Quantitative analysis of infrared contrast enhancement algorithms

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dc.contributor.advisor Salvaggio, Carl - Chair en_US
dc.contributor.advisor Helguera, Maria en_US
dc.contributor.advisor Murphy, Robert en_US
dc.contributor.author Weith-Glushko, Seth en_US
dc.date.accessioned 2007-07-05T13:43:16Z en_US
dc.date.available 2007-07-05T13:43:16Z en_US
dc.date.issued 2007-02 en_US
dc.identifier.uri http://hdl.handle.net/1850/4208 en_US
dc.description.abstract This thesis examines a quantitative analysis of infrared contrast enhancement algorithms found in literature and developed by the author. Four algorithms were studied, three of which were found in literature and one developed by the author: tail-less plateau equalization (TPE), adaptive plateau equalization (APE), the method according to Aare Mallo (MEAM), and infrared multi-scale retinex (IMSR). Engineering code was developed for each algorithm. From this engineering code, a rate of growth analysis was conducted to determine each algorithm’s computational load. From the analysis, it was found that all algorithms with the exception of IMSR have a desirable linear nature. Once the rate of growth analysis was complete, sample infrared imagery was collected. Three scenes were collected for experimentation: a low-to-high thermal variation scene, a low-to-mid thermal variation scene, and a natural scene. After collecting sample imagery and processing it with the engineering code, a paired comparison psychophysical trial was executed using local firefighters, common users of the infrared imaging system. From this trial, two metrics were formed: an average rank and an interval scale. From analysis of both metrics plus an analysis of the rate of growth, MEAM was declared to be the best algorithm overall. en_US
dc.language.iso en_US en_US
dc.subject Contrast enhancement en_US
dc.subject Histogram equalization en_US
dc.subject Image processing en_US
dc.subject Infrared en_US
dc.subject Remote sensing en_US
dc.subject Thermal IR en_US
dc.subject.lcc TA1570 .W44 2007
dc.subject.lcsh Infrared imaging--Data processing en_US
dc.subject.lcsh Algorithms--Evaluation en_US
dc.subject.lcsh Imaging systems--Image quality en_US
dc.subject.lcsh Remote sensing--Data processing en_US
dc.title Quantitative analysis of infrared contrast enhancement algorithms en_US
dc.type Thesis en_US

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