Akademik Veri Yönetim Sistemi
26th IEEE Signal Processing and Communications Applications Conference (SIU), İzmir, Türkiye, 2 - 05 Mayıs 2018
Sparse representation has attracted considerable attention in image restoration field recently. In this study, the sparse representation technique which searches for the most robust representation of a signal in terms of atoms in a dictionary is applied to the single image super resolution technique. In the literature, the first and second-order derivatives are always used as features for low resolution image patches. Therefore, a novel single image super resolution based on sparse representation approach with considering the effect of multi-scale and multidirectional features is proposed in our study. 'Set14' dataset is used in order to evaluate the performance of the proposed study and the results are compared with bicubic interpolation and a state-of-art sparse representation based study quantitatively amd qualitatively. Experimental results show the superiority of the proposed method over the other approaches in terms of quantitative and qualitative analysis by removing the blurring and aliasing artifacts.