Paper Info
Title
Stereo reconstruction and contrast restoration in daytime fog
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.
Year
DOI
Venue
2012
10.1007/978-3-642-37447-0_2
ACCV (4)
Keywords
Field
DocType
defogging problem,synthetic image,defogging algorithm,contrast restoration,stereo reconstruction,atmospheric veil depth cue,daytime fog,single image defogging,markov random field model,single image,standard stereo reconstruction,foggy weather
Computer vision,Pattern recognition,Computer science,Markov random field,Daytime,Stereo reconstruction,Gray level,Artificial intelligence,Depth perception
Conference
Citations 
PageRank 
References 
11
0.66
8
Authors
2
Name
Order
Citations
PageRank
Laurent Caraffa11156.61
Jean-Philippe Tarel280556.63