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Proceedings Paper

Quality enhancement of low-resolution image by using natural images
Author(s): E. Bilgazyev; E. Yeniaras; I. Uyanik; M. Unan; E. L. Leiss
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Paper Abstract

In this paper, we propose a new algorithm to estimate a super-resolution image from a given low-resolution image, by adding high-frequency information that is extracted from natural high-resolution images in the training dataset. The selection of the high-frequency information from the training dataset is accomplished in two steps: a nearest-neighbor search algorithm is used to select the closest images from the training dataset, which can be implemented in the GPU, and a sparse-representation algorithm is used to estimate a weight parameter to combine the high-frequency information of selected images. This simple but very powerful super-resolution algorithm can produce state-of-the-art results. Qualitatively and quantitatively, we demonstrate that the proposed algorithm outperforms existing common practices.

Paper Details

Date Published: 24 December 2013
PDF: 7 pages
Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90671L (24 December 2013); doi: 10.1117/12.2051684
Show Author Affiliations
E. Bilgazyev, Univ. of Houston (United States)
E. Yeniaras, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
I. Uyanik, Univ. of Houston (United States)
M. Unan, Univ. of Houston (United States)
E. L. Leiss, Univ. of Houston (United States)


Published in SPIE Proceedings Vol. 9067:
Sixth International Conference on Machine Vision (ICMV 2013)
Branislav Vuksanovic; Antanas Verikas; Jianhong Zhou, Editor(s)

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