Object dependent manifold priors for image deconvolution

Jie Ni, Pavan Turaga, Vishal M. Patel, Rama Chellappa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationDigital Image Processing and Analysis, DIPA 2010
StatePublished - Dec 1 2010
Externally publishedYes
EventDigital Image Processing and Analysis, DIPA 2010 - Tucson, AZ, United States
Duration: Jun 7 2010Jun 8 2010

Publication series

NameOptics InfoBase Conference Papers
ISSN (Electronic)2162-2701

Other

OtherDigital Image Processing and Analysis, DIPA 2010
CountryUnited States
CityTucson, AZ
Period6/7/106/8/10

ASJC Scopus subject areas

  • Instrumentation
  • Atomic and Molecular Physics, and Optics

Fingerprint Dive into the research topics of 'Object dependent manifold priors for image deconvolution'. Together they form a unique fingerprint.

Cite this