Superresolution with Nonlocal Regularized Sparse Representation The reconstruction of a high resolution (HR) image from its low resolution (LR) counterpart is a challenging problem. The recently developed sparse representation (SR) techniques provide new solutions to this inverse problem by introducing the l 1 norm sparsity prior into the superresolution reconstruction process. In this paper, we present a new SR based image superresolution by optimizing the objective function under an adaptive sparse domain and with the nonlocal regularization of the HR images. The adaptive sparse domain is estimated by applying principal component analysis to the grouped nonlocal similar image patches. The proposed objective function with nonlocal regularization can be efficiently solved by an iterative shrinkage algorithm. The experiments on natural images show that the proposed method can reconstruct HR images with sharp edges from degraded LR images.

Image superresolution with sparse representation prior on primitive patches We focus on the problem of single image superresolution in this paper. Given a lowresolution image, we seek to synthesize its underlying highresolution details using a learning based method. Inspired by recent progress in compressive sensing, we use sparse representation prior to regularize this illposed problem. On the other hand, with natural image statistics taken into consideration, we enforce the prior only on those image patches associated with image primitives rather than on arbitrary ones. Specifically, each patch from primitive layer of the lowresolution image, which can be viewed as a lowdimensional projection of a highresolution primitive patch, is conjectured to have a sparse representation concerning an overcomplete dictionary. Under mild conditions, the sparse representation can be correctly restored from the lowdimensional projection according to the theory of compressive sensing. We also construct a dictionary using image primitive patches which works well on generic input images. Experiment results show the efficiency of our method by outperforming other learningbased methods both subjectively and objectively.