Author

Gang Xue

Date of Award

2009

Degree Type

Thesis

Degree Name

Master of Applied Science (MASc)

Department

Electrical and Computer Engineering

Supervisor

Xiaolin Wu

Co-Supervisor

Sorina Dumitrescu

Language

English

Abstract

This thesis is concerned with the removal or reduction of noises in high resolution
video sequences. Many video denoising techniques have been published in the past
two decades) with or without motion compensation. They vary in a wide range of
complexity) performance, and implementation cost. Also) many existing video denoisers make simplistic assumptions on noise statistics and motion type, and hence
their performance depends on the validity of the assumed noise and motion models.
To improve the performance and robustness of existing methods) we propose a new
joint spatial-temporal video denoising algorithm that combines multihypothesis interframe motion compensation and directional intra-frame filtering. The algorithm takes into account general compound motions, including both global camera motion and
individual object motion(s). An affine motion model is used to characterize the global
camera movement, whereas a blockwise translational motion model is used to approximate local object motions. Quadtree data structure is used to organize and speed up the computations of block-based motion estimation. Quadtree-structured diamond search is conducted so that a large area can be examined in motion estimation at a low computational cost.
In order to achieve the best possible visual quality we augment motion-compensated
temporal interframe denoising operation by an intra-frame denoising operation of
adaptive directional filtering. The directional filter is designed for the local signal
waveform and noise level, and it has the advantage of effectively suppressing noises
without blurring edges.
The proposed video denoising algorithm is implemented and tested extensively on
high-resolution digital cinema contents. The experimental results demonstrate the
competitive advantages of the new algorithm in both visual quality and processing
throughput.

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