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Real-Time Video Dehazing Based on Spatio-Temporal MRF

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Advances in Multimedia Information Processing - PCM 2016 (PCM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9917))

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Abstract

Video dehazing has a wide range of real-time applications, but the challenges mainly come from spatio-temporal coherence and computational efficiency. In this paper, a spatio-temporal optimization framework for real-time video dehazing is proposed, which reduces blocking and flickering artifacts and achieves high-quality enhanced results. We build a Markov Random Field (MRF) with an Intensity Value Prior (IVP) to handle spatial consistency and temporal coherence. By maximizing the MRF likelihood function, the proposed framework estimates the haze concentration and preserves the information optimally. Moreover, to facilitate real-time applications, integral image technique is approximated to reduce the main computational burden. Experimental results demonstrate that the proposed framework is effectively to remove haze and flickering artifacts, and sufficiently fast for real-time applications.

X. Xu—This work is supported in part by the National Natural Science Founding of China (61171142, 61401163), Science and Technology Planning Project of Guangdong Province of China (2011A010801005, 2014B010111003, 2014B010111006), Guangzhou Key Lab of Body Data Science (201605030011) and Australian Research Council Projects (FT-130101457 and DP-140102164).

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Notes

  1. 1.

    http://trace.eas.asu.edu/yuv/.

  2. 2.

    http://www.cad.zju.edu.cn/home/gfzhang/projects/videodepth/data/.

  3. 3.

    More comparisons can be found at http://caibolun.github.io/st-mrf/.

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Authors and Affiliations

  1. South China University of Technology, Guangzhou, China

    Bolun Cai & Xiangmin Xu

  2. University of Technology Sydney, Ultimo, Australia

    Dacheng Tao

Authors
  1. Bolun Cai
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  2. Xiangmin Xu
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  3. Dacheng Tao
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Corresponding author

Correspondence to Xiangmin Xu .

Editor information

Editors and Affiliations

  1. Zhengzhou University, Zhengzhou, China

    Enqing Chen

  2. Jiaotong University, Xi’an, China

    Yihong Gong

  3. Zhengzhou University, Zhengzhou, China

    Yun Tie

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Cai, B., Xu, X., Tao, D. (2016). Real-Time Video Dehazing Based on Spatio-Temporal MRF. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9917. Springer, Cham. https://doi.org/10.1007/978-3-319-48896-7_31

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  • DOI: https://doi.org/10.1007/978-3-319-48896-7_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48895-0

  • Online ISBN: 978-3-319-48896-7

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