we propose and develop a novel nonlocal variational technique based on structural similar-ity (SS) information for image restoration problems. In the literature, patches extracted from images are compared ac-cording to their pixel values, and then non local ltering 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. The main aim of this paper is to propose to use SS between image patches to develop non local regularization models. In particular,two types of non local regularizing functions are studied: aSS based non local quadratic function (SS-NLH1) and a SS based non local total variation function (SS-NLTV) for reg-ularization 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.
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