Simultaneous video defogging and stereo reconstruction

We present a method to jointly estimate scene depth and recover the clear latent image from a foggy video sequence. In our formulation, the depth cues from stereo matching and fog information reinforce each other, and produce superior results than conventional stereo or defogging algorithms. We firs...

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Bibliographic Details
Published in:2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 4988 - 4997
Main Authors: Li, Zhuwen, Tan, Ping, Tan, Robby T., Zou, Danping, Zhou, Steven Zhiying, Cheong, Loong-Fah
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.06.2015
Subjects:
Cameras
Coherence
Computer vision
Conferences
Consistency
Fog
Image color analysis
Laplace equations
Magnetorheological fluids
Mathematical model
Mathematical models
Media
Pattern recognition
Smoothness
ISSN:1063-6919, 1063-6919
Online Access:Get full text
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Summary:We present a method to jointly estimate scene depth and recover the clear latent image from a foggy video sequence. In our formulation, the depth cues from stereo matching and fog information reinforce each other, and produce superior results than conventional stereo or defogging algorithms. We first improve the photo-consistency term to explicitly model the appearance change due to the scattering effects. The prior matting Laplacian constraint on fog transmission imposes a detail-preserving smoothness constraint on the scene depth. We further enforce the ordering consistency between scene depth and fog transmission at neighboring points. These novel constraints are formulated together in an MRF framework, which is optimized iteratively by introducing auxiliary variables. The experiment results on real videos demonstrate the strength of our method.
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ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2015.7299133