Recent Trends in Discrete Denoising
Prof. Tsachy Weissman
Dept. of EE, Stanford University
The goal of a denoising algorithm is to recover a signal from
its noise-corrupted observations. Perfect recovery is seldom
possible and performance is measured under a given fidelity
criterion. For discrete signals corrupted by discrete memoryless
channels it was recently shown, through the introduction of the
DUDE algorithm, that this task can be performed with no
knowledge of statistical properties of the input signal. The
algorithm is also practical, being implementable in linear time
and sub-linear working storage size.
will describe the algorithm, discuss some of its performance
guarantees and the intuition behind them, present a few
empirical results of its implementation in real-life scenarios,
and show how its variants can be applied to related problems.
I will also briefly mention more recent work triggered by the
discovery of the DUDE, including the finite-input-general-output
scheme, the LZ-based sequential DUDE, the optimality of singlet
decoders, and sample properties of the empirical distribution of
rate distortion codes.
Based on joint works
with Amir Dembo, Erik Ordentlich, Gadiel Seroussi, Sergio Verdu,
and Marcelo Weinberger.