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http://hdl.handle.net/11375/22387
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DC Field | Value | Language |
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dc.contributor.advisor | Wu, Xiaolin | - |
dc.contributor.author | Wang, Huazhong | - |
dc.date.accessioned | 2017-11-07T17:10:45Z | - |
dc.date.available | 2017-11-07T17:10:45Z | - |
dc.date.issued | 2009-04 | - |
dc.identifier.uri | http://hdl.handle.net/11375/22387 | - |
dc.description.abstract | In this thesis, we reexamine the classical problem of video super-resolution, with an aim to reproduce fine edge/texture details of acquired digital videos. In general, the video super-resolution reconstruction is an ill-posed inverse problem, because of an insufficient number of observations from registered low-resolution video frames. To stabilize the problem and make its solution more accurate, we develop two video super-resolution techniques: 1) a 2D autoregressive modeling and interpolation technique for video super-resolution reconstruction, with model parameters estimated from multiple registered low-resolution frames; 2) the use of image model as a regularization term to improve the performance of the traditional video super-resolution algorithm. We further investigate the interactions of various unknown variables involved in video super-resolution reconstruction, including motion parameters, high-resolution pixel intensities and the parameters of the image model used for regularization. We succeed in developing a joint estimation technique that infers these unknowns simultaneously to achieve statistical consistency among them. | en_US |
dc.language.iso | en | en_US |
dc.subject | Regularization | en_US |
dc.subject | Video Super-Resolution | en_US |
dc.subject | digital videos | en_US |
dc.subject | low-resolution video | en_US |
dc.title | Model-based Regularization for Video Super-Resolution | en_US |
dc.contributor.department | Electrical and Computer Engineering | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Master of Applied Science (MASc) | en_US |
Appears in Collections: | Digitized Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Wang_Huazong_2009Apr_Masters.pdf | 5.85 MB | Adobe PDF | View/Open |
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