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Date of Award

Fall 2012

Degree Type

Thesis

Degree Name

Master of Applied Science (MASc)

Department

Electrical and Computer Engineering

Supervisor

Shahram Shirani

Language

English

Committee Member

D. Zhao

Abstract

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.

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