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.
基于图像离散余弦变换(DCT)交流系数的拉普拉斯分布模型，提出了一种盲提取数字图像信息隐藏算法。
The ac coefficients of an image's Discrete Cosine Transform (DCT) are best approximated by Laplacian distribution model. Based on this model, the blind information hiding algorithm was proposed.
检验结果表明，广义高斯模型比拉普拉斯模型更准确地反映了DCT交流系数的分布。
The results of the tests show that the generalized Gaussian model is better than the Laplacian model in portraying the distribution of the AC DCT coefficients.
当介质的导热系数是温度的函数时，热传导方程是非线性偏微分方程，作者采用基尔霍夫变换把它变成拉普拉斯方程，于是可以找到原问题的近似解析解。
The nonlinear equation of heat conduction is transformed into a Laplace's equation by applying the Kirchhoff transformation, and an analytic approximate solution of the equation is derived.
当介质的导热系数是温度的函数时，热传导方程是非线性偏微分方程，作者采用基尔霍夫变换把它变成拉普拉斯方程，于是可以找到原问题的近似解析解。
The nonlinear equation of heat conduction is transformed into a Laplace's equation by applying the Kirchhoff transformation, and an analytic approximate solution of the equation is derived.
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