Document Detail

Sparsity constrained regularization for multiframe image restoration.
MedLine Citation:
PMID:  18451929     Owner:  NLM     Status:  PubMed-not-MEDLINE    
In this paper we present a new algorithm for restoring an object from multiple undersampled low-resolution (LR) images that are degraded by optical blur and additive white Gaussian noise. We formulate the multiframe superresolution problem as maximum a posteriori estimation. The prior knowledge that the object is sparse in some domain is incorporated in two ways: first we use the popular l(1) norm as the regularization operator. Second, we model wavelet coefficients of natural objects using generalized Gaussian densities. The model parameters are learned from a set of training objects, and the regularization operator is derived from these parameters. We compare the results from our algorithms with an expectation-maximization (EM) algorithm for l(1) norm minimization and also with the linear minimum-mean-squared error (LMMSE) estimator. Using only eight 4 x 4 pixel downsampled LR images the reconstruction errors of object estimates obtained from our algorithm are 5.5% smaller than by the EM method and 14.3% smaller than by the LMMSE method.
Premchandra M Shankar; Mark A Neifeld
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Journal of the Optical Society of America. A, Optics, image science, and vision     Volume:  25     ISSN:  1084-7529     ISO Abbreviation:  J Opt Soc Am A Opt Image Sci Vis     Publication Date:  2008 May 
Date Detail:
Created Date:  2008-05-02     Completed Date:  2008-07-22     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9800943     Medline TA:  J Opt Soc Am A Opt Image Sci Vis     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1199-214     Citation Subset:  -    
Department of Electrical and Computer Engineering, Optical Sciences Center, The University of Arizona, Tucson, AZ 85721, USA.
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