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Dual-Complementary Convolution Network for Remote-Sensing Image Denoising
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AI
DOI：10.1109/LGRS.2021.3101851.png)
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Abstract
En 中文
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.
Keywords:
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
Journal
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