Comparison of Super-Resolution Algorithms Using Image Quality Measures

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	Similar Articles Articles by Isabelle Begin Articles by Frank P. Ferrie

The 3rd Canadian Conference on Computer and Robot Vision (CRV'06)

Quebec City, Quebec, Canada

June 07-June 09

ISBN: 0-7695-2542-3

	ASCII Text	x
	Isabelle Begin, Frank P. Ferrie, "Comparison of Super-Resolution Algorithms Using Image Quality Measures," Computer and Robot Vision, Canadian Conference, pp. 72, The 3rd Canadian Conference on Computer and Robot Vision (CRV'06), 2006.

	BibTex	x
	@article{ 10.1109/CRV.2006.23, author = {Isabelle Begin and Frank P. Ferrie}, title = {Comparison of Super-Resolution Algorithms Using Image Quality Measures}, journal ={Computer and Robot Vision, Canadian Conference}, volume = {0}, year = {2006}, isbn = {0-7695-2542-3}, pages = {72}, doi = {http://doi.ieeecomputersociety.org/10.1109/CRV.2006.23}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Computer and Robot Vision, Canadian Conference TI - Comparison of Super-Resolution Algorithms Using Image Quality Measures SN - 0-7695-2542-3 SP EP A1 - Isabelle Begin, A1 - Frank P. Ferrie, PY - 2006 KW - null VL - 0 JA - Computer and Robot Vision, Canadian Conference ER -

Isabelle Begin, McGill University, Canada

Frank P. Ferrie, McGill University, Canada

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CRV.2006.23

This paper presents comparisons of two learning-based super-resolution algorithms as well as standard interpolation methods. To allow quality assessment of results, a comparison of a variety of image quality measures is also performed. Results show that a MRF-based super-resolution algorithm improves a previously interpolated image. The estimated degree of improvement varies both according to the quality measure chosen for the comparison as well as the image class.

Citation:

Isabelle Begin, Frank P. Ferrie, "Comparison of Super-Resolution Algorithms Using Image Quality Measures," crv, pp.72, The 3rd Canadian Conference on Computer and Robot Vision (CRV'06), 2006

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