Efficient block noise removal based on nonlinear manifolds

Haoying Fu, Hongyuan Zha, Jesse Louis Barlow

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

Abstract

The problem of block noise removal is considered. It is assumed that the original image is on or close to a subspace of admissible images in the form of a low dimensional nonlinear manifold. We propose to use a close variant of the total variation regularizer for measuring block noise. Based on this noise measure, we present an effective approach that reconstructs the original image in the presence of block noise. Our main computational task is the solution of a quadratic programming problem, for which we propose a very efficient interior point method. The effectiveness and efficiency of our approach is demonstrated by an example.

Original languageEnglish (US)
Title of host publicationProceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005
Pages549-556
Number of pages8
DOIs
StatePublished - Dec 1 2005
EventProceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005 - Beijing, China
Duration: Oct 17 2005Oct 20 2005

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
VolumeI

Other

OtherProceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005
Country/TerritoryChina
CityBeijing
Period10/17/0510/20/05

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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