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Anisotropic Weighted Total Variation Feature Fusion Network for Remote Sensing Image Denoising

文献类型：期刊论文

作者Qi, Huiqing1,2,3; Tan, Shengli3; Li, Zhichao1
刊名REMOTE SENSING
出版日期2022-12-01
卷号14期号:24页码:34
关键词remote sensing image noise removal weighted total variation feature fusion convolutional neural network
DOI10.3390/rs14246300
通讯作者Li, Zhichao(lizc@igsnrr.ac.cn)
英文摘要Remote sensing images are widely applied in instance segmentation and objetive recognition; however, they often suffer from noise, influencing the performance of subsequent applications. Previous image denoising works have only obtained restored images without preserving detailed texture. To address this issue, we proposed a novel model for remote sensing image denoising, called the anisotropic weighted total variation feature fusion network (AWTVF(2)Net), consisting of four novel modules (WTV-Net, SOSB, AuEncoder, and FB). AWTVF(2)Net combines traditional total variation with a deep neural network, improving the denoising ability of the proposed approach. Our proposed method is evaluated by PSNR and SSIM metrics on three benchmark datasets (NWPU, PatternNet, UCL), and the experimental results show that AWTVF(2)Net can obtain 0.12 similar to 19.39 dB/0.0237 similar to 0.5362 higher on PSNR/SSIM values in the Gaussian noise removal and mixed noise removal tasks than State-of-The-Art (SoTA) algorithms. Meanwhile, our model can preserve more detailed texture features. The SSEQ, BLIINDS-II, and BRISQUE values of AWTVF(2)Net on the three real-world datasets (AVRIS Indian Pines, ROSIS University of Pavia, HYDICE Urban) are 3.94 similar to 12.92 higher, 8.33 similar to 27.5 higher, and 2.2 similar to 5.55 lower than those of the compared methods, respectively. The proposed framework can guide subsequent remote sensing image applications, regarding the pre-processing of input images.
WOS关键词QUALITY ASSESSMENT ; RESTORATION ; CNN
资助项目Informatization Plan of Chinese Academy of Sciences ; National Key Research and Development Program of China ; National Natural Science Foundation of China ; Shanghai Science and Technology Commission Foundation ; [CAS-WX2021PY-0109] ; [2018AAA0101001] ; [11731004] ; [12126323] ; [11761141005] ; [22DZ2229014] ; [20511100200]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000903368400001
出版者MDPI
资助机构Informatization Plan of Chinese Academy of Sciences ; National Key Research and Development Program of China ; National Natural Science Foundation of China ; Shanghai Science and Technology Commission Foundation
源URL[http://ir.igsnrr.ac.cn/handle/311030/188715]  
专题中国科学院地理科学与资源研究所
通讯作者Li, Zhichao
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
2.East China Normal Univ, Shanghai Inst AI Educ, Shanghai 200062, Peoples R China
3.East China Normal Univ, Sch Math Sci, Shanghai 200241, Peoples R China
推荐引用方式
GB/T 7714		 Qi, Huiqing,Tan, Shengli,Li, Zhichao. Anisotropic Weighted Total Variation Feature Fusion Network for Remote Sensing Image Denoising[J]. REMOTE SENSING,2022,14(24):34.
APA Qi, Huiqing,Tan, Shengli,&Li, Zhichao.(2022).Anisotropic Weighted Total Variation Feature Fusion Network for Remote Sensing Image Denoising.REMOTE SENSING,14(24),34.
MLA Qi, Huiqing,et al."Anisotropic Weighted Total Variation Feature Fusion Network for Remote Sensing Image Denoising".REMOTE SENSING 14.24(2022):34.

入库方式： OAI收割

来源：地理科学与资源研究所

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