针对当前较多图像修复算法主要通过对图像块进行方差和度量的方法来完成图像修复,忽略了图像块的显著边缘特性,使得修复图像容易出现模糊效应以及不连续效应等不良现象,导致算法修复性能不佳的不足,提出了基于曲率约束因子耦合边缘加权...针对当前较多图像修复算法主要通过对图像块进行方差和度量的方法来完成图像修复,忽略了图像块的显著边缘特性,使得修复图像容易出现模糊效应以及不连续效应等不良现象,导致算法修复性能不佳的不足,提出了基于曲率约束因子耦合边缘加权法则的图像修复算法.首先,通过像素点的等照度线方向构造曲率约束因子,对数据项进行约束,形成优先级度量函数,利用优先级度量函数选取优先修补块;然后,利用像素点的均值之差构造像素自相关模型,对样本块的大小进行了调整;最后,以样本块显著边缘为约束,构造了边缘加权模型,通过边缘加权模型联合SSD(sum of squared differences)模型建立了边缘加权法则,对最优匹配块进行搜索,用于对待修补块进行修复.仿真实验结果表明,与当前图像修复算法相比,本文设计的图像修复算法修复的图像具有良好的视觉效果.展开更多
We propose a weighted model to explain the self-organizing formation of scale-free phenomenon in nongrowth random networks. In this model, we use multiple-edges to represent the connections between vertices and define...We propose a weighted model to explain the self-organizing formation of scale-free phenomenon in nongrowth random networks. In this model, we use multiple-edges to represent the connections between vertices and define the weight of a multiple-edge as the total weights of all single-edges within it and the strength of a vertex as the sum of weights for those multiple-edges attached to it. The network evolves according to a vertex strength preferential selection mechanism. During the evolution process, the network always holds its totM number of vertices and its total number of single-edges constantly. We show analytically and numerically that a network will form steady scale-free distributions with our model. The results show that a weighted non-growth random network can evolve into scMe-free state. It is interesting that the network also obtains the character of an exponential edge weight distribution. Namely, coexistence of scale-free distribution and exponential distribution emerges.展开更多
文摘针对当前较多图像修复算法主要通过对图像块进行方差和度量的方法来完成图像修复,忽略了图像块的显著边缘特性,使得修复图像容易出现模糊效应以及不连续效应等不良现象,导致算法修复性能不佳的不足,提出了基于曲率约束因子耦合边缘加权法则的图像修复算法.首先,通过像素点的等照度线方向构造曲率约束因子,对数据项进行约束,形成优先级度量函数,利用优先级度量函数选取优先修补块;然后,利用像素点的均值之差构造像素自相关模型,对样本块的大小进行了调整;最后,以样本块显著边缘为约束,构造了边缘加权模型,通过边缘加权模型联合SSD(sum of squared differences)模型建立了边缘加权法则,对最优匹配块进行搜索,用于对待修补块进行修复.仿真实验结果表明,与当前图像修复算法相比,本文设计的图像修复算法修复的图像具有良好的视觉效果.
基金Supported by the National Natural Science Foundation of China under Grant No.60874080the Commonweal Application Technique Research Project of Zhejiang Province under Grant No.2012C2316the Open Project of State Key Lab of Industrial Control Technology of Zhejiang University under Grant No.ICT1107
文摘We propose a weighted model to explain the self-organizing formation of scale-free phenomenon in nongrowth random networks. In this model, we use multiple-edges to represent the connections between vertices and define the weight of a multiple-edge as the total weights of all single-edges within it and the strength of a vertex as the sum of weights for those multiple-edges attached to it. The network evolves according to a vertex strength preferential selection mechanism. During the evolution process, the network always holds its totM number of vertices and its total number of single-edges constantly. We show analytically and numerically that a network will form steady scale-free distributions with our model. The results show that a weighted non-growth random network can evolve into scMe-free state. It is interesting that the network also obtains the character of an exponential edge weight distribution. Namely, coexistence of scale-free distribution and exponential distribution emerges.
