TY - JOUR ID - 4369 TI - Learning Curvelet-based Directional Dictionaries for Single Image Super Resolution JO - AUT Journal of Electrical Engineering JA - EEJ LA - en SN - 2588-2910 AU - Mikaeli, Elhameh AU - Aghagolzadeh, Ali AU - Nooshyar, Mehdi AD - Faculty of Engineering, mohaghegh ardabili University of Technology, ardabill, Iran. AD - Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology,Babol, Iran AD - Faculty of Engineering, Mohaghegh Ardabili University, Ardabil, Iran Y1 - 2021 PY - 2021 VL - 53 IS - 2 SP - 249 EP - 260 KW - single image super resolution KW - spare representation KW - directional features KW - local smoothness KW - nonlocal self-similarity DO - 10.22060/eej.2021.19611.5403 N2 - 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. UR - https://eej.aut.ac.ir/article_4369.html L1 - https://eej.aut.ac.ir/article_4369_58a69cab98af9e5f60672db28842e1c9.pdf ER -