Resolution is the most important criterion for the clarity of details on an image. Therefore,
high resolution images are required in numerous areas. However, obtaining high resolution
images has an evident technological cost and the value of these costs change with the quality
of used optical systems. Image processing methods are used to obtain high resolution images
with low costs. This kind of image improvement is named as superresolution image
reconstruction.
This thesis focuses on two main titles, one of which is the identification methods of blur
parameters, one of the degradation operators, and the stochastic SR image reconstruction
methods. The performances of different stochastic SR image reconstruction methods and blur
identification methods are shown and compared. Then the identified blur parameters are used
in superresolution algorithms and the results are shown.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/3/12609929/index.pdf |
| Date | 01 September 2008 |
| Creators | Senses, Engin Utku |
| Contributors | Ulusoy, Ilkay |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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