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.

© 2010 Optical Society of America

PDF Article
More Like This
Image Priors and Blind Deconvolution

William Freeman
DMC1 Digital Image Processing and Analysis (DIPA) 2010

Compressive imaging measurement design from an image patch manifold prior

Robert Muise and David Bottisti
CM1C.2 Computational Optical Sensing and Imaging (COSI) 2013

Blind deconvolution of turbulence-degraded images using natural PSF priors

Roberto Baena Gallé, Szymon Gladysz, Laurent Mugnier, Rao Gudimetla, Robert L. Johnson, and Lee Kann
SM1F.3 Signal Recovery and Synthesis (SRS) 2014


You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
Login to access Optica Member Subscription