OMA-SSR: Optical-guided multi-kernel attention based SAR image super-resolution reconstruction network

Abstract

Synthetic aperture radar (SAR) has been widely studied and applied in many fields. Although image super-resolution technology has been successfully applied to SAR imaging in recent years, there is less research on large-scale factor SAR image super-resolution methods. A more effective method is to obtain comprehensive information to guide the reconstruction of SAR images. In fact, the co-registered characteristics of high-resolution optical images have been successfully applied to improve the quality of SAR images. Inspired by this, an optical-guided multi-kernel attention based SAR image super-resolution reconstruction network (OMA-SSR) is proposed. The proposed multi-modal mutual attention (MMA) module in this network can effectively establish the dependency between SAR image features and optical image features. This network also designs a deep feature extraction module for SAR images, which includes a channel-splitted multi-kernel attention (CSMA) module and residual connections. CSMA module splits SAR image channels, extracts features in different ranges through multi-kernel convolution, and finally fuses the extracted features between different channels. Experimental results on the Sen1-2 and QXS datasets show that the proposed OMA-SSR performs well in evaluation indicators and visual effects of SAR image super-resolution reconstruction.