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.

声纳图像通常会受到斑点噪声的影响，从而导致视觉质量下降。为了提高声纳成像质量，消除或减少这些斑点噪声是非常重要且艰巨的任务。本文分析了侧扫声纳（SSS）的成像原理和噪声特性，并采用了五个典型的概率分布函数来拟合海床混响。通过实验比较，选择Gamma分布来模拟混响引起的SSS图像噪声。同时，提出了基于伽马分布（Gamma FoE）的专家去噪算法领域，用于SSS图像去噪。为了更好地感知和衡量去噪效果，快速噪声方差估计（FNVE，选择图像噪声估计方法）和盲无参考图像空间质量评估器（BRISQUE，图像质量评估方法）以进行图像质量感知。SSS图像降噪实验的最终结果表明，与其他降噪算法相比，Gamma FoE降噪算法对SSS图像降噪应用具有更好的效果。