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Application of Particle Filtering to Image Enhancement

In this report we propose a novel assumption-free on the noise model technique based on random walks for image enhancement. Our method explores multiple neighbors sets (or hypotheses) that can be used for pixel denoising, through a particle filtering approach. This approach associates weights for each hypotheses according to its relevance and its contribution in the denoising process. Toward accounting for the image structure, we introduce perturbations based on local statistical properties of the image. In other words, particle evolution are controlled by the image structure leading to a filtering window adapted to image contents. Promising experimental results and comparison with the state-of-the-art methods demonstrate the potential of such an approach