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International Conference on Scale Space and Variational Methods in Computer Vision

SSVM 2017: Scale Space and Variational Methods in Computer Vision pp 133–145Cite as

Stochastic Image Reconstruction from Local Histograms of Gradient Orientation

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10302))

Abstract

Many image processing algorithms rely on local descriptors extracted around selected points of interest. Motivated by privacy issues, several authors have recently studied the possibility of image reconstruction from these descriptors, and proposed reconstruction methods performing local inference using a database of images. In this paper we tackle the problem of image reconstruction from local histograms of gradient orientation, obtained from simplified SIFT descriptors. We propose two reconstruction models based on Poisson editing and on the combination of multiscale orientation fields. These models are able to recover global shapes and many geometric details of images. They compare well to state of the art results, without requiring the use of any external database.

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Author information

Authors and Affiliations

  1. CMLA, ENS Cachan, CNRS, Université Paris-Saclay, 94235, Cachan, France

    Agnès Desolneux & Arthur Leclaire

Authors
  1. Agnès Desolneux
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  2. Arthur Leclaire
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Corresponding author

Correspondence to Agnès Desolneux .

Editor information

Editors and Affiliations

  1. University of Copenhagen , Copenhagen, Denmark

    François Lauze

  2. Technical University of Denmark , Kongens Lyngby, Denmark

    Yiqiu Dong

  3. Technical University of Denmark , Kongens Lyngby, Denmark

    Anders Bjorholm Dahl

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Desolneux, A., Leclaire, A. (2017). Stochastic Image Reconstruction from Local Histograms of Gradient Orientation. In: Lauze, F., Dong, Y., Dahl, A. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science(), vol 10302. Springer, Cham. https://doi.org/10.1007/978-3-319-58771-4_11

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  • DOI: https://doi.org/10.1007/978-3-319-58771-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58770-7

  • Online ISBN: 978-3-319-58771-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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