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Dual-Complementary Convolution Network for Remote-Sensing Image Denoising

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS(2022)

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Abstract
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.
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Key words
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
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