Wavelet Video Denoising with Regularized Multiresolution Motion Estimation
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Wavelet Video Denoising with Regularized Multiresolution Motion Estimation Fu Jin, Paul Fieguth, and Lowell Winger Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada N2L 3G1 Received 1 September 2004; Revised 23 June 2005; Accepted 30 June 2005 This paper develops a new approach to video denoising, in which motion estimation/compensation, temporal filtering, and spatial smoothing are all undertaken in the wavelet domain. The key to making this possible is the use of a shift-invariant, overcomplete wavelet transform, which allows motion between image frames to be manifested as an equivalent motion of coefficients in the wavelet domain. Our focus is on minimizing spatial blurring, restricting to temporal filtering when motion estimates are reliable, and spatially shrinking only insignificant coefficients when the motion is unreliable. Tests on standard video sequences show that our results yield comparable PSNR to the state of the art in the literature, but with considerably improved preservation of fine spatial details. Copyright © 2006 Fu Jin et al . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
With the maturity of digital video capturing devices and broadband transmission networks, many video applications have been emerging, such as teleconferencing, remote surveillance, multimedia services, and digital television. However, the video signal is almost always corrupted by noise from the capturing devices or during transmission due to random thermal or other electronic noises. Usually, noise reduction can considerably improve visual quality and facilitate the subsequent processing tasks, such as video compression. There are many existing video denoising approaches in the spatial domain [1–4], which can roughly be divided into two or three classes. Temporal-only An approach utilizes only the temporal correlations [1], neglecting spatial information. Since video signals are strongly correlated along motion trajectories, motion estimation/compensation is normally employed. In those cases where motion estimation is not accurate, motion detection [1, 5] may be used to avoid blurring. These techniques can preserve spatial details well, but the resulting images usually still contain removable noise since spatial correlations are neglected.
Spatio-temporal More sophisticated methods exploit both spatial and temporal correlations, such as simple adaptive weighted local averaging [6], 3D order-statistic algorithms [2], 3D Kalman filtering [3], and 3D Markov models [7]. However, due to the high structural complexity of natural image sequences, accurate modeling remains an open research problem. Spatial-only, a third alternative, would apply 2D spatial denoising to each video frame, taking advantage of the vast image denoising literature. Work in this direction shows limited success, howev
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