The noises of image is effectively suppressed by restricted Laplacian sharpening algorithm. The edges of image after processing are distinct and the details are deserved.
受限拉氏锐化算法有效地控制了图像的噪声，使处理后的图像边缘更加清晰，又保护了图像的细节。
The experimental results of the advanced Laplacian sharpening algorithm and the common Laplacian sharpening algorithm are compared.
对普通拉氏锐化算法和受限拉氏锐化算法的处理效果进行比较。
The algorithm can measure discontinuity of gray values by using Laplacian and adjust automatically gray values according to intensity of response to gray mutation in Laplacian.
该算法利用拉普拉斯微分算子对像素灰度值的不连续性进行量化，根据微分算子对灰度突变的响应强度进行自动调整掩模内的各像素权值。
To solve this problem we have implemented the texture progressive transmission with the classic Laplacian pyramid algorithm and the Haar transform algorithm.
为解决这个问题用拉普拉斯金字塔算法和哈尔变换算法实现了纹理递进传输。
With the method of classifying the mesh vertices, a hybrid algorithm combined weighted median method and Laplacian method was presented.
应用顶点分类的方法，提出了一种加权均值滤波与拉普拉斯光顺相结合的混合算法。
Experiments results show that the proposed fusion algorithm outperforms other fusion algorithms based on averaging, Laplacian pyramid, and wavelet fusion method using local energy.
实验结果表明本文提出的融合算法的性能优于平均融合算法、基于拉普拉斯塔型分解的融合算法和基于局部能量信息的小波融合算法。
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.
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 principle of image decomposition and reconstruction based on Gauss-pyramid, Laplacian-pyramid, contrast-pyramid and wavelet-pyramid is emphatically analyzed, as well as the fusion algorithm.
重点分析了高斯金字塔、拉普拉斯金字塔、对比度金字塔和小波金字塔在图像分解与重构中的原理及其融合算法。
The conventional complex diffusion algorithm uses the iterative imaginary part(Laplacian of the Gaussian kernel) to control the diffusion process.
图像的复扩散是图像增强的一种有效方法。
Our work enrich the Laplacian Eigenmap algorithm theory system. In addition, a new method of estimation intrinsic dimension is proposed, it shows good proved by man-made data.
对拉普拉斯特征映射方法应用领域的拓展起到了推动作用，同时提出一种新的估计本征维数的方法并且通过人造数据的验证说明该方法具有良好的性质。
Our work enrich the Laplacian Eigenmap algorithm theory system. In addition, a new method of estimation intrinsic dimension is proposed, it shows good proved by man-made data.
对拉普拉斯特征映射方法应用领域的拓展起到了推动作用，同时提出一种新的估计本征维数的方法并且通过人造数据的验证说明该方法具有良好的性质。
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