Share Email Print
cover

Proceedings Paper

Sparse principle component analysis for single image super-resolution
Author(s): Qianying Zhang; Jitao Wu
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In this paper, we propose a novel image super-resolution method based on sparse principle component analysis. Various coupled sub-dictionaries are trained to represent high-resolution and low-resolution image patches. The proposed method simultaneously exploits the incoherence of the sub-dictionaries and nonlocal self-similarity existing in natural images. The purpose of introducing these two regularization terms is to design a novel dictionary learning algorithm for having good reconstruction. Furthermore, in the dictionary learning process, the algorithm can update the dictionary as a whole and reduce the computational cost significantly. Experimental results show the efficiency of the proposed method compared to the existing algorithms in terms of both PSNR and visual perception.

Paper Details

Date Published: 4 March 2015
PDF: 5 pages
Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 94430Y (4 March 2015); doi: 10.1117/12.2178753
Show Author Affiliations
Qianying Zhang, Beihang Univ. (China)
Jitao Wu, Beihang Univ. (China)


Published in SPIE Proceedings Vol. 9443:
Sixth International Conference on Graphic and Image Processing (ICGIP 2014)
Yulin Wang; Xudong Jiang; David Zhang, Editor(s)

© SPIE. Terms of Use
Back to Top