Sigmoid shrinkage for BM3D denoising algorithm

In this work we propose a modified version of the BM3D algorithm recently introduced by Dabov et al. for the denoising of images corrupted by additive white Gaussian noise. The original technique performs a multipoint filtering, where the nonlocal approach is combined with the wavelet shrinkage of a 3D cube composed by similar patches collected by means of block-matching. Our improvement concerns the thresholding of wavelet coefficients, which are subject to a different shrinkage depending on their level of sparsity. The modified algorithm is more robust with respect to block matching errors, especially when noise is high, as proved by experimental results on a large set of natural images.