DocumentCode
239516
Title
Single image super resolution based on sparse representation and adaptive dictionary selection
Author
Chang-Hong Fu ; Hongli Chen ; Hongbin Zhang ; Yui-Lam Chan
Author_Institution
Sch. of Electron. & Opt. Eng., Nanjing Univ. of Sci. & Technol., Nanjing, China
fYear
2014
fDate
20-23 Aug. 2014
Firstpage
449
Lastpage
453
Abstract
An improved single image super resolution based on patch-wise sparse recovery is proposed in this paper. K-SVD is adopted to train a coupled dictionary. Besides, adaptive selection is proposed among dictionaries with different patch size. Simulation results show that the proposed approach provides good subjective quality and up to 0.4 dB PSNR improvement with significant time reduction.
Keywords
image representation; image resolution; singular value decomposition; K-SVD; PSNR improvement; adaptive dictionary selection; patch size; patch-wise sparse recovery; single image super resolution; sparse representation; time reduction; Dictionaries; Digital signal processing; Image reconstruction; Image resolution; Signal resolution; Training; Vectors; K-svd; adpative dictionary selection; dictionary learning; sparse representation; super resolution;
fLanguage
English
Publisher
ieee
Conference_Titel
Digital Signal Processing (DSP), 2014 19th International Conference on
Conference_Location
Hong Kong
Type
conf
DOI
10.1109/ICDSP.2014.6900704
Filename
6900704
Link To Document