Coarse-to-fine image reconstruction based on weighted differential features and background Gauge fields

Bart J. Janssen, Remco Duits, Luc Florack

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

6 Citations (Scopus)

Abstract

We propose an iterative approximate reconstruction method where we minimize the difference between reconstructions from subsets of multi scale measurements. To this end we interpret images not as scalar-valued functions but as sections through a fibered space. Information from previous reconstructions, which can be obtained at a coarser scale than the current one, is propagated by means of covariant derivatives on a vector bundle. The gauge field that is used to define the covariant derivatives is defined by the previously reconstructed image. An advantage of using covariant derivatives in the variational formulation of the reconstruction method is that with the number of iterations the accuracy of the approximation increases. The presented reconstruction method allows for a reconstruction at a resolution of choice, which can also be used to speed up the approximation at a finer level. An application of our method to reconstruction from a sparse set of differential features of a scale space representation of an image allows for a weighting of the features based on the sensitivity of those features to noise. To demonstrate the method we apply it to the reconstruction from singular points of a scale space representation of an image.
Original languageEnglish
Title of host publicationScale Space and Variational Methods in Computer Vision (Second International Conference, SSVM 2009, Voss, Norway, June 1-5, 2009, Proceedings)
EditorsX.-C. Tai, K. Morken, M. Lysaker, K.A. Lie
Place of PublicationBerlin
PublisherSpringer
Pages377-388
Number of pages12
ISBN (Print)978-3-642-02255-5
DOIs
Publication statusPublished - 2009

Publication series

NameLecture Notes in Computer Science
Volume5567
ISSN (Print)0302-9743

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