hefei Univ, Sch Artificial Intelligence & Big Data, Hefei 230601, Peoples R China
推荐引用方式
GB/T 7714		 Qiu, Jianxiao,Jiang, Runbo,Meng, Wenwen,et al. Dual-Domain Cooperative Recovery of Atmospheric Turbulence Degradation Images[J]. REMOTE SENSING,2024,16.
APA Qiu, Jianxiao,Jiang, Runbo,Meng, Wenwen,Shi, Dongfeng,Hu, Bingzhang,&Wang, Yingjian.(2024).Dual-Domain Cooperative Recovery of Atmospheric Turbulence Degradation Images.REMOTE SENSING,16.
MLA Qiu, Jianxiao,et al."Dual-Domain Cooperative Recovery of Atmospheric Turbulence Degradation Images".REMOTE SENSING 16(2024).

入库方式： OAI收割

来源：合肥物质科学研究院

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