Traditional image denoising methods are difficult to effectively retain detailed features while filtering the speckle noise of sonar images. To overcome this problem, an image denoising method based on density clustering and grayscale transformation in a non-subsampled shearlet domain is proposed. Non-subsampled shearlet transform is used to decompose the noisy image into high-frequency coefficients and low-frequency coefficients. According to the distribution characteristics of speckle noise in the sonar image, the Density-based Spatial Clustering of Applica? tions with Noise(DBSCAN) algorithm is used to process high-frequency coefficients to separate noise interference and retain detailed information. Gray-scale transformation is performed on the low-frequency coefficients to enhance image contrast. Finally, the processed high-frequency coefficients and low-frequency coefficients are reconstructed by non-subsampled shearlet inverse transformation to achieve image denoising. The experimental results show that the method is effective in improving the image’s mean square error, peak signal-to-noise ratio, structural similarity, and so on. After denoising, the visual effect and edge retention ability of the image are greatly improved. With the gradual increase of the noise variance, the superiority of this method is further manifested, and it is suitable for the denoising of sonar images with high-density noise.