Research on Sonar Image Denoising Based on Optimized Spatial Pixel Ranking Algorithm

Spatial separation-based methods are applied in the field of sonar image denoising due to their sufficient performance. We analyzed the types and causes of noise in sonar images. Building upon the principles of classical spatial image denoising theory, we propose an algorithm suitable for processing salt and pepper noise. It is called the optimized Spatial Pixel Ranking (OSPR) Algorithm. It decomposes and clusters the pixels of the image from the spatial, and then sorts the image according to the pixel value. The same values will be screened and fused. Finally, the sorted and filtered values are used for image reconstruction. To evaluate its denoising performance, we compare the OSPR algorithm with six classic image denoising algorithms. Through simulation tests on images with varying noise ratios, the OSPR algorithm demonstrates an average improvement range of 1.36-6.32dB in terms of peak signal-to-noise ratio (PSNR). Simultaneously, the average structural similarity index (SSIM) improvement ranges from 0.061-0.224. These results indicate that the algorithm effectively removes noise from sonar images while preserving important edge information. Furthermore, the algorithm will be more applied in target recognition and marine data map construction.