基于小波系数相关性和模糊理论的声纳图像处理

针对声纳图像在生成和采集过程中产生大量的混响，提出了一种改进的基于小波系数相关性与模糊理论的声纳图像混响抑制与增强算法.该算法通过在计算过程中考虑下一个尺度中对应部位局部特性，构造局部相关性系数矩阵，提高边界定位精度.在滤波的同时，该算法采用基于模糊理论的增强算法对有用信息进行增强.实验结果表明，与其他常规算法相比，该算法明显降低了声纳图像的平均均方误差（MSE）和平均绝对误差（MAE），提高了峰值信噪比（PSNR），更适合于处理声纳图像.

Acoustic image processing based on correlation of wavelet coefficient and fuzzy theory

Sonar images are usually highly corrupted by reverberations caused by the complex generation mechanism.In order to prepare the image for further segmentation,recognition or other processing,certain suppression and enhancement should be applied in advance.A wavelet domain solution combined with spatial correlation and fuzzy logic method for reverberation suppression and contrast enhancement was proposed.This algorithm adopts a new local correlation coefficient matrix considering local property in the next scale,which increased the ability of edge detection.Meanwhile,a fuzzy theory based algorithm was used to enhance the useful information in sonar images.Experiments show that this method significantly decreases the mean squared error(MSE) and mean absolute error(MAE),while increase the peak signal-to-noise ratio(PSNR).Compared with other methods,this method is suitable for sonar image pre-processing.