TY - GEN
T1 - Object dependent manifold priors for image deconvolution
AU - Ni, Jie
AU - Turaga, Pavan
AU - Patel, Vishal M.
AU - Chellappa, Rama
PY - 2010/12/1
Y1 - 2010/12/1
N2 - Deblurring is an inverse problem which has traditionally been studied from a signal processing perspective. In this paper we consider the role of extra information in the form of prior knowledge of the object class to solve this problem. Specifically, we incorporate unlabeled image data of the object class, say natural images, in the form of a patch-manifold prior for the object class. The manifold is implicitly estimated from the given unlabeled data. We show how the patch manifold prior effectively exploits the availability of the sample class data for regularizing the deblurring problem.
AB - Deblurring is an inverse problem which has traditionally been studied from a signal processing perspective. In this paper we consider the role of extra information in the form of prior knowledge of the object class to solve this problem. Specifically, we incorporate unlabeled image data of the object class, say natural images, in the form of a patch-manifold prior for the object class. The manifold is implicitly estimated from the given unlabeled data. We show how the patch manifold prior effectively exploits the availability of the sample class data for regularizing the deblurring problem.
UR - http://www.scopus.com/inward/record.url?scp=84896753284&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84896753284&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84896753284
SN - 9781557528926
T3 - Optics InfoBase Conference Papers
BT - Digital Image Processing and Analysis, DIPA 2010
T2 - Digital Image Processing and Analysis, DIPA 2010
Y2 - 7 June 2010 through 8 June 2010
ER -