Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty
This addresses the challenge of denoising discrete data without prior channel knowledge, which is incremental as it builds on Neural DUDE.
The paper tackles the problem of discrete denoising under channel uncertainty by proposing an iterative channel estimation (ICE) algorithm that removes the need for a known noisy channel, showing that ICE-N-DUDE performs universally well across different data types and significantly outperforms the Baum-Welch baseline.
We propose a novel iterative channel estimation (ICE) algorithm that essentially removes the critical known noisy channel assumption for universal discrete denoising problem. Our algorithm is based on Neural DUDE (N-DUDE), a recently proposed neural network-based discrete denoiser, and it estimates the channel transition matrix as well as the neural network parameters in an alternating manner until convergence. While we do not make any probabilistic assumption on the underlying clean data, our ICE resembles Expectation-Maximization (EM) with variational approximation, and it takes advantage of the property of N-DUDE being locally robust around the true channel. With extensive experiments on several radically different types of data, we show that the ICE equipped N-DUDE (dubbed as ICE-N-DUDE) can perform \emph{universally} well regardless of the uncertainties in both the channel and the clean source. Moreover, we show ICE-N-DUDE becomes extremely robust to its hyperparameters and significantly outperforms the strong baseline that can deal with the channel uncertainties for denoising, the widely used Baum-Welch (BW) algorithm for hidden Markov models (HMM).