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SUMMARY:Fractional Order Derivatives Regularization: Models\, Algorithms
and Applications - Ke Chen (University of Liverpool)
DTSTART:20170905T085000Z
DTEND:20170905T094000Z
UID:TALK77821@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:In variational imaging and other inverse problem modeling\, re
gularisation plays a major role.In recent years\, high order regularizers
such as the mean curvature\, the Gaussian curvature and Euler'\;s elast
ica are increasingly studied and applied\, and many impressive results ove
r the widely-used gradient based models are reported.
Here we prese
nt some results from studying another class of high and non-integer order
regularisers based on fractional order derivatives and focus on two aspect
s of this class of models:(i) theoretical analysis and advantages\; (ii) e
fficient algorithms.We found that models with regularization by fractional
order derivatives are convex in a suitable space and algorithms exploitin
g structured matrices can be employed to design efficient algorithms.Appli
cations to restoration and registration are illustrated. \;This opens
many opportunities to apply these regularisers to a wide class of imaging
problems.
Ke Chen and J P Zhang\, EPSRC Liverpool Centre for Mathem
atics in Healthcare\,Centre for Mathematical Imaging Techniques\, \; &
nbsp\;and Department of Mathematical Sciences\,The University of Liverpool
\,United Kingdom[ http://tinyurl.com/EPSRC-LCMH ] \;
LOCATION:Seminar Room 1\, Newton Institute
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