A generalized Gaussian image model for edge-preserving MAP estimation
A generalized Gaussian image model for edge-preserving MAP estimation
复制标题
DOI：
10.1109/83.236536
复制
发表时间：
1993-07-01
影响因子：
10.6
通讯作者：
Sauer, Ken
中科院分区：
文献类型：
--
作者：
Bournan, Charles;Sauer, Ken
We present a Markov random field model which allows realistic edge modeling while providing stable maximum a posteriori MAP solutions. The proposed model, which we refer to as a generalized Gaussian Markov random field (GGMRF), is named for its similarity to the generalized Gaussian distribution used in robust detection and estimation. The model satisifies several desirable analytical and computational properties for MAP estimation, including continuous dependence of the estimate on the data, invariance of the character of solutions to scaling of data, and a solution which lies at the unique global minimum of the a posteriori log-likeihood function. The GGMRF is demonstrated to be useful for image reconstruction in low-dosage transmission tomography.
