Single Image Dehazing based on Dark Channel Prior with Different Atmospheric Light

Sheng Zhang, Wencang Bai

Abstract

Single image dehazing based on dark channel prior could recover a high-quality haze-free image from non-sky image. However, it does not perform well in bright region such as sky region. This paper proposes a novel method for single image dehazing, which jointly considers the atmospheric lights of sky regions and land surface. In this proposal, we divide the image with sky regions into bright image (such as sky region and artificial light) and dark image (such as natural outdoor scenery and buildings) according to the image saturation, the intensity of pixels and Rayleigh scattering theory. In the recovery processing, bright image and dark image can be recovered separately with different parameters of atmospheric light. The experimental results show that the proposed scheme can obtain a high-quality haze-free image in the images which cover the sky.

References

  1. He, K., Sun, J., Tang, X., 2009. Single image haze removal using dark channel prior. In IEEE Conference on Computer Vision and Pattern Recognition, 2009. pp. 1956-1963.
  2. Oakley, J, P., Bu, H., 2007. Correction of simple contrast loss in color images. In IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, vol. 16, pp. 511-22.
  3. Tan, R, T., 2008. Visibility in bad weather from a single image. Proc. IEEE Conf. Computer Vision and Pattern Rrcognition pp. 1-8.
  4. Fattal, R., 2008. Single image dehazing. Acm Transactions on Graphics, vol. 27, pp. 1-9.
  5. Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C., 2013. Efficient Image Dehazing with Boundary Constraint and Contextual Regularization. In IEEE International Conference on Computer Vision, pp. 617-624.
  6. Narasimhan, S, G., Nayar, S. K., 2000. Chromatic framework for vision in bad weather. In Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on, pp. 598-605 vol.1.
  7. Narasimhan, S, G., Nayar, S, K., 2002. Vision and the Atmosphere. International Journal of Computer Vision, vol. 48, pp. 233-254.
  8. He, K., Sun, J., Tang, X., 2013. Guided Image Filtering. IEEE Transactions on Software Engineering, vol. 35, pp. 1397-1409.
  9. Chuang, Y, Y., Curless, B., Salesin, D, H., Szeliski, R., 2001. A Bayesian Approach to Digital Matting. CVPR, vol. 2, pp. 264-271.
  10. Levin, A., Lischinski, D., Weiss, Y., 2006. A Closed Form Solution to Natural Image Matting. In IEEE Computer Society Conference on Computer Vision & Pattern Recognition, pp. 61-68.
Download


Paper Citation


in Harvard Style

Zhang S. and Bai W. (2017). Single Image Dehazing based on Dark Channel Prior with Different Atmospheric Light . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 224-229. DOI: 10.5220/0006154702240229


in Bibtex Style

@conference{visapp17,
author={Sheng Zhang and Wencang Bai},
title={Single Image Dehazing based on Dark Channel Prior with Different Atmospheric Light},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={224-229},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006154702240229},
isbn={978-989-758-225-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Single Image Dehazing based on Dark Channel Prior with Different Atmospheric Light
SN - 978-989-758-225-7
AU - Zhang S.
AU - Bai W.
PY - 2017
SP - 224
EP - 229
DO - 10.5220/0006154702240229