Abstract/Details

Parametric model-adaptive image restoration


1995 1995

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Abstract (summary)

We have developed two parametric model-adaptive image restoration methods. The first one is the model-adaptive maximum likelihood method based on the generalized Gaussian noise model. Improved performance over many widely used methods is obtained by adapting the optimization criteria to the observed data. The second method is the generalized Gaussian Markov random field (GGMRF) image prior method. Image structure is modeled by adjusting a shape parameter of the GGMRF. The GGMRF model can lead to better results than that of the popular Gaussian Markov random field (GMRF) model. In addition, results from using the GGMRF model are more pleasing to human visual perception. Parameter estimation methods and fast iterative algorithms are also presented to complement the development of these two methods.

Indexing (details)


Subject
Electrical engineering
Classification
0544: Electrical engineering
Identifier / keyword
Applied sciences
Title
Parametric model-adaptive image restoration
Author
Pun, Wai Ho
Number of pages
160
Publication year
1995
Degree date
1995
School code
0022
Source
DAI-B 56/10, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
Advisor
Jeffs, Brian D.
University/institution
Brigham Young University
University location
United States -- Utah
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
9603685
ProQuest document ID
304182709
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/304182709
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