Image identification and restoration using EM algorithm
| dc.contributor | Graduate Program in Electrical and Electronic Engineering. | |
| dc.contributor.advisor | Anarım, Emin. | |
| dc.contributor.advisor | Istefanopulos, Yorgo. | |
| dc.contributor.author | Yemez, YĆ¼cel. | |
| dc.date.accessioned | 2023-03-16T10:22:07Z | |
| dc.date.available | 2023-03-16T10:22:07Z | |
| dc.date.issued | 1992. | |
| dc.description.abstract | This thesis considers the problem of identification and restoration of images degraded by additive Gaussian white noise. It is assumed that the power of the noise and the statistical properties of the original image are not known a priori. A new approach which reduces the two dimensional problem to a one dimensional problem by using the unitary discrete Fourier transform is introduced. Then, by applying the expectation-maximization (EM) algorithm, the image is restored and the parameters of various types of AR models are identified under noisy conditions. Two different methods are used for restoration, namely, maximum likelihood restoration and Kalman filtering. The simulation results of the presented approach are also included. | |
| dc.format.extent | 30 cm. | |
| dc.format.pages | xiii, 56 leaves; | |
| dc.identifier.other | EE 1992 Y39 | |
| dc.identifier.uri | https://digitalarchive.library.bogazici.edu.tr/handle/123456789/13058 | |
| dc.publisher | Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 1992. | |
| dc.relation | Includes appendices. | |
| dc.relation | Includes appendices. | |
| dc.subject.lcsh | Expectation-maximization algorithms. | |
| dc.subject.lcsh | Image processing. | |
| dc.subject.lcsh | Kalman filtering. | |
| dc.subject.lcsh | Autoregression (Statistics) | |
| dc.title | Image identification and restoration using EM algorithm |
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