Skip to main content
SpringerLink
Log in

An Effective Scale-Aware Edge-Smoothing Weighting Constraint-Based Weighted Guided Image Filter for Single Image Dehazing

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

This paper proposes a new effective scale-aware edge-smoothing weighting constraint-based weighted guided image filter (ESAESWC-WGIF) for single image dehazing. Edge-weighting constraint incorporated in this method is multi-scale and less sensitive to regularization parameter. It removes halo artifacts and over-smoothing strongly and preserves edge information in both flat and sharp regions more accurately than the guided image filter (GIF) and weighted guided image filter (WGIF). There are three main steps in the proposed method: In the first step, dark channel prior method is applied to hazy input image to estimate atmospheric map and transmission map. In the next step, we refine the initial transmission map using the proposed ESAESWC-WGIF. It removes halo artifacts, over-smoothing effect strongly and preserves edge information in both flat and sharp regions. In the final step, the haze-free image is recovered from the scene radiance. About 3200 images from Fattal, NYU2, D-HAZY, Haze-RD, and O-Haze datasets are used to compare the performance of the proposed filter with the existing image dehazing methods. Experimental results prove that the proposed method is independent of the nature of the input image. Moreover, it produces better visual quality. It is noteworthy that the proposed method is faster than the existing methods for a given resolution of images.

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
Fig. 10

Similar content being viewed by others

Data Availability

The datasets generated or analyzed during the present study are available from the corresponding author upon reasonable request.

References

  1. C. Ancuti, C.O. Ancuti, C. De Vleeschouwer, D-HAZY: a dataset to evaluate quantitatively dehazing algorithms. In 2016 IEEE International Conference on Image Processing (ICIP), pp. 2226–2230. IEEE (2016). https://doi.org/10.1109/ICIP.2016.7532754

  2. C.O. Ancuti, C. Ancuti, R. Timofte, C. De Vleeschouwer, O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 867–8678 (2018). https://doi.org/10.1109/CVPRW.2018.00119

  3. C.O. Ancuti, C. Ancuti, M. Sbert, R. Timofte, Dense-Haze: a benchmark for image dehazing with dense-haze and haze-free images. In 2019 IEEE International Conference on Image Processing (ICIP), pp. 1014–1018 (2019). https://doi.org/10.1109/ICIP.2019.8803046

  4. T.M. Bui, W. Kim, Single image dehazing using color ellipsoid prior. IEEE Trans. Image Process. 27(2), 999–1009 (2017). https://doi.org/10.1109/TIP.2017.2771158

    Article  MathSciNet  MATH  Google Scholar 

  5. B. Cai, X. Xu, K. Jia, C. Qing, D. Tao, DehazeNet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 25(11), 5187–5198 (2016). https://doi.org/10.1109/TIP.2016.2598681

    Article  MathSciNet  MATH  Google Scholar 

  6. B. Chen, S. Wu, Weighted aggregation for guided image filtering. Signal Image Video Process. 14(3), 491–498 (2020). https://doi.org/10.1007/s11760-019-01579-1

    Article  Google Scholar 

  7. L.K. Choi, J. You, A.C. Bovik, Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans. Image Process. 24(11), 3888–3901 (2015). https://doi.org/10.1109/TIP.2015.2456502

    Article  MathSciNet  MATH  Google Scholar 

  8. N.R. Draper, H. Smith, Applied Regression Analysis, vol. 326 (Wiley, New York, 1998)

    Book  MATH  Google Scholar 

  9. A. Dudhane, S. Murala, RYF-Net: deep fusion network for single image haze removal. IEEE Trans. Image Process. 29, 628–640 (2020). https://doi.org/10.1109/TIP.2019.2934360

    Article  MathSciNet  MATH  Google Scholar 

  10. R. Fattal, Single image dehazing. ACM Trans. Graph. (TOG) 27(3), 1–9 (2008). https://doi.org/10.1145/1360612.1360671

    Article  Google Scholar 

  11. H. Geethu, S. Shamna, J.J. Kizhakkethottam, Weighted guided image filtering and haze removal in single image. Procedia Technol. 24, 1475–1482 (2016). https://doi.org/10.1016/j.protcy.2016.05.248

    Article  Google Scholar 

  12. K. He, J. Sun, X. Tang, Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2010). https://doi.org/10.1109/TPAMI.2010.168

    Article  Google Scholar 

  13. K. He, J. Sun, X. Tang, Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2012). https://doi.org/10.1109/TPAMI.2012.213

    Article  Google Scholar 

  14. L. He, J. Zhao, N. Zheng, D. Bi, Haze removal using the difference–structure–preservation prior. IEEE Trans. Image Process. 26(3), 1063–1075 (2016). https://doi.org/10.1109/TIP.2016.2644267

    Article  MathSciNet  MATH  Google Scholar 

  15. G.-S. Hong, B.-G. Kim, A local stereo matching algorithm based on weighted guided image filtering for improving the generation of depth range images. Displays 49, 80–87 (2017). https://doi.org/10.1016/j.displa.2017.07.006

    Article  Google Scholar 

  16. G.-S. Hong, J.-K. Park, B.-G. Kim, Near real-time local stereo matching algorithm based on fast guided image filtering. In 2016 6th European Workshop on Visual Information Processing (EUVIP), pp. 1–5. IEEE (2016). https://doi.org/10.1109/EUVIP.2016.7764595

  17. Q. Huynh-Thu, M. Ghanbari, Scope of validity of PSNR in image/video quality assessment. Electron. Lett. 44(13), 800–801 (2008). https://doi.org/10.1049/el:20080522

    Article  Google Scholar 

  18. H. Israël, F. Kasten, Koschmieders theorie der horizontalen sichtweite. In Die Sichtweite im Nebel und die Möglichkeiten ihrer künstlichen Beeinflussung, pp. 7–10. Springer (1959). https://doi.org/10.1007/978-3-663-04661-5_2

  19. M. Ju, C. Ding, D. Zhang, Y.J. Guo, BDPK: Bayesian dehazing using prior knowledge. IEEE Trans. Circuits Syst. Video Technol. 29(8), 2349–2362 (2018). https://doi.org/10.1109/TCSVT.2018.2869594

    Article  Google Scholar 

  20. M. Ju, C. Ding, Y.J. Guo, D. Zhang, IDGCP: image dehazing based on Gamma correction prior. IEEE Trans. Image Process. 29, 3104–3118 (2019). https://doi.org/10.1109/TIP.2019.2957852

    Article  MATH  Google Scholar 

  21. M. Ju, C. Ding, W. Ren, Y. Yang, IDBP: image dehazing using blended priors including non-local, local, and global priors. IEEE Trans. Circuits Syst. Video Technol. (2021). https://doi.org/10.1109/TCSVT.2021.3101503

    Article  Google Scholar 

  22. J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, D. Lischinski, Deep photo: model-based photograph enhancement and viewing. ACM Trans. Graph. (TOG) 27(5), 1–10 (2008). https://doi.org/10.1145/1409060.1409069

    Article  Google Scholar 

  23. F. Kou, W. Chen, C. Wen, Z. Li, Gradient domain guided image filtering. IEEE Trans. Image Process. 24(11), 4528–4539 (2015). https://doi.org/10.1109/TIP.2015.2468183

    Article  MathSciNet  MATH  Google Scholar 

  24. B. Li, X. Peng, Z. Wang, J. Xu, D. Feng, AOD-Net: All-in-one dehazing network. In 2017 IEEE International Conference on Computer Vision (ICCV), pp. 4780–4788 (2017). https://doi.org/10.1109/ICCV.2017.511

  25. C. Li, C. Guo, J. Guo, P. Han, H. Fu, R. Cong, PDR-Net: perception-inspired single image dehazing network with refinement. IEEE Trans. Multimed. 22(3), 704–716 (2019). https://doi.org/10.1109/TMM.2019.2933334

    Article  Google Scholar 

  26. Z. Li, J. Zheng, Z. Zhu, W. Yao, S. Wu, Weighted guided image filtering. IEEE Trans. Image process. 24(1), 120–129 (2014). https://doi.org/10.1109/TIP.2014.2371234

    Article  MathSciNet  MATH  Google Scholar 

  27. S. Lin, C. Wong, G. Jiang, M. Rahman, T. Ren, N. Kwok, H. Shi, Y.-H. Yu, T. Wu, Intensity and edge based adaptive unsharp masking filter for color image enhancement. Optik 127(1), 407–414 (2016). https://doi.org/10.1016/j.ijleo.2015.08.046

    Article  Google Scholar 

  28. Z. Lu, B. Long, K. Li, F. Lu, Effective guided image filtering for contrast enhancement. IEEE Signal Process. Lett. 25(10), 1585–1589 (2018). https://doi.org/10.1109/LSP.2018.2867896

    Article  Google Scholar 

  29. Z. Lu, B. Long, S. Yang, Saturation based iterative approach for single image dehazing. IEEE Signal Process. Lett. 27, 665–669 (2020). https://doi.org/10.1109/LSP.2020.2985570

    Article  Google Scholar 

  30. E.J. McCartney, Optics of the Atmosphere: Scattering by Molecules and Particles (Wiley, New York, 1976)

    Google Scholar 

  31. A.K. Moorthy, A.C. Bovik, A two-step framework for constructing blind image quality indices. IEEE Signal Process. Lett. 17(5), 513–516 (2010). https://doi.org/10.1109/LSP.2010.2043888

    Article  Google Scholar 

  32. S.G. Narasimhan, S.K. Nayar, Chromatic framework for vision in bad weather. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No. PR00662), volume 1, pp. 598–605. IEEE (2000). https://doi.org/10.1109/CVPR.2000.855874

  33. S.G. Narasimhan, S.K. Nayar, Vision and the atmosphere. Int. J. Comput. Vis. 48(3), 233–254 (2002). https://doi.org/10.1023/A:1016328200723

    Article  MATH  Google Scholar 

  34. X. Qin, Z. Wang, Y. Bai, X. Xie, H. Jia, FFA-Net: feature fusion attention network for single image dehazing. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 11908–11915 (2020). https://doi.org/10.1609/aaai.v34i07.6865

  35. G. Sharma, W. Wu, E.N. Dalal, The CIEDE2000 color-difference formula: implementation notes, supplementary test data, and mathematical observations. Color Res. Appl. 30(1), 21–30 (2005). https://doi.org/10.1002/col.20070

    Article  Google Scholar 

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

  37. N. Silberman, D. Hoiem, P. Kohli, R. Fergus, Indoor segmentation and support inference from rgbd images. In European Conference on Computer Vision, pp. 746–760. Springer (2012). https://doi.org/10.1007/978-3-642-33715-4_54

  38. R.T. Tan, Visibility in bad weather from a single image. In 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008). https://doi.org/10.1109/CVPR.2008.4587643

  39. C. Tomasi, R. Manduchi, Bilateral filtering for gray and color images. In Sixth International Conference on Computer Vision, pp. 839–846. IEEE (1998). https://doi.org/10.1109/ICCV.1998.710815

  40. Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). https://doi.org/10.1109/TIP.2003.819861

    Article  Google Scholar 

  41. S.K. Yadav, K. Sarawadekar, Single image dehazing using adaptive gamma correction method. In TENCON 2019: 2019 IEEE Region 10 Conference (TENCON), pp. 1752–1757. IEEE (2019). https://doi.org/10.1109/TENCON.2019.8929383

  42. S.K. Yadav, K. Sarawadekar, Steering kernel-based guided image filter for single image dehazing. In 2020 IEEE Region 10 Conference (TENCON), pp. 444–449. IEEE (2020). https://doi.org/10.1109/TENCON50793.2020.9293825

  43. D. Yang, J. Sun, Proximal Dehaze-Net: a prior learning-based deep network for single image dehazing. In Proceedings of the European Conference on Computer Vision (ECCV), pp. 729–746 (2018). https://doi.org/10.1007/978-3-030-01234-2_43

  44. B. Zhang, J.P. Allebach, Adaptive bilateral filter for sharpness enhancement and noise removal. IEEE Trans. Image Process. 17(5), 664–678 (2008). https://doi.org/10.1109/TIP.2008.919949

    Article  MathSciNet  Google Scholar 

  45. J. Zhang, Y. Cao, S. Fang, Y. Kang, C.W. Chen, Fast haze removal for nighttime image using maximum reflectance prior. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7016–7024 (2017). https://doi.org/10.1109/CVPR.2017.742

  46. Y. Zhang, L. Ding, G. Sharma, HazeRD: an outdoor scene dataset and benchmark for single image dehazing. In 2017 IEEE International Conference on Image Processing (ICIP), pp. 3205–3209. IEEE (2017). https://doi.org/10.1109/ICIP.2017.8296874

  47. S. Zhao, L. Zhang, Y. Shen, Y. Zhou, RefineDNet: a weakly supervised refinement framework for single image dehazing. IEEE Trans. Image Process. 30, 3391–3404 (2021). https://doi.org/10.1109/TIP.2021.3060873

    Article  Google Scholar 

  48. Q. Zhu, J. Mai, L. Shao, A fast single image haze removal algorithm using color attenuation prior. IEEE Trans. Image Process. 24(11), 3522–3533 (2015). https://doi.org/10.1109/TIP.2015.2446191

    Article  MathSciNet  MATH  Google Scholar 

  49. E.R. Ziegel, The elements of statistical learning. Technometrics 45(3), 267–268 (2003). https://doi.org/10.1198/tech.2003.s770

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics Engineering, Indian Institute of Technology (BHU), Varanasi, Varanasi, U.P, 221005, India

    Sumit Kr. Yadav & Kishor Sarawadekar

Authors
  1. Sumit Kr. Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kishor Sarawadekar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kishor Sarawadekar.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yadav, S.K., Sarawadekar, K. An Effective Scale-Aware Edge-Smoothing Weighting Constraint-Based Weighted Guided Image Filter for Single Image Dehazing. Circuits Syst Signal Process 42, 6136–6159 (2023). https://doi.org/10.1007/s00034-023-02389-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-023-02389-0

Keywords

Search

Navigation