Title :
A generalized Gaussian image model for edge-preserving MAP estimation
Author :
Bouman, Charles ; Sauer, Ken
Author_Institution :
Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN, USA
fDate :
7/1/1993 12:00:00 AM
Abstract :
The authors present a Markov random field model which allows realistic edge modeling while providing stable maximum a posterior (MAP) solutions. The model, referred 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 satisfies 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-likelihood function. The GGMRF is demonstrated to be useful for image reconstruction in low-dosage transmission tomography
Keywords :
Markov processes; image reconstruction; GGMRF; Markov random field model; a posteriori log-likelihood function; edge modeling; generalized Gaussian Markov random field; generalized Gaussian image model; image reconstruction; low-dosage transmission tomography; map estimation; maximum a posterior; Bayesian methods; Computer vision; Cost function; Gaussian distribution; Image edge detection; Image processing; Image reconstruction; Markov random fields; Robustness; Tomography;
Journal_Title :
Image Processing, IEEE Transactions on