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Denoising of Infrared Images Using Independent Component Analysis
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
2005 (English)Independent thesis Basic level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [sv]

Denna uppsats syftar till att undersöka användbarheten av metoden Independent Component Analysis (ICA) för brusreducering av bilder tagna av infraröda kameror. Speciellt fokus ligger på att reducera additivt brus. Bruset delas upp i två delar, det Gaussiska bruset samt det sensorspecifika mönsterbruset. För att reducera det Gaussiska bruset används en populär metod kallad sparse code shrinkage som bygger på ICA. En ny metod, även den byggandes på ICA, utvecklas för att reducera mönsterbrus. För varje sensor utförs, i den nya metoden, en analys av bilddata för att manuellt identifiera typiska mönsterbruskomponenter. Dessa komponenter används därefter för att reducera mönsterbruset i bilder tagna av den aktuella sensorn. Det visas att metoderna ger goda resultat på infraröda bilder. Algoritmerna testas både på syntetiska såväl som på verkliga bilder och resultat presenteras och jämförs med andra algoritmer.

Abstract [en]

The purpose of this thesis is to evaluate the applicability of the method Independent Component Analysis (ICA) for noise reduction of infrared images. The focus lies on reducing the additive uncorrelated noise and the sensor specific additive Fixed Pattern Noise (FPN). The well known method sparse code shrinkage, in combination with ICA, is applied to reduce the uncorrelated noise degrading infrared images. The result is compared to an adaptive Wiener filter. A novel method, also based on ICA, for reducing FPN is developed. An independent component analysis is made on images from an infrared sensor and typical fixed pattern noise components are manually identified. The identified components are used to fast and effectively reduce the FPN in images taken by the specific sensor. It is shown that both the FPN reduction algorithm and the sparse code shrinkage method work well for infrared images. The algorithms are tested on synthetic as well as on real images and the performance is measured.

Place, publisher, year, edition, pages
Institutionen för systemteknik , 2005. , 53 p.
Keyword [en]
Fixed Pattern Noise, Infrared sensors, Noise reduction, Independent Component Analysis, Sparse Code Shrinkage
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-4954ISRN: LiTH-ISY-EX--05/3726--SEOAI: oai:DiVA.org:liu-4954DiVA: diva2:20831
Subject / course
Computer Vision Laboratory
Uppsok
Technology
Supervisors
Examiners
Available from: 2005-11-28 Created: 2005-11-28 Last updated: 2012-07-02

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • text
  • asciidoc
  • rtf