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http://hdl.handle.net/11375/9254
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DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Wu, Xiaolin | en_US |
dc.contributor.author | Zhao, Yong | en_US |
dc.date.accessioned | 2014-06-18T16:46:23Z | - |
dc.date.available | 2014-06-18T16:46:23Z | - |
dc.date.created | 2011-06-01 | en_US |
dc.date.issued | 2010-10 | en_US |
dc.identifier.other | opendissertations/4394 | en_US |
dc.identifier.other | 5415 | en_US |
dc.identifier.other | 2042272 | en_US |
dc.identifier.uri | http://hdl.handle.net/11375/9254 | - |
dc.description.abstract | <p>A novel linear programming approach for optimal contrast-tone mapping is proposed. A measure of contrast gain and a sister measure of tone distortion are defined for gray level transfer functions. These definitions allow us to depart from the current practice of histogram equalization and formulate contrast enhancement as a problem of maximizing contrast gain subject to a limit on tone distortion and possibly other constraints that suppress artifacts. The resulting contrast-tone optimization problem can be solved efficiently by linear programming. The proposed constrained optimization framework for contrast enhancement is general, and the user can add and fine tune the constraints to achieve desired visual effects. Experimental results are presented to illustrate the performance of the proposed method, demonstrating clearly superior performance of the new technique over histogram equalization. In addition, two locally adaptive contrast enhancement techniques by the proposed method are investigated.</p> | en_US |
dc.subject | Electrical and Computer Engineering | en_US |
dc.subject | Electrical and Computer Engineering | en_US |
dc.title | A LINEAR PROGRAMMING APPROACH FOR OPTIMAL CONTRAST-TONE MAPPING | en_US |
dc.type | thesis | en_US |
dc.contributor.department | Electrical and Computer Engineering | en_US |
dc.description.degree | Master of Science (MS) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Size | Format | |
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fulltext.pdf | 2.01 MB | Adobe PDF | View/Open |
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