Sciweavers

TIT
2008

Scanning and Sequential Decision Making for Multidimensional Data - Part II: The Noisy Case

13 years 3 months ago
Scanning and Sequential Decision Making for Multidimensional Data - Part II: The Noisy Case
We consider the problem of sequential decision making for random fields corrupted by noise. In this scenario, the decision maker observes a noisy version of the data, yet judged with respect to the clean data. In particular, we first consider the problem of scanning and sequentially filtering noisy random fields. In this case, the sequential filter is given the freedom to choose the path over which it traverses the random field (e.g., noisy image or video sequence), thus it is natural to ask what is the best achievable performance and how sensitive this performance is to the choice of the scan. We formally define the problem of scanning and filtering, derive a bound on the best achievable performance, and quantify the excess loss occurring when nonoptimal scanners are used, compared to optimal scanning and filtering. We then discuss the problem of scanning and prediction for noisy random fields. This setting is a natural model for applications such as restoration and coding of noisy im...
Asaf Cohen, Tsachy Weissman, Neri Merhav
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TIT
Authors Asaf Cohen, Tsachy Weissman, Neri Merhav
Comments (0)