<p>Video super-resolution for dual-mode cameras in single-view and mono-view scenarios is studied in this thesis. Dual-mode cameras are capable of generating high-resolution still images while shooting video sequences at low-resolution. High-resolution still images are used to form a regularization function for solving the inverse problem of super-resolution. Exploiting proposed regularization function in this thesis obviates the need for classic regularization function. Experimental results show that using proposed regularization function instead of classic regularization functions for super-resolution of single-view video leads to improved results. In this thesis, super-resolution problem is divided into low-resolution frame fusion and de-blurring. A frame fusion scheme for multi-view video is proposed and performance improvement when exploiting multi-view sequence instead of single-view for frame fusion is studied. Experimental results show that information taken by a set of cameras instead of a single camera can improve super-resolution process, especially when video contains fast motions. As a side work, we applied our low-resolution multi-view frame fusion algorithm to 3D frame-compatible format resolution enhancement. Multi-view video super-resolution using high-resolution still images is performed at the decoder to prevent increasing computation complexity of the encoder. Experimental results show that this method delivers comparable compression efficiency for lower bit-rates.</p> / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/12383 |
| Date | 10 1900 |
| Creators | Najafi, Seyedreza |
| Contributors | Shirani, Shahram, Zhao, D., Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Detected Language | English |
| Type | thesis |
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