Blur Estimation and Superresolution from Multiple Registered Images

Date

2009-01-01

Author

Senses, Engin Utku
Ulusoy, İlkay

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In this study, a superresolution method using registered, noisy and down sampled images is presented. Maximum a Posteriori (MAP) method, one of the statistical pixel domain approaches, is used as the superresolution algorithm. Performances of different data fidelity terms and regularization terms used in the literature are shown. In most of the applications the effects that degrade the image frame are assumed to be known completely or known limited. In this application, the performances of the several methods used to find the amount of blur caused by the unfocussed camera lenses are shown and the best method results are used in the superrresolution algorithm. In this way, the error value of superresolved image is decreased.

URI

https://hdl.handle.net/11511/95140

DOI

https://doi.org/10.1109/siu.2009.5136482

Conference Name

IEEE 17th Signal Processing and Communications Applications Conference

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

E. U. Senses and İ. Ulusoy, “Blur Estimation and Superresolution from Multiple Registered Images,” presented at the IEEE 17th Signal Processing and Communications Applications Conference, Antalya, Türkiye, 2009, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/95140.