  Leading denoising methods such as 3D block matching (BM3D) are patch-based.
However, they can suffer from frequency domain artefacts and require to specify
explicit noise models. We present a patch-based method that avoids these
drawbacks. It combines a simple and fast patch reordering with a non-linear
smoothing. The smoothing rewards both patch and pixel similarities in a
multiplicative way. We perform experiments on real world images with additive
white Gaussian noise (AWGN), and on electron microscopy data with a more
general additive noise model. Our filter outperforms BM3D in 77% of the
experiments, with improvements of up to 29% with respect to the mean squared
error.
