An estimation-theoretic approach to video denoising
Venue
2015 IEEE International Conference on Image Processing, IEEE, pp. 4273-4277
Publication Year
2015
Authors
Jingning Han, Timothy Kopp, Yaowu Xu
BibTeX
Abstract
A novel denoising scheme is proposed to fully exploit the spatio-temporal
correlations of the video signal for efficient enhancement. Unlike conventional
pixel domain approaches that directly connect motion compensated reference pixels
and spatially neighboring pixels to build statistical models for noise filtering,
this work first removes spatial correlations by applying transformations to both
pixel blocks and performs estimation in the frequency domain. It is premised on the
realization that the precise nature of temporal dependencies, which is entirely
masked in the pixel domain by the statistics of the dominant low frequency
components, emerges after signal decomposition and varies considerably across the
spectrum. We derive an optimal non-linear estimator that accounts for both motion
compensated reference and the noisy observations to resemble the original video
signal per transform coefficient. It departs from other transform domain approaches
that employ linear filters over a sizable reference set to reduce the uncertainty
due to the random noise term. Instead it jointly exploits this precise statistical
property appeared in the transform domain and the noise probability model in an
estimation-theoretic framework that works on a compact support region. Experimental
results provide evidence for substantial denoising performance improvement.
