A Superior Sonar Image Reconstruction Technique Based on Enhanced Real-ESRGAN

The quality of sonar images is crucial for underwater perception and interpretation. However, due to the low resolution, weak separability, and strong interference characteristics of sonar data, improving the quality of sonar images has always been a highly challenging task. Existing super-resolution technologies, such as Real-ESRGAN, although they show certain effects in image reconstruction, still fail to meet the strict requirements for sonar image processing. They often lead to artifacts and over-smoothing in the reconstructed images, which degrade image fidelity and limit further analysis. Aiming at the limitations of Real-ESRGAN in reconstructing underwater sonar images, this letter proposes a sonar image reconstruction method based on enhanced Real-ESRGAN. During the sonar image reconstruction process, this method employs a noise reduction treatment that combines deep learning-based non-local means filtering with adaptive wavelet transformation. This effectively reduces artifacts caused by aliasing effects. Moreover, the CoordGate module is introduced into the backbone network Residual in Residual Dense Block Network (RRDBNet) of Real-ESRGAN, significantly enhancing the model's feature modulation capability. Experimental results indicate that this method performs excellently in sonar image reconstruction, particularly in terms of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM), producing visually sharper and structurally more consistent results compared to the baseline method.