Due to the inherent characteristics of sonar imaging principles and the interference of complex underwater environments， underwater sonar images generally suffer from insufficient resolution and missing target details. To address these issues， a high-quality sonar image generation method based on multi-scale feature fusion was proposed. Firstly， the Residual Dense Blocks （RDBs） were used to extract image features at shallow level， thereby capturing basic texture and contour information， and establishing spatial layout of the image. Secondly， a Multi-Scale Attention feature extraction module （MSA） was designed to focus on key features at different scales adaptively and further enhance the expression of key features while suppressing redundant information expression through the attention mechanism. Finally， a discriminator network was constructed using a pixel-by-pixel discrimination strategy based on spectral normalization， which improved the reconstruction ability of complex object contours and details. Experimental results on an underwater sonar image dataset show that the proposed method achieves relative improvements of 6.7% and 5.4%， respectively， in Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM） metrics compared to the existing representative method ESRGAN （Enhanced Super-Resolution Generative Adversarial Network）. It can be seen that the proposed method improves the generation performance on underwater sonar image dataset effectively.