Learning Curvelet-based Directional Dictionaries for Single Image Super Resolution

Learning and reconstruction-based methods are the two main approaches to the solve single image super resolution (SISR) problem. In this paper, to exploit the advantages of both learning based and reconstruction based approaches, we propose a new SISR framework by combining them, which can effectively utilize their benefits. The external directional dictionaries (EDD) are learned from external high quality images. Additionally, we embeded the nonlocal means (NLM) filter and an isotropic total variation (TV) scheme in the reconstruction based method. We suggest a new supervised clustering scheme via curvelet based direction extraction method (CCDE) to learn the external directional dictionaries from candidate patches with sharp edges. Each input patch is coded by all the EDD. Each of the reconstructed patches under different EDD is applied with a weighted penalty to characterize the given input patch. To disclose new details, the local smoothness and nonlocal self-similarity priors are added on the recovered patch by TV scheme and NLM filter. Extensive experimental results validate the effectiveness and robustness of the proposed method comparing with the state-of- the-art algorithms in SISR methods. Our proposed schemes can retrieve more fine structures and obtain superior results than the competing methods with the scaling factors of 2 and 3.