Gradient Prior Dilated Convolution Network for Remote Sensing Image Super-Resolution
Ziyu Liu, Ruyi Feng, Lizhe Wang, Tieyong Zeng
Abstract
Super-resolution (SR) aims to recover highresolution (HR) image from a single or multiple low-resolution (LR) images, compensating for the limitations of satellite sensor imaging. Deep convolutional neural networks (CNN) have made great achievement in remote sensing image super resolution. In this letter, we propose a novel gradient prior dilated convolutional network (GPDCN) for remote sensing images SR, which obtainscontextual spatial connections and alleviates structural distortions. The GPDCN comprise a multi-scale feature extraction network and a feature reconstruction network. The former employs a double-path dilated residual block (DPDRB) with dilation convolution to increase a receptive field, a global selfattention module (GSA) to detect long-range reliance among image patches, and a gradient propagation network (GPN) to extract high-level gradient information. The latter uses the MHOA module to reconstruct the feature by collecting the highorder characteristics of multiple frequency bands. Experiments with the Massachusetts_Roads and 3K VEHICLE_SR datasets demonstrate that the GPDCN outperforms recent techniques concerning both quantitative and qualitative measures.