The 11th Iranian and the first International Conference on Machine Vision and Image Processing

Single Image Super Resolution via Curvelet Based Directional Dictionaries

Elhameh Mikaeli, Ali Aghagolzadeh, Masoumeh Azghani
The 11th Iranian and the first International Conference on Machine Vision and Image Processing (MVIP 2020)

Abstract

Learning and reconstruction-based methods are two main approaches to solve the single image super resolution (SISR) problem. In this paper we learn the external directional dictionaries (EDD) from external high quality images. Also, we embed 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 (EED) from candidate patches with sharp edges. Each input patch is coded by all 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, respectively. Extensive experimental results validate the effectiveness and robustness of the proposed methods.

Keywords: Single Image Super-resolution, Spare Representation, Directional Features, Local Smoothness, Nonlocal Self-similarity



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