首先，通过拉普拉斯金字塔变换将源图像分解为各级分辨率的子图像。
Firstly, the source images are decomposed into sub-images at different scales through Laplacian pyramid transform.
最后利用熵和空间频率对该方法的融合性能进行了评估分析，并与基于拉普拉斯变换和小波变换的图像融合方法进行了比较。
They use entropy and spatial frequency to evaluate the fusion result of their method and perform a comparison with the Laplacian based and the wavelet based image fusion methods.
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.
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