Skip to main content
SpringerLink
Log in

Cardinal color fusion network for single image haze removal

  • Special Issue Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Haze during bad weather degrades visibility of the scene drastically. Degradation of the scene visibility varies concerning the transmission map (TrMap) of the scene. Estimation of accurate TrMap is a key step to reconstruct the haze-free scene. Previous approaches, DehazeNet (Cai et al. in IEEE Trans Image Process 25(11):5187–5198, 2016), MSCNN (Ren et al., in: European conference on computer vision, Springer, New York, 2016), overcome the drawbacks of handcrafted features. However, they fail to recover the haze-free scene without color distortion. In this paper, cardinal color (red, green and blue) fusion network is proposed to estimate the robust TrMap. Proposed network recovers haze-free scene without color distortion. In the first stage, it generates multi-channel depth maps using a novel parallel convolution filter bank. The second stage comprises of multi-scale convolution filter bank which extracts the scale-invariant features. Further, spatial pooling followed by convolution filter estimates the robust scene transmission map. In the last stage, estimated TrMap is given to the optical model to recover the haze-free scene. Three benchmark datasets namely D-HAZY (Ancuti et al., in: 2016 IEEE international conference on image processing (ICIP), 2016), ImageNet (Deng et al., in: IEEE conference on computer vision and pattern recognition, 2009 (CVPR 2009), 2009) and a set of real-world hazy scenes are used to analyze the effectiveness of the proposed approach. Structural similarity index, mean square error and peak signal-to-noise ratio are used to evaluate the proposed network for single image haze removal. Further, the effect of haze on object detection is analyzed and robust cascaded pipeline, i.e., haze removal followed by Faster RCNN is proposed for object detection in the hazy environment. Performance analysis shows that the proposed network estimates robust TrMap and recovers haze-free scene without color distortion.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. Proposed network is designed to learn the different combinations of possible priors, it generates multiple depth maps. Multiple depth map indicates the haze-relevant features and not the literal meaning.

  2. Car images [16] are collected from the internet. Available at http://www.vision.caltech.edu/archive.html.

References

  1. Ancuti, C., Ancuti, C.O., De Vleeschouwer, C.: D-hazy: a dataset to evaluate quantitatively dehazing algorithms. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 2226–2230 (2016)

  2. Ancuti, C.O., Ancuti, C., Hermans, C., Bekaert, P.: A fast semi-inverse approach to detect and remove the haze from a single image. In: Asian Conference on Computer Vision, pp. 501–514. Springer, New York (2010)

  3. 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  MATH  Google Scholar 

  4. Chaudhary, S., Murala, S.: Depth-based end-to-end deep network for human action recognition. In: IET Computer Vision (2018)

  5. Chaudhary, S., Murala, S.: Tsnet: deep network for human action recognition in hazy videos. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3981–3986 (2018)

  6. Cozman, F., Krotkov, E.: Depth from scattering. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 801–806 (1997). https://doi.org/10.1109/CVPR.1997.609419

  7. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009. pp. 248–255 (2009)

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

    Article  Google Scholar 

  9. Goodfellow, I.J., Warde-Farley, D., Mirza, M., Courville, A., Bengio, Y.: Maxout networks (2013). arXiv preprint arXiv:1302.4389

  10. He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)

    Article  Google Scholar 

  11. He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 6, 1397–1409 (2013)

    Article  Google Scholar 

  12. Hinds, W.C.: Aerosol Technology: Properties, Behavior, and Measurement of Airborne Particles. Wiley, New York (2012)

    Google Scholar 

  13. Huang, S.C., Chen, B.H., Wang, W.J.: Visibility restoration of single hazy images captured in real-world weather conditions. IEEE Trans. Circuits Syst. Video Technol. 24(10), 1814–1824 (2014)

    Article  Google Scholar 

  14. Nayar, S.K., Narasimhan, S.G.: Vision in bad weather. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 820–827 (1999)

  15. Patil, P., Murala, S., Dhall, A., Chaudhary, S.: Msednet: multi-scale deep saliency learning for moving object detection. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1670–1675 (2018)

  16. Philip, B., Updike, P.: Car dataset. In: California Institute of Technology SURF Project for Summer (2001) http://www.vision.caltech.edu/archive.html

  17. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)

  18. Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., Yang, M.H.: Single image dehazing via multi-scale convolutional neural networks. In: European Conference on Computer Vision, pp. 154–169. Springer, New York (2016)

  19. Schechner, Y.Y., Narasimhan, S.G., Nayar, S.K.: Instant dehazing of images using polarization. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. CVPR 2001. vol. 1, pp. I–I (2001)

  20. Schechner, Y.Y., Narasimhan, S.G., Nayar, S.K.: Polarization-based vision through haze. Appl. Opt. 42(3), 511–525 (2003)

    Article  Google Scholar 

  21. Shwartz, S., Namer, E., Schechner, Y.Y.: Blind haze separation. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1984–1991 (2006). https://doi.org/10.1109/CVPR.2006.71

  22. Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from rgbd images. Comput. Vis. ECCV 2012, 746–760 (2012)

    Google Scholar 

  23. Tan, R.T.: Visibility in bad weather from a single image. In: IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008, pp. 1–8 (2008)

  24. Tang, K., Yang, J., Wang, J.: Investigating haze-relevant features in a learning framework for image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2995–3000 (2014)

  25. Yu, J., Xiao, C., Li, D.: Physics-based fast single image fog removal. In: 2010 IEEE 10th International Conference on Signal Processing (ICSP), pp. 1048–1052 (2010)

  26. 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  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CVPR Lab, Department of Electrical Engineering, Indian Institute of Technology Ropar, Rupnagar, India

    Akshay Dudhane & Subrahmanyam Murala

Authors
  1. Akshay Dudhane
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Subrahmanyam Murala
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Subrahmanyam Murala.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dudhane, A., Murala, S. Cardinal color fusion network for single image haze removal. Machine Vision and Applications 30, 231–242 (2019). https://doi.org/10.1007/s00138-019-01014-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-019-01014-y

Keywords

Search

Navigation