Skip to main content
SpringerLink
Log in

DeeptransMap: a considerably deep transmission estimation network for single image dehazing

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Due to the ill-posed phenomenon of the classical physical model, single image dehazing based on the model has been a challenging vision task. In recent years, applying machine learning techniques to estimate a critical parameter transmission has proven to be an effective solution to this issue. Accordingly, the robustness and accuracy of learning-based transmission estimation model is extremely important, since it does impact on the final dehazing effects. The state-of-the-art dehazing algorithms by this means generally use haze-relevant features as the single input to their transmission estimation models. However, the used haze-relevant features sometimes are not sufficient and reliable in holding real intrinsic information related to haze due to their two shortcomings and ultimately bring about their less effectiveness for some dehazing cases. Based on related efforts on representation learning and deep convolutional neural networks, in this paper, we seek to achieve the robustness and accuracy of transmission estimation model for bolstering the effectiveness of single image dehazing. Specifically, we propose a hybrid model combining unsupervised and supervised learning in a considerably deep neural networks architecture, termed DeeptransMap, in order to achieve accurate transmission map from a single image. Experimental results demonstrate that our work performs favorably against several state-of-the-art dehazing methods with the same estimated goal and keeps efficient in terms of the computational complexity of transmission estimation.

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

Similar content being viewed by others

References

  1. Ancuti CO, Ancuti C, Hermans C, Bekaert P (2010) A fast semi-inverse approach to detect and remove the haze from a single image. In: Proceedings of the 10th Asian conference on computer vision (ACCV), pp 501–514. Springer

  2. Bai S, Li Z, Hou J (2017) Learning two-pathway convolutional neural networks for categorizing scene images. Multimed Tools Appl 76(15):16145–16162

    Article  Google Scholar 

  3. Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  5. Coates A, Ng AY (2012) Learning feature representations with k-means. Lect Notes Comput Sci 7700:561–580 Springer

    Article  Google Scholar 

  6. Economopoulos T, Asvestas P, Matsopoulos G (2010) Contrast enhancement of images using partitioned iterated function systems. Image Vis Comput 28(1):45–54

    Article  Google Scholar 

  7. Fattal R (2008) Single image dehazing. ACM Trans Graph 27(3):1–9

    Article  Google Scholar 

  8. Geiger A, Lenz P, Stiller C, Urtasun R (2013) Vision meets robotics: the KITTI dataset. Int J Robot Res 32(11):1231–1237

    Article  Google Scholar 

  9. Hautière N, Tarel J, Aubert D, Dumont E (2011) Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal Stereology 27(2):87–95

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  11. He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409

    Article  Google Scholar 

  12. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770-778. IEEE

  13. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861

  14. Hu J, Shen L, Sun G (2017) Squeeze-and-excitation networks. arXiv preprint arXiv:1709.01507

  15. Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R (2014) Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on multimedia, pp 675-678. ACM

  16. Jobson D, Rahman Z, Woodell G, Hines G (2006) A comparison of visual statistics for the image enhancement of foresite aerial images with those of major image classes. In: Proceedings of SPIE - the international society for optical engineering, 6246:624601-624601-8

  17. Lee H, Grosse R, Ranganath R, Ng AY (2009) Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th annual international conference on machine learning, pp 609-616. ACM

  18. Li B, Peng X, Wang Z, Xu J, Feng D (2017) An all-in-one network for dehazing and beyond. arXiv preprint arXiv:1707.06543

  19. Liou C, Huang J, Yang W (2008) Modeling word perception using the Elman network. Neurocomputing 71(16):3150–3157

    Article  Google Scholar 

  20. McCartney EJ (1975) Optics of the atmosphere: scattering by molecules and particles. John Wiley and Sons: New York

  21. Meng G, Wang Y, Duan J, Xiang S, Pan C (2013) Efficient image dehazing with boundary constraint and contextual regularization. In: Proceedings of the IEEE international conference on computer vision, pp 617-624. IEEE

  22. Narasimhan S, Nayar S (2003) Contrast restoration of weather degraded images. IEEE Trans Pattern Anal Mach Intell 25(6):713–724

    Article  Google Scholar 

  23. Nayar SK, Narasimhan SG (1999) Vision in bad weather. In: Proceedings of the seventh IEEE international conference on computer vision, pp 820-827. IEEE

  24. Ren W, Liu S, Zhang H, Pan J, Cao X, Yang M (2016) Single image dehazing via multi-scale convolutional neural networks. In: Proceedings of the 14th European conference on computer vision (ECCV), pp 154–169. Springer

  25. Rifai S, Vincent P, Muller X, Glorot X, Bengio Y (2011) Contractive auto-encoders: explicit invariance during feature extraction. In: Proceedings of the 28th international conference on machine learning, pp 833-840

  26. Saxena A, Sun M, Ng A (2009) Make3D: learning 3D scene structure from a single still image. IEEE Trans Pattern Anal Mach Intell 31(5):824–840

    Article  Google Scholar 

  27. Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y (2013) Overfeat: integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229

  28. Silberman N, Hoiem D, Kohli P, Fergus R (2012) Indoor segmentation and support inference from rgbd images. In: Proceedings of the 12th European conference on computer vision (ECCV), pp 746–760. Springer

  29. Sutskever I, Martens J, Dahl G, Hinton G (2013) On the importance of initialization and momentum in deep learning. In: Proceedings of the 30th International conference on machine learning (ICML), pp 1139–1147

  30. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1-9. IEEE

  31. Tan RT (2008) Visibility in bad weather from a single image. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1-8. IEEE

  32. Tang K, Yang J, Wang J (2014) 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. IEEE

  33. Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P (2010) Stacked Denoising autoencoders: learning useful representations in a deep network with a local Denoising criterion. J Mach Learn Res 11(12):3371–3408

    MathSciNet  MATH  Google Scholar 

  34. Wang Z, Bovik A (2002) A universal image quality index. IEEE Signal Processing Lett 9(3):81–84

    Article  Google Scholar 

  35. Wang Y, Wu L (2018) Beyond low-rank representations: orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering. Neural Netw 103:1–8

    Article  Google Scholar 

  36. Wang Y, Lin X, Wu L, Zhang W, Zhang Q (2015) Lbmch: learning bridging mapping for cross-modal hashing. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, pp 999-1002. ACM

  37. Wang Y, Lin X, Wu L, Zhang W, Zhang Q, Huang X (2015) Robust subspace clustering for multi-view data by exploiting correlation consensus. IEEE Trans Image Process 24(11):3939–3949

    Article  MathSciNet  Google Scholar 

  38. Wang Y, Lin X, Wu L, Zhang W (2017) Effective multi-query expansions: collaborative deep networks for robust landmark retrieval. IEEE Trans Image Process 26(3):1393–1404

    Article  MathSciNet  Google Scholar 

  39. Wu L, Wang Y, Gao J, Li X (2018) Deep adaptive feature embedding with local sample distributions for person re-identification. Pattern Recogn 73:275–288

    Article  Google Scholar 

  40. Wu L, Wang Y, Li X, Gao J (2018) Deep attention-based spatially recursive networks for fine-grained visual recognition. IEEE Trans Cybernetics PP(99):1–12

    Google Scholar 

  41. Wu L, Wang Y, Li X, Gao J (2018) What-and-where to match: deep spatially multiplicative integration networks for person re-identification. Pattern Recogn 76:727–738

    Article  Google Scholar 

  42. Xu Y, Wen J, Fei L, Zhang Z (2016) Review of video and image defogging algorithms and related studies on image restoration and enhancement. IEEE Access 4:165–188

    Article  Google Scholar 

  43. Yu J, Xu D, Liao Q (2011) Image defogging: a survey. J Image Graph 16(9):1561–1576

    Google Scholar 

  44. Yu X, Xiao C, Deng M, Peng L (2011) A classification algorithm to distinguish image as haze or non-haze. In: Proceedings of the sixth international conference on image and graphics, pp 286-289. IEEE

  45. Zhang X, Zhou X, Lin M, Sun J (2017) ShuffleNet: an extremely efficient convolutional neural network for mobile devices. arXiv preprint arXiv:1707.01083

  46. Zhang M, Gao C, Li Q, Wang L, Zhang J (2018) Action detection based on tracklets with the two-stream CNN. Multimed Tools Appl 77(3):3303–3316

    Article  Google Scholar 

  47. Zhao J, Mathieu M, Goroshin R, LeCun Y (2016) Stacked what-where auto-encoders. arXiv preprint arXiv:1506.02351v8

  48. Zhu Q, Mai J, Shao L (2012) Single image Dehazing using color attenuation prior. Int J Comput Vis 98(3):263–278

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 51679180), the National Social Science Foundation of China (No. 15BGL048) and Hubei Province Science and Technology Support Project, China (2015BAA072). The authors greatly acknowledge all reviewers for their detailed comments.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430063, People’s Republic of China

    Jing Huang, Wen Jiang, Lin Li & Gaojing Zhou

  2. Technology School of Navigation, Wuhan University of Technology, Wuhan, 430063, People’s Republic of China

    Yuanqiao Wen

Authors
  1. Jing Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wen Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuanqiao Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gaojing Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Huang.

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

Huang, J., Jiang, W., Li, L. et al. DeeptransMap: a considerably deep transmission estimation network for single image dehazing. Multimed Tools Appl 78, 30627–30649 (2019). https://doi.org/10.1007/s11042-018-6536-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6536-x

Keywords

Search

Navigation