Training Database Adequacy Analysis for Learning-Based Super-Resolution

CC
CRV
2007
Fourth Canadian Conference on Computer and Robot Vision (CRV '07)
Abstract - Training Database Adequacy Analysis for Learning-Based Super-Resolution

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

Fourth Canadian Conference on Computer and Robot Vision (CRV '07)

Montreal, Quebec, Canada

May 28-May 30

ISBN: 0-7695-2786-8

	ASCII Text	x
	Isabelle Begin, Frank P. Ferrie, "Training Database Adequacy Analysis for Learning-Based Super-Resolution," Computer and Robot Vision, Canadian Conference, pp. 29-35, Fourth Canadian Conference on Computer and Robot Vision (CRV '07), 2007.

	BibTex	x
	@article{ 10.1109/CRV.2007.65, author = {Isabelle Begin and Frank P. Ferrie}, title = {Training Database Adequacy Analysis for Learning-Based Super-Resolution}, journal ={Computer and Robot Vision, Canadian Conference}, volume = {0}, year = {2007}, isbn = {0-7695-2786-8}, pages = {29-35}, doi = {http://doi.ieeecomputersociety.org/10.1109/CRV.2007.65}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Computer and Robot Vision, Canadian Conference TI - Training Database Adequacy Analysis for Learning-Based Super-Resolution SN - 0-7695-2786-8 SP29 EP35 A1 - Isabelle Begin, A1 - Frank P. Ferrie, PY - 2007 KW - null VL - 0 JA - Computer and Robot Vision, Canadian Conference ER -

Isabelle Begin, McGill University

Frank P. Ferrie, McGill University

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CRV.2007.65

This paper explores the possibility of assessing the adequacy of a training database to be used in a learningbased super-resolution process. The Mean Euclidean Distance (MED) function is obtained by averaging the distance between each input patch and its closest candidate in the training database, for a series of blurring kernels used to construct the low-resolution database. The shape of that function is thought to indicate the level of adequacy of the database, thus indicating to the user the potential of success of a learning-based super-resolution algorithm using this database.

Citation:

Isabelle Begin, Frank P. Ferrie, "Training Database Adequacy Analysis for Learning-Based Super-Resolution," crv, pp.29-35, Fourth Canadian Conference on Computer and Robot Vision (CRV '07), 2007

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