Document Detail


Image superresolution using support vector regression.
MedLine Citation:
PMID:  17547137     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
A thorough investigation of the application of support vector regression (SVR) to the superresolution problem is conducted through various frameworks. Prior to the study, the SVR problem is enhanced by finding the optimal kernel. This is done by formulating the kernel learning problem in SVR form as a convex optimization problem, specifically a semi-definite programming (SDP) problem. An additional constraint is added to reduce the SDP to a quadratically constrained quadratic programming (QCQP) problem. After this optimization, investigation of the relevancy of SVR to superresolution proceeds with the possibility of using a single and general support vector regression for all image content, and the results are impressive for small training sets. This idea is improved upon by observing structural properties in the discrete cosine transform (DCT) domain to aid in learning the regression. Further improvement involves a combination of classification and SVR-based techniques, extending works in resolution synthesis. This method, termed kernel resolution synthesis, uses specific regressors for isolated image content to describe the domain through a partitioned look of the vector space, thereby yielding good results.
Authors:
Karl S Ni; Truong Q Nguyen
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Publication Detail:
Type:  Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  IEEE transactions on image processing : a publication of the IEEE Signal Processing Society     Volume:  16     ISSN:  1057-7149     ISO Abbreviation:  IEEE Trans Image Process     Publication Date:  2007 Jun 
Date Detail:
Created Date:  2007-06-05     Completed Date:  2007-07-03     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9886191     Medline TA:  IEEE Trans Image Process     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1596-610     Citation Subset:  IM    
Affiliation:
Video Processing Laboratory, Electrical and Computer Engineering Department, University of California, San Diego, CA 92093-0407 USA. ksni@ucsd.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Artificial Intelligence*
Data Interpretation, Statistical
Image Enhancement / methods*
Image Interpretation, Computer-Assisted / methods*
Imaging, Three-Dimensional / methods*
Pattern Recognition, Automated / methods*
Regression Analysis
Reproducibility of Results
Sensitivity and Specificity

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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