Dual-Complementary Convolution Network for Remote-Sensing Image Denoising

被引:10
作者
Jia, Xinlei [1 ,2 ]
Peng, Yali [1 ,2 ]
Li, Jun [3 ]
Ge, Bao [4 ]
Xin, Yunhong [4 ]
Liu, Shigang [1 ,2 ]
机构
[1] Minist Educ, Key Lab Modern Teaching Technol, Xian 710062, Peoples R China
[2] Shaanxi Normal Univ, Sch Comp Sci, Xian 710119, Peoples R China
[3] Nanjing Normal Univ, Sch Comp Sci & Technol, Nanjing 210046, Peoples R China
[4] Shaanxi Normal Univ, Sch Phys & Informat Technol, Xian 710119, Peoples R China
基金
中国国家自然科学基金;
关键词
Remote sensing; Convolution; Image denoising; Noise reduction; Feature extraction; Training; Discrete wavelet transforms; Convolutional neural network (CNN); remote-sensing image denoising; shuffling operation; wavelet transform;
D O I
10.1109/LGRS.2021.3101851
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Remote-sensing images serve as key data sources which play a crucial role in recording the target information of ground features. Due to the limitations of the existing imaging equipment, environments, and transmission conditions, the obtained remote-sensing images are usually contaminated by noise in real-world scenarios. To address this problem, we propose a dual-complementary convolution network (DCCNet), including structural and detailed subnetwork, for repairing the structure and details of noisy remote-sensing images. More specifically, they generate multiresolution inputs via discrete wavelet transform and shuffling operation, respectively. Since the convolution operation is imposed on low-resolution inputs, the network parameters are considerably reduced. Experimental evaluations demonstrate that our proposed network exhibits superior performance to other competing methods in remote-sensing public datasets. The code of the DCCNet is available at https://github.com/20155104009/DCCNet.
引用
收藏
页数:5
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