The proposed method is composed of two main and sequential operations. The first one is a non-parametric patch sampling method used to fill-in missing regions. Rather than filling in missing regions at the original resolution, the inpainting algorithm is applied on a coarse version of the input picture. There are several reasons for performing the inpainting on a low-resolution image. First, the coarse version of the input picture could be compared to a gist representing dominant and important structures. The second operation is run on the output of the first step. Its goal is to enhance the resolution and the subjective quality of the inpainted areas. We use a single-image SR approach. Given a low-resolution input image, which is the result of the first inpainting step, we recover its high-resolution using a set of training examples, which are taken from the known part of the input picture. This new method is generic since there is no constraint on both the number and the type of inpainting methods used in the first pass. The better the inpainting of low-resolution images, the better the final result should be. Regarding the number of methods, one could imagine using different settings (patch size, search windows etc) or methods to fill-in the low-resolution images and to fuse results. a new inpainting framework which combines non-parametric patch sampling method with a super-resolution method. We first propose an extension of a well-known examplar-based method (improvements are sparsity-based priority, K-coherence candidates and a similarity metric adapted from and compare it to existing methods. Then, a super-resolution method is used to recover a high resolution version. This framework is interesting for different reasons. The proposed method thus builds upon earlier work on examplar-based inpainting in particular on the approach proposed , as well as upon earlier work on single-image examplar-based super-resolution.The quality of the low-resolution inpainted image has a critical impact on the quality at the final resolution, the inpainting algorithm is first improved by considering both a linear combination of K most similar patches (K-NN) to the input patch rather than using simply the best match by template matching and K-coherence candidates as proposed.