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Stereo Reconstruction and Contrast Restoration in Daytime Fog

  • Laurent Caraffa
  • Jean-Philippe Tarel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7727)

Abstract

Stereo reconstruction serves many outdoor applications, and thus sometimes faces foggy weather. The quality of the reconstruction by state of the art algorithms is then degraded as contrast is reduced with the distance because of scattering. However, as shown by defogging algorithms from a single image, fog provides an extra depth cue in the gray level of far away objects. Our idea is thus to take advantage of both stereo and atmospheric veil depth cues to achieve better stereo reconstructions in foggy weather. To our knowledge, this subject has never been investigated earlier by the computer vision community. We thus propose a Markov Random Field model of the stereo reconstruction and defogging problem which can be optimized iteratively using the α-expansion algorithm. Outputs are a dense disparity map and an image where contrast is restored. The proposed model is evaluated on synthetic images. This evaluation shows that the proposed method achieves very good results on both stereo reconstruction and defogging compared to standard stereo reconstruction and single image defogging.

Keywords

Markov Random Field Stereo Pair Markov Random Field Model Remote Object Data Stereo 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Laurent Caraffa
    • 1
  • Jean-Philippe Tarel
    • 1
  1. 1.LEPSiS, IFSTTARUniversité Paris-EstParisFrance

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