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A Comparison of Data Transformations in Image Denoising
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The study of signal processing has wide applications, such as in hi-fi audio, television, voice recognition and many other areas. Signals are rarely observed without noise, which obstruct our analysis of signals. Hence, it is of great interest to study the detection, approximation and removal of noise.

 In this thesis we compare two methods for image denoising. The methods are each based on a data transformation. Specifically, Fourier Transform and Singular Value Decomposition are utilized in respective methods and compared on grayscale images. The comparison is based on the visual quality of the resulting image, the maximum peak signal-to-noise ratios attainable for the respective methods and their computational time.

We find that the methods are fairly equal in visual quality. However, the method based on the Fourier transform scores higher in peak signal-to-noise ratio and demands considerably less computational time.

Place, publisher, year, edition, pages
2018.
Keywords [en]
Fourier Analysis, Principal Component Analysis, Singular Value Decomposition, Image Denoising
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-375715OAI: oai:DiVA.org:uu-375715DiVA, id: diva2:1284461
Subject / course
Statistics
Supervisors
Available from: 2019-02-01 Created: 2019-01-31 Last updated: 2019-02-01Bibliographically approved

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Department of Statistics
Probability Theory and Statistics

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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