Multi-Scale EPLL

The work presented in this paper offers a multi-scale extension to the Expected Patch Log Likelihood (EPLL) method by Zoran and Weiss, by forcing the same prior on scaled-down versions of the image to be recovered. This paper motivates the multi-scale approach by first addressing a toy problem of Gaussian signals, for which it is shown how local patch averaging, EPLL and its multi-scale extension, are all approximating the global optimal filtering. Then the multi-scale EPLL is demonstrated for denoising, deblurring, and single-image super-resolution. The following freely available package contains the data and Matlab scripts of all the simulations presented in the above mentioned paper.