In this paper, we present a general framework for denoising of images corrupted with additive white Gaussian noise based on the idea of regional similarity. The proposed method adds an additional similarity function to the bilateral filtering framework. The new similarity function is based on 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. The proposed framework is then extended into a multiresolution setting using wavelets and scale space. It is shown that the resulting multiresolution multilateral (MRM) filtering algorithm not only eliminates coarse-grain noise but can also faithfully reconstruct anisotropic oscillatory patterns, particularly in the presence of high levels of noise.