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12/05/02
MIT/HP Alliance Colloquium: "Universal Discrete Denoising for a Known Channel"
Dr. Tsachy Weissman, Stanford University, 4-5pm, 33-206

Abstract:
The problem of discrete denoising arises in various scenarios including channel decoding, hidden Markov model state estimation, image enhancement, biomolecular sequence analysis, text correction, and many more. We propose a discrete denoising algorithm, that, based on the observation of the output of a known Discrete Memoryless Channel (DMC), estimates the input sequence to minimize a given fidelity criterion. The algorithm is universal in the sense that it requires no knowledge of the input sequence or its statistical properties. Yet, asymptotically it performs as well as the optimum distribution-dependent scheme. The proposed denoising algorithm is practical, and can be implemented in O(n log n) time and linear storage complexity.

Based on joint work with Erik Ordentlich, Gadiel Seroussi, Sergio Verdu, and Marcelo Weinberger.






           
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