Network flow optimization for restoration of images
Network flow optimization for restoration of images
复制标题
用于图像恢复的网络流量优化
DOI：
10.1155/s1110757x02110035
复制
发表时间：
2001
影响因子：
--
通讯作者：
B. Zalesky
中科院分区：
文献类型：
--
作者：
B. Zalesky
提出了一种基于网络流优化的灰度图像和彩色图像噪声恢复方法。伊辛模型被用作所提出的方法的统计背景。我们提出了新的多分辨率网络流最小割算法，这是特别有效的最大后验概率（MAP）估计损坏的图像识别。该算法能够计算大尺寸图像的最大后验概率估计，并可在并发模式下使用。我们还考虑了两个函数U1（x）=λ∑i的整数极小化问题|益溪|
The network flow optimization approach is offered for restoration of gray-scale and color images corrupted by noise. The Ising models are used as a statistical background of the proposed method. We present the new multiresolution network flow minimum cut algorithm, which is especially efficient in identification of the maximum a posteriori (MAP) estimates of corrupted images. The algorithm is able to compute the MAP estimates of large-size images and can be used in a concurrent mode. We also consider the problem of integer minimization of two functions, U1(x)=λ∑i|yi−xi|