Dual-Complementary Convolution Network for Remote-Sensing Image Denoising

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.