WST:小波软阈值
“小波软阈值”(Wavelet Soft Thresholding,常简写为WST)是一种在信号处理与数据去噪领域广泛使用的技术,其缩写形式便于书写和交流。该方法通过对小波系数进行软阈值处理,在保留信号关键特征的同时有效抑制噪声,尤其适用于图像处理、语音分析及生物医学信号处理等综合应用场景。
Wavelet Soft Thresholding具体释义
Wavelet Soft Thresholding的英文发音
例句
- The quality of regression was improved by combining wavelet soft thresholding with kernel partial least squares ( KPLS ).
- 结合小波软阈值(WST)法和核心偏最小二乘法改进回归质量。
- The quality of noise removal and regression was improved by combining wavelet soft thresholding with radial basis function neural network.
- 结合小波软阈值(WST)法和径向基函数神经网络改进了回归质量。
- The quality of noise removal was improved by combining wavelet soft thresholding with principal component analysis ( PCA ). HYBRID method was used for denoising, after comparison with other soft thresholding method.
- 该法结合小波软阈值(WST)法和主组分分析改进除噪声质量,与其它软阈值法比较选用了HYBRID法;
- Therefore, wavelet soft thresholding method can implement regional competition model faster.
- 因此,利用小波软阈值(WST)法进行区域竞争模型的数值实现效率较高。
- Meanwhile, with the adjustment of the speed factor, the different objects number is obtained, to satisfy various situations and requirements, which improve the application of CV model. Secondly, a numerical method based on wavelet soft thresholding for region competition model was presented.
- 同时,通过调整速度因子的大小,可以得到不同的目标个数,以满足不同场合和应用的需要,提高CV模型的适用范围。二、提出一种基于小波软阈值(WST)的区域竞争模型数值实现方法。
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