Skip to main content
SpringerLink
Log in

Design and implementation of hardware-efficient architecture for saturation-based image dehazing algorithm

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

Abstract

For real-time single-image dehazing, this paper suggests a straightforward and efficient saturation-based transmission map estimation method. For the suggested image dehazing algorithm, the design of a hardware-efficient very large scale integration (VLSI) architecture is also provided. By removing the computationally demanding sorting operations, the algorithm computes the dark channel, increases the robustness of atmospheric light estimation using a hardware-friendly local atmospheric light estimation module based on the pixel saturation values, and reduces the effects of halo artifacts using an edge-preserving filter to estimate the saturation-based transmission map. Compared to previous sophisticated dehazing approaches, this study exhibits competitive performance in the quality of the dehazed images. The best of the existing dehazing architecture as well as the proposed architecture are described in Verilog hardware description language (HDL), functionally verified using Vivado 2019.1 simulator, and synthesized using Cadence genus compiler. The results of the implementation show that the suggested design is hardware-efficient and offers higher throughput. The suggested dehazing architecture achieves better results in terms of area and delay than the most recent methods and is appropriate for applications with hardware restrictions.

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
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

The datasets used for analysis in this paper are publicly available and they are acknowledged in the references.

References

  1. Parihar, A. S., Gupta, Y. K., Singodia, Y., Singh, V., Singh, K.: A comparative study of image dehazing algorithms. In: 2020 5th Int. Conf. Commun. Electron. Syst. (ICCES), pp. 766–771 (2020). https://doi.org/10.1109/ICCES48766.2020.9138037

  2. Agrawal, S.C., Jalal, A.S.: A comprehensive review on analysis and implementation of recent image dehazing methods. Arch. Comput. Methods Eng. 29, 4799–4850 (2022). https://doi.org/10.1007/s11831-022-09755-2

    Article  Google Scholar 

  3. Sahu, G., Seal, A., Bhattacharjee, D., Nasipuri, M., Brida, P., Krejcar, O.: Trends and prospects of techniques for Haze removal from degraded images: a survey. IEEE Trans. Emerg. Top. Comput. Intell. 6, 762–782 (2022). https://doi.org/10.1109/TETCI.2022.3173443

    Article  Google Scholar 

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

  5. 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). https://doi.org/10.1109/TPAMI.2010.168

    Article  Google Scholar 

  6. Yang, H., Wang, J.: Color image contrast enhancement by co-occurrence histogram equalization and dark channel prior. In: Proc. 3rd Int. Cong. Image Signal Process. (CISP), Yantai, pp. 659–663 (2010)

  7. Tripathi, A. K., Mukhopadhyay, S.: Single image fog removal using bilateral filter. In: Proc. IEEE Int. Conf. Signal Process. Comput. Control (ISPCC), pp. 1–6 (2012)

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

    Article  Google Scholar 

  9. Lee, S., Yun, S., Nam, J.H., Won, C.S., Jung, S.W.: A review on dark channel prior based image dehazing algorithms. EURASIP J. Image Video Process. 1, 1–23 (2016)

    Google Scholar 

  10. Kim, J.H., Jang, W.D., Sim, J.Y., Kim, C.S.: Optimized contrast enhancement for real-time image and video dehazing. Vis. Commun. Image Represent. 24(3), 410–425 (2013)

    Article  Google Scholar 

  11. Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. In: Proc. IEEE Int. Conf. Computer Vision, pp. 617–624 (2013). https://doi.org/10.1109/ICCV.2013.82

  12. 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)

    Article  MathSciNet  MATH  Google Scholar 

  13. Qiu, Y., Wu, S.: Contrast-based stereoscopic images dehazing. In: 2015 IEEE 10th Conf. Ind. Electron. Appl. (ICIEA), pp. 597–602 (2015). https://doi.org/10.1109/ICIEA.2015.7334181

  14. Berman, D., Treibitz, T., Avidan, S.: Single image dehazing using hazelines. IEEE Trans. Pattern Anal. Mach. Intell. 42(3), 720–734 (2020). https://doi.org/10.1109/TPAMI.2018.2882478

    Article  Google Scholar 

  15. Kim, K., Kim, S., Kim, K.S.: Effective image enhancement techniques for fog-affected indoor and outdoor images. IET Image Process. 12(4), 465–471 (2018). https://doi.org/10.1049/iet-ipr.2016.0819

    Article  Google Scholar 

  16. Park, Y., Kim, T.H.: Fast execution schemes for dark-channel-prior-based outdoor video dehazing. IEEE Access 6, 10003–10014 (2018). https://doi.org/10.1109/ACCESS.2018.2806378

    Article  Google Scholar 

  17. 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). https://doi.org/10.1007/s11554-017-0698-z

    Article  Google Scholar 

  18. Lu, J., Dong, C.: Dsp-based image real-time dehazing optimization for improved dark-channel prior algorithm. J. Real-Time Image Process. 17(5), 1675–1684 (2020). https://doi.org/10.1007/s11554-019-00933-3

    Article  MathSciNet  Google Scholar 

  19. Ngo, D., Lee, S., Kang, B.: Robust single-image haze removal using optimal transmission map and adaptive atmospheric light. Remote Sens. 12(14), 2233 (2020). https://doi.org/10.3390/rs12142233

    Article  Google Scholar 

  20. Kim, S.E., Park, T.H., Eom, I.K.: Fast single image dehazing using saturation based transmission map estimation. IEEE Trans. Image Process. 29, 1985–1998 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  21. Sahu, G., Seal, A., Krejcar, O., Yazidi, A.: Single image dehazing using a new color channel. J. Vis. Commun. Image Represent. 74, 103008 (2021). https://doi.org/10.1016/j.jvcir.2020.103008

    Article  Google Scholar 

  22. Kumari, A., Sahoo, S.K., Chinnaiah, M.C.: Fast and efficient visibility restoration technique for single image dehazing and defogging. IEEE Access 9, 48131–48146 (2021). https://doi.org/10.1109/ACCESS.2021.3068446

    Article  Google Scholar 

  23. Hsu, W.Y., Chen, Y.S.: Single image dehazing using wavelet-based haze-lines and denoising. IEEE Access 9, 104547–104559 (2021). https://doi.org/10.1109/ACCESS.2021.3099224

    Article  Google Scholar 

  24. Shiau, Y.H., Yang, H.Y., Chen, P.Y., Chuang, Y.Z.: Hardware implementation of a fast and efficient haze removal method. IEEE Trans. Circuits Syst. Video Technol. 23(8), 1369–1374 (2013)

    Article  Google Scholar 

  25. Zhang, B., Zhao, J.: Hardware implementation for real-time haze removal. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 25(3), 1188–1192 (2017)

    Article  Google Scholar 

  26. Shiau, Y.H., Kuo, Y.T., Chen, P.Y., Hsu, F.Y.: VLSI design of an efficient flicker-free video defogging method for real-time applications. IEEE Trans. Circuits Syst. Video Technol. 29(1), 238–251 (2019). https://doi.org/10.1109/TCSVT.2017.2777140

    Article  Google Scholar 

  27. Kuo, Y.T., Chen, W.T., Chen, P.Y., Li, C.H.: VLSI implementation for an adaptive haze removal method. IEEE Access 7, 173977–173988 (2019). https://doi.org/10.1109/ACCESS.2019.2953959

    Article  Google Scholar 

  28. Kumar, R., Balasubramanian, R., Kaushik, B.K.: Efficient method and architecture for real-time video defogging. IEEE Trans. Intell. Transp. Syst. 22(10), 6536–6546 (2021). https://doi.org/10.1109/TITS.2020.2993906

    Article  Google Scholar 

  29. 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). https://doi.org/10.1109/TIP.2016.2598681

    Article  MathSciNet  MATH  Google Scholar 

  30. Tang, G., Zhao, L., Jiang, R., Zhang, X.: Single image dehazing via lightweight multi-scale networks. In: Proc. IEEE Int. Conf. Big Data (Big Data), pp. 154–169 (2019)

  31. Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: Aod-net: all-in-one dehazing network. In: Proc. IEEE Int. Conf. Computer Vision, pp. 4770–4778 (2017)

  32. Yang, G., Evans, A.N.: Improved single image dehazing methods for resource-constrained platforms. J. Real-Time Image Process. 18, 2511–2525 (2021). https://doi.org/10.1007/s11554-021-01143-6

    Article  Google Scholar 

  33. Jeong, C.Y., Moon, K., Kim, M.: An end-to-end deep learning approach for real-time single image dehazing. J Real-Time Image Process. 20, 12 (2023). https://doi.org/10.1007/s11554-023-01270-2

    Article  Google Scholar 

  34. Karnati, M., Seal, A., Sahu, G., Yazidi, A., Krejcar, O.: A novel multi-scale based deep convolutional neural network for detecting COVID-19 from X-rays. Appl. Soft Comput. 125, 109109 (2022)

    Article  Google Scholar 

  35. Sahu, G., Seal, A., Bhattacharjee, D., Frischer, R., Krejcar, O.: A novel parameter adaptive dual channel MSPCNN based single image dehazing for intelligent transportation systems. IEEE Trans. Intell. Transp. Syst. 24(3), 3027–3047 (2023)

    Article  Google Scholar 

  36. Lee, Y.H., Wu, B.H.: Algorithm and architecture design of a hardware-efficient image dehazing engine. IEEE Trans. Circuits Syst. Video Technol. 29(7), 2146–2161 (2019)

    Article  Google Scholar 

  37. Ancuti, C.O., Ancuti, C., Timofte, R., De Vleeschouwer, C.: O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 754–762 (2018)

  38. Ancuti, C.O., Ancuti, C., Timofte, R., De Vleeschouwer, C.: I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images. In: Proceedings International Conference Advance Concepts Intelligent Visual System. Cham, Switzerland: Springer, 2018, pp. 620-631 (2018).

  39. Li, B., Ren, W., Fu, D., Tao, D., Feng, D., Zeng, W., Wang, Z.: Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process. 28(1), 492–505 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  40. Choi, L.K., You, J., Bovik, A.C.: LIVE Image Defogging Database, Online (2015). http://live.ece.utexas.edu/research/fog/fade_defade.html

  41. Sharma, G., Wu, W., Dalal, E.N.: The CIEDE2000 color-difference formula: implementation notes, supplementary test data, and mathematical observations. Color. Res. Appl. 30(1), 21–30 (2005)

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Special Manpower Development Program for Chip to System Design (SMDP-C2SD) Project, NIT Calicut, by the Ministry of Electronics and Information Technology (MeitY), Govt. of India, for providing research facilities and technical support.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kozhikode, Kerala, India

    Anuja George & E. P. Jayakumar

Authors
  1. Anuja George
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. P. Jayakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AG have made a substantial contribution to the concept, design, and hardware implementation of the proposed algorithm presented in the article. AG drafted the manuscript with the findings of the research work. EPJ provided critical feedback and helped shape the research framework and the analysis. EPJ revised the manuscript critically for important intellectual content. All authors reviewed the manuscript.

Corresponding author

Correspondence to Anuja George.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

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

George, A., Jayakumar, E.P. Design and implementation of hardware-efficient architecture for saturation-based image dehazing algorithm. J Real-Time Image Proc 20, 102 (2023). https://doi.org/10.1007/s11554-023-01356-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11554-023-01356-x

Keywords

Search

Navigation