SAC - Semi-adaptive, convex optimization
methodology for image restoration
Kirsi Majava
Department of Mathematical Information Technology in University
of Jyvaskyla
Tuesday, March 12, 2002 15:00-16:00,
Room 145, NIST North (820)
Gaithersburg
Tuesday, March 12, 2002 13:00-14:00,
Room 4550
Boulder
Abstract:
The aim of image restoration is to reconstruct both sharp edges
and smooth subsurfaces from a given noisy image effectively. For
this purpose, we describe a new methodology which is based on a
semiadaptive, convex (SAC) optimization formulation.
The SAC method describes basic steps to realize an image restoration
algorithm with high restoration capability and decent computational
efficiency, with automatic determination of free parameters.
Another novel ingredient of the approach is the utilization of a
suitable histogram capturing the basic characteristics and the amount
of noise in a given digital image. The principal part of the algorithm
is based on the fast active-set method for solving the BV-regularized
image restoration problem.
Contact: A. J. KearsleyNote: Visitors from outside NIST must contact
Robin Bickel; (301) 975-3668;
at least 24 hours in advance.
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