摘要
针对小波去斑方法在医学超声图像抑斑上的不足，提出一种混合离散小波变换DWT（Discrete Wavelet Transform）和双树复小波变换DTCWT（Dual-tree Complex Wavelet Transform）进行阈值处理和变量收缩的医学超声图像自适应去斑算法。首先，在小波域，根据小波系数能量的特点，计算综合阈值实现图像预处理；然后，结合小波系数的尺度相关性，提出一种改进的三变量收缩函数，实现图像去斑。实验结果表明该方法较已有的经典方法更为有效，一般情况信噪比可提高0.6-2.6dB，图像边缘信息保持能力更突出。
Aiming at the shortage of traditional wavelet-based despeckling methods for medical ultrasound images, we present a novel adaptive despeckling algorithm for medical ultrasound image, which applies the hybrid discrete wavelet transform （DWT） and dual-tree complex wavelet transform （DTCWT） in threshold processing and variable shrinking. First, the synthesis threshold in wavelet domain is calculated according to the feature of wavelet coefficients energy to realise image preprocessing; Then, an improved trivariate shrinkage function is presented in combination with the scale correlation of wavelet coefficients to implement image despeckling. Experimental results demonstrate that our method is more effective than the existing classic methods, which can raise the signal-to-noise ratio （SNR） by 0.6~2.6 dB with better performance of edge preservation in general circumstance.
出处
《计算机应用与软件》
CSCD
北大核心
2014年第10期202-205,233,共5页
Computer Applications and Software
基金
国家自然科学基金项目(61201423
61105010)
武汉市晨光计划项目(201150431095)
湖北省高等学校优秀中青年科技创新团队计划项目(T201202)
湖北省重点实验室开放基金项目(znss 2013B016)
