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Fourth Canadian Conference on Computer and Robot Vision (CRV '07)
Training Database Adequacy Analysis for Learning-Based Super-Resolution
Montreal, Quebec, Canada
May 28-May 30
ISBN: 0-7695-2786-8
Isabelle Begin, McGill University
Frank P. Ferrie, McGill University
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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