Stochastic Image Reconstruction from Local Histograms of Gradient Orientation

  • Agnès DesolneuxEmail author
  • Arthur Leclaire
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10302)


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.


Image synthesis Reconstruction from features SIFT Poisson editing Maximum entropy distributions Exponential models 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.CMLA, ENS Cachan, CNRS, Université Paris-SaclayCachanFrance

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