SAR Image Super-Resolution Based on Multiscale Edge Texture-Oriented GAN Approach

Synthetic aperture radar (SAR) imaging plays a crucial role in remote sensing, however, the inherent hardware characteristics, such as limited system bandwidth and the impacts of the synthetic aperture time, inevitably lead to resolution loss of SAR images. Low spatial resolution may lead to decreased image detail and blurred image edge. To enhance spatial resolution and suppress edge blurring in SAR images, this paper proposes a multi-scale edge texture-oriented generative adversarial network-based (METGAN) SAR image super-resolution approach. The proposed METGAN model comprises two phases: the super-resolution phase and the multi-scale edge texture enhancement phase. In the super-resolution phase, attention residual dense block (ARDB) units are designed to enhance spatial resolution without significant loss of edge information. In the multi-scale edge texture enhancement phase, the edge detection operator is incorporated into the texture coarse extraction (TCE) module for coarse extraction of image edges. Subsequently, within the attention multi-scale texture enhancement (AMTE) module, the atrous spatial pyramid pooling is integrated to enhance feature extraction capabilities. Based on the proposed ARDB unit and multi-scale edge texture enhancement stage, super-resolution SAR images with high spatial resolution and image details can be obtained. Experimental results on public datasets SARBuD and RSDD-SAR demonstrate that the proposed METGAN approach offers superior reconstruction performance compared to traditional methods and other GAN-based approaches.