Influenced by the measurement mechanism, the marine environment, and other factors, side-scan sonar images often exhibit high noise levels and low resolution. This presents significant challenges for high-resolution underwater topography imaging and small-scale target detection. To address this, this paper proposes a super-resolution reconstruction method for side-scan sonar images based on a diffusion model and texture consistency. Firstly, based on the imaging mechanism of side-scan sonar, the seafloor reverberation model is introduced to establish the degradation mechanism for underwater acoustic images. Next, a feature bootstrap module is developed to integrate low-resolution image and texture features, projecting them into a potential semantic space. Finally, through multiple iterations of the diffusion model, guidance information at different scales is incorporated into the generation process. This enables the reconstruction of target contours, edge features, and high-frequency details at various iteration stages. As a result, texture-consistent, high-quality side-scan sonar images are obtained. Experimental results demonstrate that the proposed algorithm outperforms the comparison methods in both subjective visual effects and objective evaluation metrics. Among them, the FID scores reached 106.66, 117.27, and 148.41, respectively. In addition, we conducted SSS pipeline target segmentation experiments and large-area georeferenced image reconstruction experiments, further verifying the feasibility and effectiveness of the proposed method in practical tasks such as SSS target detection, large-area topographic surveys, and high-resolution imaging. The source code is available at: https://github.com/Yang-Code984/Sonar-super-resolution-main.