Noise reduction for sonar images by statistical analysis and fields of experts

Sonar images are usually suffering from speckle noise which results in poor visual quality. In order to improve the sonar imaging quality, removing or reducing these speckle noises is a very important and arduous task. In this paper, the imaging principle and noise characteristics of the side-scan sonar (SSS) are analyzed, and five typical probability distribution functions are used to fit the seabed reverberation. Through experiment comparison, the Gamma distribution is selected to simulate the noise of the SSS image caused by the reverberation. Simultaneously, the fields of experts denoising algorithm based on the Gamma distribution (Gamma FoE) is proposed for SSS image denoising. In order to perceive and measure the denoising effect better, evaluation indexes of Fast Noise Variance Estimation (FNVE, an image noise estimation method) and Blind Referenceless Image Spatial Quality Evaluator (BRISQUE, an image quality evaluation method) are selected for image quality perception. The final results of the SSS image denoise experiment show that the Gamma FoE denoise algorithm has a better effect on SSS image denoise application than other denoise algorithms.