Image restoration using a hybrid combination of particle filtering and wavelet denoising

In this paper we propose a novel image restoration method that effectively combines a particle filter with wavelet shrinkage to achieve robust performance against inhomogeneous noise mixtures. Specifically, the particle filter acts to suppress outlier-rich components of the noise while, in a subsequent step, the wavelet domain shrinkage attenuates any remaining, less heavily tailed noise components. We present late breaking preliminary examples demonstrating excellent rejection of salt-and-pepper like Cauchy noise mixed with additive white Gaussian noise (AWGN). Although limited in scope, these preliminary results suggest that the combination of particle filters with more traditional restoration techniques is a powerful approach that can provide a new dimension of flexibility for addressing noise mixtures involving difficult nonlinear and non-Gaussian components.