PROJECT TITLE :

Multispectral Image Denoising With Optimized Vector Bilateral Filter - 2014

ABSTRACT:

Vector bilateral filtering has been shown to produce smart tradeoff between noise removal and edge degradation when applied to multispectral/hyperspectral image denoising. It has also been demonstrated to produce dynamic vary enhancement of bands that have impaired signal to noise ratios (SNRs). Typical vector bilateral filtering described in the literature does not use parameters satisfying optimality criteria. We tend to introduce an approach for selection of the parameters of a vector bilateral filter through an optimization procedure rather than by ad hoc means that. The approach is based on posing the filtering problem in concert of nonlinear estimation and minimization of the Stein's unbiased risk estimate of this nonlinear estimator. Along the manner, we tend to provide a plausibility argument through an analytical example on why vector bilateral filtering outperforms band-wise 2D bilateral filtering in enhancing SNR. Experimental results show that the optimized vector bilateral filter provides improved denoising performance on multispectral images compared with many different approaches.