In this talk, I will introduce a novel nonlocal variational technique based on structural similarity (SS) information for image restoration problems. In the literature, patches extracted from images are compared according to their pixel values, and then nonlocal filtering can be employed for image restoration. The disadvantage of this approach is that intensity based patch distance may not be effective in image restoration, especially for images containing texture or structural information. We propose to use SS between image patches to develop nonlocal regularization models. In particular, two types of nonlocal regularizing functions are studied: a SS based nonlocal quadratic function (SS-NLH1) and a SS based nonlocal total variation function (SS-NLTV) for regularization of image restoration problems. Moreover, we employ iterative algorithms to solve these SS-NLH1 and SS-NLTV variational models numerically, and discuss the convergence of these algorithms. Experimental results are presented to demonstrate the effectiveness of the proposed models.