针对离散余弦变换（DCT）系数分布模型类型存在的异议，研究并比较了拉普拉斯模型和广义高斯模型。
There is no consensus on the distribution of the AC DCT coefficients till now. In this work, the generalized Gaussian model and the Laplacian model, which are used most widely, were studied.
同时利用拉普拉斯密度模型作为特征系数的稀疏惩罚函数，保证了图像结构的稀疏性。
At the same time, this algorithm utilized the Laplace density model as the feature coefficients sparse punitive function to ensure an image's sparse structure.
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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