MAP and LS estimation of abrupt changes in multiplicative noise using dynamic programming

This paper addresses the problem of estimating abrupt changes corrupted by multiplicative noise. Two estimators are considered depending on the noise assumptions: the Maximum A Posteriori (MAP) estimator and the Least-Squares (LS) estimator. Both estimators are computed using dynamic programming. This study is then applied to edge detection in SAR images.