基于小波的超声斑纹噪声抑制与对比度增强  

Denoising and contrast enhancement of ultrasound speckle image based on wavelet

作　　者：周秦武[1] 卞正中[1] 张大龙[1] 

机构地区：[1]西安交通大学生命科学与技术学院,西安710049

出　　处：《声学学报》2003年第4期381-384,共4页Acta Acustica

基　　金：国家自然科学基金(60071029)

摘　　要：提出了一种基于小波变换的降低超声图像斑纹噪声,同时完成对比度增强的非线性处理新方法。斑纹噪声属于乘性噪声,是造成超声图像质量退化的主要原因,采用Jain提出的斑纹噪声模型,完成对数化处理后的超声图像的小波变换,然后在最细分辨级上完成小波变换系数的软阈值方法处理,而在中间分辨级上采用硬阈值方法处理,并采用GAG特性曲线对图像细节特征进行增强。算法在预处理阶段还采用了平滑滤波器对最粗分辨级的平滑小波系数进行滤波,以减少脉冲能量对处理结果的干扰。对多幅超声图象的实验结果显示,相对于现有的去噪方法,该方法可以同时实现降噪与局部特征增强的两重目的,具有更佳的适用性。This paper present a new nonlinear algorithm for speckle reduction and contrast enhancement of ultrasound image based on wavelet. Speckle is multiplicative noise and the main reason to cause ultrasonic image degenerate. We adopt Jain's speckle noise model to carry out our scheme. Shrinkage of wavelet coefficients via soft threholding within finer levels of scale is carried out on coefficients of logarithmically transformed ultrasonic image. Enhancement of image features is accomplished via nonlinear stretching according to GAG feature curve followed by hard thresholding of wavelet coefficients within midrange levels of scales. A spatially weighted averaging operation is done to smooth wavelet approximation coefficients at first level in order to avoid effect of pulse energy. The results of our some experiments show that this algorithm produced superior results of denoising and enhancement at the same time when compared to results obtained from existing denoising methods alone.

关 键 词：小波变换 超声图像 噪声抑制 斑纹噪声 噪声抑制 对比度增强 图像处理 数化处理 

分 类 号：O426.9[理学—声学]