Super‐resolution reconstruction: using non‐local structure similarity and edge sharpness dictionary. Issue 12 (25th October 2017) - Zhao, Jianwei; Hu, Heping; Zhou, Zhenghua; Cao, Feilong
Abstract : Image super‐resolution (SR) reconstruction, which gains high‐pixel and multi‐detail image from single or several low‐pixel images, has attracted increasing interest in recent years. This study proposes a new SR method based on sparse representation, which made good use of the non‐local (NL) structure similarity and edge sharpness dictionary. Firstly, all the training patches are classified into different clusters according to diverse edge sharpness of patches. Secondly, different dictionaries are trained for different training patches in each cluster. Thirdly, the NL structure similarity is added into the constraint of NL structure similarity model, and the suitable dictionary is selected for current patch to achieve the coefficients according to the value of edge sharpness of patch. Finally, the high‐resolution (HR) image is obtained by integrating HR patches obtained by the product of HR dictionaries and coefficients. Moreover, by calculating edge sharpness, the different dictionaries which adapt to patches with different structure are obtained, and the NL similarity is well utilised and more details are added to HR patch. Compared to some classical and common methods, the proposed method possesses better reconstruction effects in numerical and visual aspects.