In chapter 6 we research on a denoising technique through shrinking the wavelet coefficients adaptively, then fuzzy membership in Fuzzy Mathematics is introduced to put forward wavelet fuzzy SNR to evaluate quality of SAR images based on it. 第六章研究了小波系数自适应收缩算法抑制斑点噪声,在此基础上引入模糊数学中隶属度的概念,提出了一种新的评估SAR图像质量的指标一小波模糊信噪比。
Then, we summarized the laser ultrasound optical detection method, including the non-interference detection and interference detection. Secondly, aiming at the de-nosing in laser ultrasonic nondestructive testing, we put forward wavelet transform to analyze and process the ultrasound information. 然后,概括了激光超声的常用光学检测方法,包括非干涉检测和干涉检测。其次,针对激光超声无损检测中的降噪问题,提出采用小波变换方法进行超声信息的分析处理。
At the same time, in order to combine the two mean's advantages and improve the prediction performance, author puts forward wavelet Grey model ( WGM ) and present corresponding theory testification and analyzed example. 同时,根据小波分析和灰色系统理论各自的优点,提出了小波GM预测模型概念,给出了其理论证明与实例分析。
A method of forward wavelet transform and inverse wavelet transform for medical ultrasound images is introduced, which is based on the components of 2D image, and after forward wavelet transformed, 2D Grouping Huffman Coding is used. 介绍了一种医学超声图像小波分解和重构方法,即根据二维图像的结构,在小波分解后采用二维分组Huffman编码算法。
Based on the wavelet high frequency coefficient, we bring forward a wavelet part thresholding method. 通过分析图像的小波分解高频系数特性,提出了一种小波局部阈值消噪方法。