NONLINEAR MULTIVARIATE IMAGE FILTERING TECHNIQUES

In this paper, nonlinear multivariate image filtering techniques are p roposed to handle color images corrupted by noise, First, we briefly r eview the principle of reduced ordering (R-ordering) and then define t hree R-orderings by selecting different central locations, Considering noise attenuation, edge preservation, and detail retention, R-orderin g based multivariate filters are designed by combining the R-ordering schemes, To implement color image filtering more effectively, we devel op them into a locally adaptive version, The output of the adaptive fi lter is the closest sample to a central location that is a weighted li near combination of the mean, the marginal median, and the center samp le, As a result, we study an adaptive hybrid multivariate (AHM) filter consisting of the mean filter, the marginal median filter, and the id entity filter, The performance of the two adaptive filtering technique s is compared with that of some nonadaptive ones, The examples of colo r image filtering show that the adaptive multivariate image filtering gives a rather good performance improvement.