Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/22387
Title: | Model-based Regularization for Video Super-Resolution |
Authors: | Wang, Huazhong |
Advisor: | Wu, Xiaolin |
Department: | Electrical and Computer Engineering |
Keywords: | Regularization;Video Super-Resolution;digital videos;low-resolution video |
Publication Date: | Apr-2009 |
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. |
URI: | http://hdl.handle.net/11375/22387 |
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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