Skip to main content
SpringerLink
Log in

Smart and real-time image dehazing on mobile devices

  • Original Research Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

Haze is one of the common factors that degrades the visual quality of the images and videos. This diminishes contrast and reduces visual efficiency. The ALS (Atmospheric light scattering) model which has two unknowns to be estimated from the scene: atmospheric light and transmission map, is commonly used for dehazing. The process of modelling the atmospheric light scattering is complex and estimation of scattering is time consuming. This condition makes dehazing in real-time difficult. In this work, a new approach is employed for dehazing in real time which reads the orientation sensor of mobile device and compares the amount of rotation with a pre-specified threshold. The system decides whether to recalculate the atmospheric light or not. When the amount of rotation is little means there are only subtle changes to the scene, it uses the pre-estimated atmospheric light. Therefore, the system does not need to recalculate it at each time instant and this approach accelerates the overall dehazing process. 0.07 s fps (frame per second) per frame processing time (~ 15 fps) is handled for 360p imagery. Frame processing time results show that our approach is superior to the state-of-the-art real-time dehazing implementations on mobile operating systems.

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. Wang, W., Yuan, X.: Recent advances in image dehazing. IEEE/CAA J. Automatica Sinica 4(3), 410–436 (2017)

    Article  MathSciNet  Google Scholar 

  2. Jia, Z., Wang, H.C., Caballero, R., Xiong, Z.Y., Zhao, J.W., Finn, A.: Real-time content adaptive contrast enhancement for see-through fog and rain. In Proc. IEEE Int. Conference Acoustics Speech and Signal Processing, pp. 1378−1381 (2010)

  3. Al-Sammaraie, M.F.: Contrast enhancement of roads images with foggy scenes based on histogram equalization. In Proc. 10th International Conference on Computer Science & Education, pp. 95−101 (2015)

  4. Kim, J.H., Sim, J.Y., Kim, C.S.: Single image dehazing based on contrast enhancement. In Proc. IEEE International Conference Acoustics, Speech and Signal Processing, pp. 1273−1276 (2011)

  5. Cai, W.T., Liu, Y.X., Li, M.C., Cheng, L., Zhang, C.X.: A self-adaptive homomorphic filter method for removing thin cloud. In Proc. 19th International Conference Geoinformatics, pp. 1−4 (2011)

  6. Tan, K., Oakley, J.P.: Physics-based approach to color image enhancement in poor visibility conditions. J. Opt. Soc. Am. 18(10), 2460–2467 (2001)

    Article  Google Scholar 

  7. Tang, K.T., Yang, J.C., Wang, J.: Investigating haze-relevant features in a learning framework for image dehazing. In Proc. IEEE Conference Computer Vision and Pattern Recognition, pp. 2995−3002 (2014)

  8. Gibson, K.B., Belongie, S.J., Nguyen, T.Q.: Example based depth from fog. In Proc. 20th IEEE International Conference on Image Processing, pp. 728−732 (2013)

  9. Fang, S., Xia, X.S., Xing, H., Chen, C.W.: Image dehazing using polarization effects of objects and airlight. Opt. Express 22(16), 19523–19537 (2014)

    Article  Google Scholar 

  10. Galdran, A., Vazquez-Corral, J., Pardo, D., Bertalmio, M.: Enhanced variational image dehazing. SIAM J. Imaging Sci. 8(3), 1519–2154 (2015)

    Article  MathSciNet  Google Scholar 

  11. Son, J., Kwon, H., Shim, T., Kim, Y., Ahu, S., Sohng, K.: Fusion method of visible and infrared images in foggy environment. In Proc. International Conference on Image Processing, Computer Vision, and Pattern Recognition, pp. 433−437 (2015)

  12. Ancuti, C.O., Ancuti, C.: Single image dehazing by multi-scale fusion. IEEE Trans. Image Process. 22(8), 3271–3282 (2013)

    Article  Google Scholar 

  13. Ma, Z.L., Wen, J., Zhang, C., Liu, Q.Y., Yan, D.N.: An effective fusion defogging approach for single sea fog image. Neurocomputing 173, 1257–1267 (2016)

    Article  Google Scholar 

  14. Guo, F., Tang, J., Cai, Z.X.: Fusion strategy for single image dehazing. Int. J. Digit. Content Technol. Appl. 7(1), 19–28 (2013)

    Google Scholar 

  15. Zhang, H., Liu, X., Huang, Z.T., Ji, Y.F.: Single image dehazing based on fast wavelet transform with weighted image fusion. In Proc. IEEE International Conference on Image Processing, pp. 4542−4546 (2014)

  16. Simi, V.R., Edla, D.R., Joseph, J., and Kuppili, V.: Parameter-free fuzzy histogram equalisation with illumination preserving characteristics dedicated for contrast enhancement of magnetic resonance images. Appl. Soft Comput. 93, (2020)

  17. Joseph, J., Periyasamy, R.: A fully customized enhancement scheme for controlling brightness error and contrast in magnetic resonance images. Biomed. Signal Process. Control 39, 271–283 (2018)

    Article  Google Scholar 

  18. Joseph, J., Sivaraman, J., Periyasamy, R., Simi, V.R.: An objective method to identify optimum clip-limit and histogram specification of contrast limited adaptive histogram equalization for MR images. Biocybernet. Biomed. Eng. 37(3), 489–497 (2017)

    Article  Google Scholar 

  19. Hao, W., He, M., Ge, H., Wang, C., Qing-Wei, G.: Retinex-like method for image enhancement in poor visibility conditions. Procedia Eng. 15, 2798–2803 (2011)

    Article  Google Scholar 

  20. Wang, W., Chang, F., Ji, T., Wu, X.: A fast single-image dehazing method based on a physical model and gray projection. IEEE Access (2018). https://doi.org/10.1109/ACCESS.2018.2794340

    Article  Google Scholar 

  21. Kaiming, H., Jian, S., Xiaoou, T.: Single image haze removal using dark channel prior. In IEEE Transactions on pattern analysis and machine intelligence. (2011)

  22. Park, D., Park, H., Han, D. K., Ko, H.: Single image dehazing with image entropy and information fidelity. In IEEE International Conference on Image Processing (ICIP), pp. 4037-4041, (2014)

  23. Li, J., Li, G., Fan, H.: Image dehazing using residual-based deep CNN. IEEE Access 6, 26831–26842 (2018)

    Article  Google Scholar 

  24. Li, C., Guo, J., Porikli, F., Fu, H., Pang, Y.: A cascaded convolutional neural network for single image dehazing. IEEE Access 6, 24877–24887 (2018)

    Article  Google Scholar 

  25. Haouassi, S., Di, W.: Image dehazing based on (CMTnet) cascaded multi-scale convolutional neural networks and efficient light estimation algorithm. Appl. Sci. 10, 1190 (2020)

    Article  Google Scholar 

  26. 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  Google Scholar 

  27. Rashid, H., Zafar, N., Javed Iqbal, M., Dawood, H., Dawood, H.: Single image dehazing using CNN. Procedia Comput. Sci. 147, 124–130 (2019)

    Article  Google Scholar 

  28. Hassan, H., Bashir, A.K., Ahmad, M., et al.: Real-time image dehazing by superpixels segmentation and guidance filter. J. Real-Time Image Process. (2020). https://doi.org/10.1007/s11554-020-00953-4

    Article  Google Scholar 

  29. Yuanyuan, S., Yue. M.: Single image dehazing on mobile device based on GPU rendering technology. In Journal of Robotics, Networking and Artificial Life (2015)

  30. Lu, J., Dong, C.: DSP-based image real-time dehazing optimization for improved dark-channel prior algorithm. J. Real-Time Image Process. 17, 1675–1684 (2019)

    Article  Google Scholar 

  31. C6748 pure DSP device data sheet : Available on: https://www.ti.com/lit/ml/sprt633/sprt633.pdf?ts=1597690676332&ref_url=https%253A%252F%252Fwww.google.com%252F. Accessed 8 Oct 2020

  32. Vazquez-Corral, J., Galdran, A., Cyriac, P., et al.: A fast image dehazing method that does not introduce color artifacts. J. Real-Time Image Process. 17, 607–622 (2020)

    Article  Google Scholar 

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

    Article  Google Scholar 

  34. Diaz-Ramirez, V.H., Hernández-Beltrán, J.E., Juarez-Salazar, R.: Real-time haze removal in monocular images using locally adaptive processing. J. Real-Time Image Process. 16, 1959–1973 (2019)

    Article  Google Scholar 

  35. Cheng, K., Yu, Y., Zhou, H., et al.: GPU fast restoration of non-uniform illumination images. J. Real-Time Image Process. 18, 75–83 (2020)

    Article  Google Scholar 

  36. Hernandez-Beltran, J., Diaz-Ramirez, V., Juarez-Salazar, R.: Real-time image dehazing using genetic programming. J. Opt. Photonics Inf. Process. 13, (2019)

  37. Zhang, J., Hu, S.: A GPU-accelerated real-time single image de-hazing method using pixel-level optimal de-hazing criterion. J. Real-Time Image Process. 9, 661–672 (2014). https://doi.org/10.1007/s11554-012-0244-y

    Article  Google Scholar 

  38. Fattal, R.: Single image dehazing. In Proc. of ACM SIGGRAPH 08 (2008)

  39. Kopf, J., Neubert, B., Chen, B., Cohen, M., Cohen-Or, D., Deussen, O., Uyttendaele, M., Lischinski, D.: Deep photo: modelbased photograph enhancement and viewing. ACM Trans. Graph. 27(5), 1–10 (2008)

    Article  Google Scholar 

  40. 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 

  41. Simi, V.R., Edla, D.R., Joseph, J., Kuppili, V.: Analysis of controversies in the formulation and evaluation of restoration algorithms for MR images. Expert Syst. Appl. 135, 39–59 (2019)

    Article  Google Scholar 

  42. Kuppusamy, P.G., Joseph, J., Sivaraman, J.: A customized nonlocal restoration scheme with adaptive strength of smoothening for MR images. Biomed. Signal Process. Control 49, 160–172 (2019)

    Article  Google Scholar 

  43. Simulink Android Support: Available on: https://www.mathworks.com/hardware-support/android-programming-simulink.html. Accessed 8 Oct 2020

  44. Android Studio. Available on: https://developer.android.com/studio. Accessed 8 Oct 2020

Download references

Author information

Authors and Affiliations

  1. Image and Signal Processing Group, HAVELSAN A.S, Ankara, Turkey

    Yucel Cimtay

Authors
  1. Yucel Cimtay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yucel Cimtay.

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

Cimtay, Y. Smart and real-time image dehazing on mobile devices. J Real-Time Image Proc 18, 2063–2072 (2021). https://doi.org/10.1007/s11554-021-01085-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-021-01085-z

Keywords

Search

Navigation