正则化图像恢复是条件约束的最优化问题，而小波系数的贝叶斯统计选择是基于图像的随机场观点。
A regularized image restoration is the optimization for some conditional constraint, and the selection of wavelet coefficients based Bayesian statistic is on the image random field view.
基于贝叶斯证据框架下的最小二乘小波支持向量机，设计了一种新型模拟电路故障诊断方法。
Based on least squares wavelet support vector machines (LS-WSVM) within the Bayesian evidence framework, a systematic method for fault diagnosis of analog circuits was proposed.
图像的小波系数具有很强的非高斯统计特性，可以建立推广的拉普拉斯先验分布，用贝叶斯估计对图像小波系数滤波来达到降噪目的。
The wavelet subband coefficients of images have highly non Gaussian statistics that may be modeled with generalized Laplacian distributions, and Bayesian estimation is used to suppress noise.
通过比较傅里叶分析和小波分析中能量守恒公式，提出了小波能量谱的概念。
The conception of wavelet energy spectrum (WES) is posed by comparing the conservation of total energy in harmonic analysis, power spectrum analysis and wavelet transform.
在此基于小波域隐马尔可夫树模型，将贝叶斯估计和同态滤波思想有机结合，提出一种新的医学超声图像去噪方法。
Based on wavelet domain Hidden Markov model, a novel speckle suppression method for medical ultrasound images is presented which combines Bayesian estimation and homomorphic filtering.
与傅里叶分析中使用的无限重复的正弦波不同，小波常常是不规则的和非对称的，随着离中心点越来越远，其数值逐渐靠近零。
Unlike the sinusoidal, endlessly repeating waves used in Fourier analysis, wavelets are often irregular and asymmetric, with values that die out to zero as they move farther from a central point.
与傅里叶分析中使用的无限重复的正弦波不同，小波常常是不规则的和非对称的，随着离中心点越来越远，其数值逐渐靠近零。
Unlike the sinusoidal, endlessly repeating waves used in Fourier analysis, wavelets are often irregular and asymmetric, with values that die out to zero as they move farther from a central point.
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