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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Fractional Order Derivatives Regularization: Mode
ls\, Algorithms and Applications - Ke Chen (Univer
sity of Liverpool)
DTSTART;TZID=Europe/London:20170905T095000
DTEND;TZID=Europe/London:20170905T104000
UID:TALK77821AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/77821
DESCRIPTION:In variational imaging and other inverse problem m
odeling\, regularisation plays a major role.In rec
ent years\, high order regularizers such as the me
an curvature\, the Gaussian curvature and Euler
9\;s elastica are increasingly studied and applied
\, and many impressive results over the widely-use
d gradient based models are reported.

Here
we present some results from studying another clas
s of high and non-integer order regularisers based
on fractional order derivatives and focus on two
aspects of this class of models:(i) theoretical an
alysis and advantages\; (ii) efficient algorithms.
We found that models with regularization by fracti
onal order derivatives are convex in a suitable sp
ace and algorithms exploiting structured matrices
can be employed to design efficient algorithms.App
lications to restoration and registration are illu
strated. \;This opens many opportunities to ap
ply these regularisers to a wide class of imaging
problems.

Ke Chen and J P Zhang\, EPSRC Liv
erpool Centre for Mathematics in Healthcare\,Centr
e for Mathematical Imaging Techniques\, \; &nb
sp\;and Department of Mathematical Sciences\,The U
niversity of Liverpool\,United Kingdom[ http://tinyurl.com/EPSRC-LCMH ]&nbs
p\;

LOCATION:Seminar Room 1\, Newton Institute
CONTACT:INI IT
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