Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
S2DLDP with its Application to Palmprint Recognition Read chapter Read first chapter

2017 | OriginalPaper | Chapter

No Reference Assessment of Image Visibility for Dehazing

Authors : Manjun Qin, Fengying Xie, Zhiguo Jiang

Published in: Image and Graphics

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Haze affects the quality and visibility of the image. Many dehazing algorithms have been developed in recent years. However, the evaluation for the performance of the dehazing method is still not solved. The assessment is not easy to achieve since the reference image is not available. In this paper, a no reference image quality evaluation indicator is proposed to assess the visibility of a dehazed image. A multi-scale contrast feature is designed to measure the image sharpness. Considering some dehazing methods often cause under-dehazing results, a dark channel feature is employed to describe the haze residual degree of the restored image. Fusing the two features together, the final indicator that can measure the image visibility is obtained. Experimental results show that the assessment results are highly correlated with human visual perceptions and objective quality scores, which demonstrate the effectiveness and robustness of the proposed approach.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Book Page Identification Using Convolutional Neural Networks Trained by Task-Unrelated Dataset
next chapter Multi-focus Image Fusion Using Dictionary Learning and Low-Rank Representation
Literature
1.
Tan, R.T.: Visibility in bad weather from a single image. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)
2.
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)CrossRef
3.
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)
4.
5.
Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image Dehazing with boundary constraint and contextual regularization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 617–624 (2013)
6.
Dai, S.K., Tarel, J.P.: Adaptive sky detection and preservation in Dehazing algorithm. In: 2015 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pp. 634–639. IEEE (2015)
7.
Wu, D., Zhu, Q., Wang, J., Xie, Y., Wang, L.: Image haze removal: status, challenges and prospects. In: 2014 4th IEEE International Conference on Information Science and Technology (ICIST), pp. 492–497. IEEE (2014)
8.
Mai, J., Zhu, Q., Wu, D.: The latest challenges and opportunities in the current single image Dehazing algorithms. In: 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 118–123. IEEE (2014)
9.
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRef
10.
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)MathSciNetCrossRef
11.
Fattal, R.: Dehazing using color-lines. ACM Trans. Graph. (TOG) 34(1), 13 (2014)CrossRef
12.
Mai, J., Zhu, Q., Wu, D., Xie, Y., Wang, L.: Back propagation neural network Dehazing. In: 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1433–1438. IEEE (2014)
13.
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)MathSciNetCrossRef
14.
Hautiere, N., Tarel, J.P., Aubert, D., Dumont, E.: Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal. Stereol. 27(2), 87–95 (2011)MathSciNetCrossRefMATH
15.
Fang, S., Yang, J., Zhan, J., Yuan, H., Rao, R.: Image quality assessment on image haze removal. In: 2011 Chinese Control and Decision Conference (CCDC), pp. 610–614. IEEE (2011)
16.
17.
Fattal, R.: Single image Dehazing. ACM Trans. Graph. (TOG) 27(3), 72 (2008)CrossRef
18.
Gibson, K.B., Nguyen, T.Q.: Fast single image fog removal using the adaptive wiener filter. In: 2013 20th IEEE International Conference on Image Processing (ICIP), pp. 714–718. IEEE (2013)
19.
Kim, J.H., Jang, W.D., Sim, J.Y., Kim, C.S.: Optimized contrast enhancement for real-time image and video Dehazing. J. Vis. Commun. Image Represent. 24(3), 410–425 (2013)CrossRef
20.
Gu, K., Tao, D., Qiao, J.-F., Lin, W.: Learning a no-reference quality assessment model of enhanced images with big data. IEEE Trans. Neural Netw. Learn. Syst. (2017)
21.
Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)MathSciNetCrossRefMATH
Metadata
Title
No Reference Assessment of Image Visibility for Dehazing
Authors
Manjun Qin
Fengying Xie
Zhiguo Jiang
Copyright Year
2017
Book
Image and Graphics

Print ISBN: 978-3-319-71606-0

Electronic ISBN: 978-3-319-71607-7

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-71607-7_58

Premium Partner

    Image Credits