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Deep Video Dehazing

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Book cover Advances in Multimedia Information Processing – PCM 2017 (PCM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10735))

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Abstract

Haze is a major problem in videos captured in outdoors. Unlike single-image dehazing, video-based approaches can take advantage of the abundant information that exists across neighboring frames. In this work, assuming that a scene point yields highly correlated transmission values between adjacent video frames, we develop a deep learning solution for video dehazing, where a CNN is trained end-to-end to learn how to accumulate information across frames for transmission estimation. The estimated transmission map is subsequently used to recover a haze-free frame via atmospheric scattering model. To train this network, we generate a dataset consisted of synthetic hazy and haze-free videos for supervision based on the NYU depth dataset. We show that the features learned from this dataset are capable of removing haze that arises in outdoor scene in a wide range of videos. Extensive experiments demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world videos.

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References

  1. He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1956–1963. IEEE Press (2009)

    Google Scholar 

  2. Li, N., Liu, Z., Lei, J., Song, M., Bu, J.: Automatic color image enhancement using double channels. In: Chen, E., Gong, Y., Tie, Y. (eds.) PCM 2016. LNCS, vol. 9917, pp. 74–83. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48896-7_8

    Chapter  Google Scholar 

  3. Ren, W., Cao, X., Pan, J., Guo, X., Zuo, W., Yang, M.-H.: Image deblurring via enhanced low-rank prior. IEEE Trans. Image Process. 25(7), 3426–3437 (2017)

    Article  MathSciNet  Google Scholar 

  4. Berman, D., Treibitz, T., Avidan, S.: Non-local image dehazing. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1674–168. IEEE Press (2016)

    Google Scholar 

  5. Kim, J.-H., Jang, W.-D., Park, Y., Lee, D.-H., Sim, J.-Y., Kim, C.-S.: Temporally x real-time video dehazing. In: 19th IEEE International Conference on Image Processing, pp. 969–972. IEEE Press (2012)

    Google Scholar 

  6. Lv, X., Chen, W., Shen, I.: Real-time dehazing for image and video. In: 18th Pacific Conference on Computer Graphics and Applications, pp. 62–69. IEEE Press (2010)

    Google Scholar 

  7. Su, S., Delbracio, M., Wang, J., Sapiro, G., Heidrich W., Wang O.: Deep video deblurring. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE Press (2017)

    Google Scholar 

  8. Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33715-4_54

    Chapter  Google Scholar 

  9. Meng, G., Wang, Y., Duan, J., Xiang, S., Pan C.: Efficient image dehazing with boundary constraint and contextual. In: IEEE International Conference on Computer Vision, pp. 617–624. IEEE Press (2013)

    Google Scholar 

  10. Fattal, R.: Single image dehazing. ACM Trans. Graph. 27(3), 72 (2008)

    Article  Google Scholar 

  11. Zhu, Q., Mai, J., Shao, L.: A fast single image haze removal algorithm using color attenuation prior. IEEE Trans. Image Process. 24(11), 3522–3533 (2015)

    Article  MathSciNet  Google Scholar 

  12. Ancuti, C.O., Ancuti, C.: Single image dehazing by multi-scale fusion. IEEE Trans. Image Process. 22(8), 3271–3282 (2013)

    Article  Google Scholar 

  13. Zhang, J., Li, L., Zhang, Y., Yang, G., Cao, X., Sun, J.: Video dehazing with spatial and temporal coherence. Vis. Comput. 27(6), 749–757 (2011)

    Article  Google Scholar 

  14. Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017)

    Article  MathSciNet  Google Scholar 

  15. Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., Yang, M.-H.: Single image dehazing via multi-scale convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 154–169. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_10

    Chapter  Google Scholar 

  16. Cai, B., Xu, X., Jia, K., Qing, C., Tao, D.: DehazeNet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 25(11), 5187–5198 (2016)

    Article  MathSciNet  Google Scholar 

  17. Chen, C., Do, M.N., Wang, J.: Robust image and video dehazing with visual artifact suppression via gradient residual minimization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 576–591. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_36

    Chapter  Google Scholar 

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Acknowledgments

This work is supported by the National Key R&D Program of China (Grant No. 2016YFB0800603), National Natural Science Foundation of China (No. 61422213, U1605252), Beijing Natural Science Foundation (4172068).

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Authors and Affiliations

  1. State Key Laboratory of Information Security (SKLOIS), Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Wenqi Ren & Xiaochun Cao

Authors
  1. Wenqi Ren
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  2. Xiaochun Cao
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Corresponding author

Correspondence to Xiaochun Cao .

Editor information

Editors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Bing Zeng

  2. University of Chinese Academy of Sciences, Beijing, China

    Qingming Huang

  3. University of Ottawa, Ottawa, Ontario, Canada

    Abdulmotaleb El Saddik

  4. University of Electronic Science and Technology of China, Chengdu, China

    Hongliang Li

  5. Chinese Academy of Sciences, Beijing, China

    Shuqiang Jiang

  6. Harbin Institute of Technology, Harbin, China

    Xiaopeng Fan

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Ren, W., Cao, X. (2018). Deep Video Dehazing. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_2

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  • DOI: https://doi.org/10.1007/978-3-319-77380-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77379-7

  • Online ISBN: 978-3-319-77380-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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