Skip to main content
SpringerLink
Log in

Real-time image and video dehazing based on multiscale guided filtering

  • 1213: Computational Optimization and Applications for Heterogeneous Multimedia Data
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

We propose a real-time dehazing algorithm for hazy images and videos based on multiscale guided filtering. The most time-consuming step in physical model-based algorithms is estimating the transmission map and atmospheric light. In this work, we develop a computationally efficient approach for the estimation. First, we construct an image pyramid from a hazy image. Then, we estimate the transmission map and atmospheric light at the coarsest level. Next, we obtain the transmission at the finest level by iterative upsampling with guide image filtering to avoid information loss. Furthermore, we extend the single-image dehazing algorithm to real-time video dehazing to reduce flickering artifacts in dehazed videos by making transmission values temporally coherent. Experimental results show that the proposed algorithm is applicable in real-time applications, while providing comparable or even better performance than that of state-of-the-art algorithms.

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. Preliminary results of this work have been presented in part in [26]. In this paper, we provide more algorithmic details of the transmission estimation and develop a new algorithm to preserve the temporal coherence for video dehazing. Furthermore, more comprehensive experiments are included, which show the effectiveness of the proposed algorithm, including subjective and objective assessments and temporal coherence evaluation.

  2. http://mcl.korea.ac.kr/projects/dehazing/

  3. https://github.com/JiamingMai/Color-Attenuation-Prior-Dehazing

  4. https://cchen156.github.io/code/robustdehaze.zip

  5. https://github.com/rwenqi/GFN-dehazing

  6. https://github.com/legendongary/Proximal-Dehaze-Net-CPU

  7. https://github.com/proteus1991/GridDehazeNet

  8. https://github.com/Boyiliee/EVD-Net

  9. https://github.com/viengiaan/MGF_dehazing

References

  1. Ancuti CO et al (2019) NTIRE 2019 image dehazing challenge report. In: Proc IEEE Conf Comput Vis Pattern Recognit. Workshops, pp 2241–2253

  2. Berman D, Treibitz T, Avidan S (2016) Non-local image dehazing. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 1674–1682

  3. Bui TM, Kim W (2018) Single image dehazing using color ellipsoid prior. IEEE Trans Image Process 27(2):999–1009

    Article  MathSciNet  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. Chen C, Do M, Wang J (2016) Robust image and video dehazing with visual artifact suppression via gradient residual minimization. In: Proc European Conf Comput Vis, pp 576–591

  6. Choi LK, You J, Bovik AC (2015) Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans Image Process 24 (11):3888–3901

    Article  MathSciNet  Google Scholar 

  7. Dong H, Pan J, Xiang L, Hu Z, Zhang X, Wang F, Yang MH (2020) Multi-scale boosted dehazing network with dense feature fusion. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 2154–2164

  8. Engin D, Genc A, Ekenel HK (2018) Cycle-Dehaze: Enhanced CycleGAN for single image dehazing. In: Proc IEEE Conf Comput Vis Pattern Recognit. Workshops, pp 938–946

  9. Fattal R (2015) Dehazing using color-lines. ACM Trans Graphics 34(1):13:1–13:14

    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 L, Zhao J, Zheng N, Bi D (2017) Haze removal using the difference-structure-preservation prior. IEEE Trans Image Process 26 (3):1063–1075

    Article  MathSciNet  Google Scholar 

  13. Kim JH, Jang WD, Sim JY, Kim CS (2013) Optimized contrast enhancement for real-time image and video dehazing. J Vis Commun Image Represent 24(3):410–425

    Article  Google Scholar 

  14. Kou F, Chen W, Wen C, Li Z (2015) Gradient domain guided image filtering. IEEE Trans Image Process 24(11):4528–4539

    Article  MathSciNet  Google Scholar 

  15. Li R, Pan J, Li Z, Tang J (2018) Single image dehazing via conditional generative adversarial network. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 8202–8211

  16. Li B, Peng X, Wang Z, Xu J, Feng D (2017) AOD-Net: All-in-one dehazing network. In: Proc IEEE Int Conf Comput Vis, pp 4780–4788

  17. Li B, Peng X, Wang Z, Xu J, Feng D (2018) End-to-end united video dehazing and detection. In: Proc AAAI Conf Artificial Intell

  18. Li Z, Tan P, Tan RT, Zou D, Zhou SZ, Cheong LF (2015) Simultaneous video defogging and stereo reconstruction. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 4988–4997

  19. Liu Q, Gao X, He L, Lu W (2018) Single image dehazing with depth-aware non-local total variation regularization. IEEE Trans Image Process 27 (10):5178–5191

    Article  MathSciNet  Google Scholar 

  20. Liu X, Ma Y, Shi Z, Chen J (2019) GridDehazeNet: Attention-based multi-scale network for image dehazing. In: Proc IEEE Int Conf Comput Vis, pp 7313–7322

  21. Lucas B, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: Proc Int Joint Conf Artif Intell, pp 674–679

  22. Mittal A, Moorthy AK, Bovik AC (2012) No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 21(12):4695–4708

    Article  MathSciNet  Google Scholar 

  23. Mittal A, Soundararajan R, Bovik AC (2013) Making a “completely blind” image quality analyzer. IEEE Signal Process Lett 20(3):209–212

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Nguyen TV, Mai TTN, Lee C (2021) Single maritime image defogging based on illumination decomposition using texture and structure priors. IEEE Access 9:34590–34603

  26. Nguyen TV, Vien AG, Lee C (2019) Fast image dehazing based on multi-scale guided filtering. In: Proc Int Workshop Advanced Image Technol, pp 182–186

  27. Park J, Han DK, Ko H (2020) Fusion of heterogeneous adversarial networks for single image dehazing. IEEE Trans Image Process 29:4721–4732

    Article  Google Scholar 

  28. Qin B, Huang Z, Zeng F, Ji Y (2015) Fast single image dehazing with domain transformation-based edge-preserving filter and weighted quadtree subdivision. In: Proc IEEE Int Conf Image Process, pp 4233–4237

  29. Qu Y, Chen Y, Huang J, Xie Y (2019) Enhanced pix2pix dehazing network. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 8152–8160

  30. Ren W, Ma L, Zhang J, Pan J, Cao X, Liu W, Yang M (2018) Gated fusion network for single image dehazing. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 3253–3261

  31. Ren W, Zhang J, Xu X, Ma L, Cao X, Meng G, Liu W (2019) Deep video dehazing with semantic segmentation. IEEE Trans Image Process 28(4):1895–1908

    Article  MathSciNet  Google Scholar 

  32. Shao Y, Li L, Ren W, Gao C, Sang N (2020) Domain adaptation for image dehazing. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 2805–2814

  33. Tan RT (2008) Visibility in bad weather from a single image. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 1–8

  34. Vazquez-Corral J, Galdran A, Cyriac P, Bertalmio M (2018) A fast image dehazing method that does not introduce color artifacts. J Real-Time Image Process. 17(3):607–622

    Article  Google Scholar 

  35. Venkatanath N, Praneeth D, Bh MC, Channappayya SS, Medasani SS (2015) Blind image quality evaluation using perception based features. In: Proc IEEE Nat Conf Commun, pp 1–6

  36. Wang W, Yuan X, Wu X, Liu Y (2017) Fast image dehazing method based on linear transformation. IEEE Trans Multimedia 19(6):1142–1155

    Article  Google Scholar 

  37. Xu Z, Liu X, Ji N (2009) Fog removal from color images using contrast limited adaptive histogram equalization. In: Int Congress Image Signal Process, pp 1–5

  38. Xu H, Zhai G, Wu X, Yang X (2014) Generalized equalization model for image enhancement. IEEE Trans Multimedia 16(1):68–82

    Article  Google Scholar 

  39. Yang J, Jiang B, Lv Z, Jiang N (2017) A real-time image dehazing method considering dark channel and statistics features. J Real-Time Image Process 13(3):479–490

    Article  Google Scholar 

  40. Yang D, Sun J (2018) Proximal Dehaze-Net: A prior learning-based deep network for single image dehazing. In: Proc European Conf Comput Vis, pp 729–746

  41. Zhang X, Dong H, Pan J, Zhu C, Tai Y, Wang C, Li J, Huang F, Wang F (2021) Learning to restore hazy video: A new real-world dataset and a new method. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 9239–9248

  42. Zhang H, Sindagi V, Patel VM (2018) Multi-scale single image dehazing using perceptual pyramid deep network. In: Proc IEEE Conf Comput Vis Pattern Recognit. Workshops, pp 1015–1024

  43. Zhao D, Xu L, Ma L, Li J, Yan Y (2020) Pyramid global context network for image dehazing. IEEE Trans Circuits Syst Video Technol 31(8):3037–3050

  44. Zhu Q, Mai J, Shao L (2015) A fast single image haze removal algorithm using color attenuation prior. IEEE Trans Image Process 24(11):3522–3533

    Article  MathSciNet  Google Scholar 

  45. Zhu H, Peng X, Chandrasekhar V, Li L, Lim JH (2018) DehazeGAN: When image dehazing meets differential programming. In: Proc Int Joint Conf Artificial Intell, pp 1234–1240

Download references

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (No. NRF-2019R1A2C4069806 and NRF-2022R1F1A1074402).

Author information

Authors and Affiliations

  1. Department of Multimedia Engineering, Dongguk University, Seoul, 04620, South Korea

    Thuong Van Nguyen, An Gia Vien & Chul Lee

Authors
  1. Thuong Van Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. An Gia Vien
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chul Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chul Lee.

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

Van Nguyen, T., Vien, A.G. & Lee, C. Real-time image and video dehazing based on multiscale guided filtering. Multimed Tools Appl 81, 36567–36584 (2022). https://doi.org/10.1007/s11042-022-13533-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13533-4

Keywords

Search

Navigation