A multiresolution framework for local similarity based image denoising

In this paper, we present a generic framework for denoising of images corrupted with additive white Gaussian noise based on the idea of regional similarity. The proposed framework employs a similarity function using the distance between pixels in a multidimensional feature space, whereby multiple feature maps describing various local regional characteristics can be utilized, giving higher weight to pixels having similar regional characteristics. An extension of the proposed framework into a multiresolution setting using wavelets and scale space is presented. It is shown that the resulting multiresolution multilateral (MRM) filtering algorithm not only eliminates the coarse-grain noise but can also faithfully reconstruct anisotropic features, particularly in the presence of high levels of noise.

► Framework for denoising images corrupted with additive white Gaussian noise.
► The iterative multilateral filtering framework is an extension of bilateral filter.
► Uses similarity function involving distance in a multidimensional feature space.
► Extension in multiresolution (MRM filter) using wavelets and scale space.
► The MRM filter is particularly effective in the presence of high levels of noise.