PMID- 18296219
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20121002
LR  - 20080225
IS  - 1057-7149 (Print)
IS  - 1057-7149 (Linking)
VI  - 2
IP  - 3
DP  - 1993
TI  - A generalized Gaussian image model for edge-preserving MAP estimation.
PG  - 296-310
AB  - 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.
FAU - Bouman, C
AU  - Bouman C
AD  - Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN.
FAU - Sauer, K
AU  - Sauer K
LA  - eng
PT  - Journal Article
PL  - United States
TA  - IEEE Trans Image Process
JT  - IEEE transactions on image processing : a publication of the IEEE Signal 
      Processing Society
JID - 9886191
EDAT- 1993/01/01 00:00
MHDA- 1993/01/01 00:01
CRDT- 1993/01/01 00:00
PHST- 1993/01/01 00:00 [pubmed]
PHST- 1993/01/01 00:01 [medline]
PHST- 1993/01/01 00:00 [entrez]
AID - 10.1109/83.236536 [doi]
PST - ppublish
SO  - IEEE Trans Image Process. 1993;2(3):296-310. doi: 10.1109/83.236536.