Single image super-resolution via low-rank tensor representation and hierarchical dictionary learning

Weili Guan
Weili Guan
Xu Bai
Xu Bai
Hongbin Guo
Hongbin Guo

Multimedia Tools and Applications, pp. 11767-11785, 2020.

Cited by: 2|Bibtex|Views21|DOI:https://doi.org/10.1007/s11042-019-08259-9
EI
Other Links: academic.microsoft.com|dblp.uni-trier.de|link.springer.com

Abstract:

Super-resolution (SR) has been widely studied due to its importance in real applications and scenarios. In this paper, we focus on generating an SR image from a single low-resolution (LR) input image by employing the multi-resolution structures of an input image. By taking the LR image and its downsampled resolution (DR) and upsampled res...More

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