Sonar Image Super-Resolution Based on Structure-Texture Dual Preservation

Sonar imaging system plays a crucial role in ocean exploration since it can overcome the limitations of light conditions. However, the challenge of low resolution remains in sonar images (SIs) due to sonar imaging characteristics and varying compression for low-bandwidth transmission. Most existing image super-resolution (SR) methods treat both the structure and texture in the same way, thus failing to simultaneously capture the rich global-local information. Nevertheless, both structure and texture are essential for the visual quality and applications of SIs. In this study, we propose a structure-texture dual-preserving network (STDPNet) tailored to capture both local texture details and global structure in a parallel manner for SISR. To further explore the internal correlation between structure and texture features, a feature interaction strategy is introduced. Moreover, conventional loss functions for SR often yield smooth results. We propose a hybrid loss function with spectral and local gradient-aware components to preserve frequency content and enhance texture detail. Experimental results validate the superior performance of the proposed STDPNet.