提出采用小波域多尺度信息融合的方法建立数字半色调尺度相关的误差测度函数。利用尺度间小波系数的自相关性融合层间的小波细节系数，采用加权最小平方法建立边缘误差测度函数。应用改进的K-means 聚类法将原图像分割为几个区域，利用各区域方差倒数做为权重，建立区域误差测度函数。应用改进直接二值搜索方法最小化初始图像和半色调图像的误差，得到最优的数字半色调图像。模拟结果表明了提出算法的有效性。

A novel approach to improve the halftoning image quality by a mixture distortion criterion is the combination of a edge weighted least squares depending on the fusion multiscale information and the region weighted least squares depending on the improved K-means clustering method. The multiscale characterization of the original image using the discrete wavelet transform is obtained. The boundary information of the target image is fused by the wavelet coefficients of the correlation between wavelet transform layers, to increase the pixel resolution scale. The inter-scale fusion method to gain fusion coefficient of the fine-scale is applied, which takes into account the detail of the image and approximate information, where the fusion coefficient is referred to as the weighting operator, and to establish the boundary energy function. The improved K-means clustering method is used to segment an image several regions and the new energy function is constructed using the weighted least squares method, which the reciprocal of the variance of the segmented regions are referred to as the weighting operator to establish the region energy function. In the halftone process, each clustering uses the weighted least-squares method through energy minimization via direct binary search algorithm, to gain halftoning image. Simulation results on typical test images further confirm the performance of the new approach.