Skip to main content
SpringerLink
Log in

The Retinex-based image dehazing using a particle swarm optimization method

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

To the best of our knowledge, currently the physical model based method is still an ill posed problem. Additionally, the image enhancement approaches also suffer from the texture preservation issue. Retinex-based approach is proved its effectiveness in image dehazing while the parameter should be turned properly. Therefore, in this paper, the particle swarm optimization (PSO) algorithm is firstly performed to optimize the parameter and the hazed image is converted into hue, saturation, intensity(HSI) for color compensation, In the other hand, the multi-scale local detail upgrading and the bilateral filtering approaches are designed to overcome the dehazing artefacts and edge preservation, which could further improve the overall visual effect of images. Experimental results on natural and synthetic images by using qualitative analysis and frequently used quantitative evaluation metrics illustrate the approving defogging effect of the proposed method. For instance, in a natural image road, our method achieves the higher e for 0.63, γ for 3.21 and H for 7.81, respectively and lower σ for 0.04. In a synthetic image poster, the higher PSNR for 18.17 and SSIM for 0.78 are also acquired compared to other explored approaches in this paper. Besides, the results performed on other underwater and aerial images in this study further demonstrates its defog effectiveness.

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

Similar content being viewed by others

References

  1. Bhutada GG, Anand RS, Saxena SC (2012) PSO-based learning of sub-band adaptive thresholding function for image denoising. SIViP 6(1):1–7

    Article  Google Scholar 

  2. Chang J, Bai J (2015) An image enhancement algorithm based on Gaussian weighted bilateral filtering and retinex theory. In: 2015 8th International Congress on Image and Signal Processing (CISP), IEEE, pp 257-262

  3. Choi D H, Jang I H, Kim M H, Kim N C (2007) Color image enhancement based on single-scale retinex with a JND-based nonlinear filter. In: 2007 IEEE International Symposium on Circuits and Systems, pp 3948-3951

  4. Choi H Y, Ko J, Hong WK (n.d.) Combining dehazing and retinex for visibility enhancement

  5. Chuangbai X, Hongyu Z, Jing Y, Pu Y (2015) Traffic image defogging method based on wls. Infrared Laser Eng 3:052

    Google Scholar 

  6. El Khoury J, Le Moan S, Thomas JB, Mansouri A (2017) Color and sharpness assessment of single image dehazing. Multimed Tools Appl

  7. Elad M (2005) Retinex by two bilateral filters. In: International Conference on Scale-Space Theories in Computer Vision, pp. 217–229.

  8. Eriksson B (2007) Automatic image de-weathering using curvelet-based vanishing point detection. Commun Pure Appl Math 219-232

  9. Fan T, Li C, Ma X, Chen Z, Zhang X, Chen L (2017) An improved single image defogging method based on Retinex. In: 2017 2nd International Conference on Image, Vision and Computing (ICIVC). IEEE, pp 410–413

  10. Fang S, Yang JR, Cao Y, Wu PF, Rao RZ (2012) Local multi-scale Retinex algorithm based on guided image filtering. J Image Graph 17(7):748–755

    Google Scholar 

  11. Fattal R (2008) Single image dehazing. ACM Trans Graph 27(3):72

    Article  Google Scholar 

  12. Fattal R (2014) Dehazing using color-lines. TOG 34(1):13

    Article  Google Scholar 

  13. Goncalves L T, Gaya J D O, Drews P, Botelho S S D C (2017) Deepdive: an end-to-end dehazing method using deep learning. In: 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) pp 436-441

  14. Guo JM, Syue JY, Radzicki V, Lee H (n.d.) An efficient fusion-based defogging. IEEE Trans Image Process 26(9):4217–4228

  15. Hautiere N, Tarel JP, Aubert D, Dumont E (2011) Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal Stereol 27(2):87–95

    Article  MathSciNet  Google Scholar 

  16. Hazlyna H N, Mashor M Y, Mokhtar N R, Salihah A A, Hassan R, Raof R A A, Osman M K (2010). Comparison of acute leukemia image segmentation using HSI and RGB color space. In: 10th International Conference on Information Science, Signal Processing and Their Applications (ISSPA) pp 749-752

  17. He K, Sun J, Tang X (2009) Single image haze removal using dark channel prior. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 2341–2353

  18. Hines G, Rahman Z U, Jobson D, Woodell G (2005) Single-scale retinex using digital signal processors

  19. Hu X, Gao X, Wang H (2014) A novel retinex algorithm and its application to fog-degraded image enhancement. Sensors Transducers 175(7):138

    Google Scholar 

  20. Jobson DJ, Rahman Z, Woodell GA (1997) A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans Image Process 6(7):965–976

    Article  Google Scholar 

  21. Lahmiri S, Boukadoum M (2016) Combined partial differential equation filtering and particle swarm optimization for noisy biomedical image segmentation. In: 7th IEEE Latin American Symposium on Circuits & Systems, pp 363-366

  22. Land E (1983) Recent advances in Retinex theory and some implications for cortieal computations: color vision and the natural image. Proc Natl Acad Sci U S A 80(16):5163–5169

    Article  Google Scholar 

  23. Li J (2013) Application of image enhancement method for digital images based on Retinex theory. Optik 124(23):5986–5988

    Article  Google Scholar 

  24. Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295

    Article  Google Scholar 

  25. Liao X, Li K, Yin J (2017) Separable data hiding in encrypted image based on compressive sensing and discrete fourier transform[J]. Multimed Tools Appl 76(20):20739–20753

    Article  Google Scholar 

  26. Liao X, Guo S, Yin J, Wang H, Li X, Sangaiah AK (2018) New cubic reference table based image steganography. Multimed Tools Appl 77(8):10033–10050

    Article  Google Scholar 

  27. Ling Z, Fan G, Wang Y, Lu X (2016) Learning deep transmission network for single image dehazing. In: IEEE International Conference on Image Processing pp. 2296–2300.

  28. Liu L, Wang H (2011) Image enhancement using a nonlinear method with an improved single-scale Retinex algorithm. In: 2011 International Conference on Electronics, Communications and Control (ICECC), IEEE, pp. 2086–2089

  29. Long J, Shi Z, Tang W, Zhang C (2014) Single remote sensing image dehazing. IEEE Geosci Remote Sens Lett 11(1):59–63

    Article  Google Scholar 

  30. McCartney EJ (1976) Optics of the atmosphere: scattering by molecules and particles, nyjw

  31. Negru M, Nedevschi S, Peter RI (2015) Exponential contrast restoration in fog conditions for driving assistance. IEEE Trans Intell Transp Syst 16(4):2257–2268

    Article  Google Scholar 

  32. Nishino K, Kratz L, Lombardi S (2012) Bayesian defogging. Int J Comput Vis 98(3):263–278

    Article  MathSciNet  Google Scholar 

  33. Paris S, Kornprobst P, Tumblin J (2009) Bilateral filtering. Int J Numer Methods Eng 63(13):1911–1938

    MATH  Google Scholar 

  34. Rong Z, Jun WL (2014) Improved wavelet transform algorithm for single image dehazing. Optik-Int J Light Electron Opt 125(13):3064–3066

    Article  Google Scholar 

  35. Scharstein D, Szeliski R (2002) A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int J Comput Vis 47(1–3):7–42

    Article  Google Scholar 

  36. Shrivastava A, Jain S (2016) Single image dehazing based on one dimensional linear filtering and adaptive histogram equalization method. In: 2016 International Conference on Electrical, Electronics, and Optimization Techniques, pp. 4074-4078

  37. Singh D, Kumar V(2018) Single image haze removal using integrated dark and bright channel prior. Modern Phys Lett B 1850051

  38. Tian B, Li Y, Li B, Wen D (2013) Rear-view vehicle detection and tracking by combining multiple parts for complex urban surveillance. IEEE Trans Intell Transp Syst 15(2):597–606

    Article  Google Scholar 

  39. Tu Z, Bai X (2009) Auto-context and its application to high-level vision tasks and 3d brain image segmentation. IEEE Trans Pattern Anal Mach Intell 32(10):1744–1757

    Google Scholar 

  40. Wang S, Zheng J, Hu HM, Li B (2013) Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Trans Image Process 22(9):3538–3548

    Article  Google Scholar 

  41. Wang Y, Wang H, Yin C, Dai M (2016) Biologically inspired image enhancement based on Retinex. Neurocomputing 177:373–384

    Article  Google Scholar 

  42. Wu J, Wang ZW, Fang ZX (2009) Application of retinex in color restoration of image enhancement to night image. In: 2009 2nd International Congress on Image and Signal Processing. IEEE, pp 1-4

  43. Yun SH, Kim JH, Kim S (2010) Image enhancement using a fusion framework of histogram equalization and Laplacian pyramid. IEEE Trans Consum Electron 56(4):2763–2771

    Article  MathSciNet  Google Scholar 

  44. Zhou J, Zhou F (2013) Single image dehazing motivated by Retinex theory. In: 2013 2nd international symposium on instrumentation and measurement, sensor network and automation (IMSNA). IEEE pp 243-247

  45. Zhou C, Gao HB, Gao L, Zhang WG (2003) Particle swarm optimization (PSO) algorithm. Appl Res Comput 12:7–11

    Google Scholar 

Download references

Acknowledgements

This work was supported by Guangzhou Science and Technology Project (201904010107), Guangdong Provincial Natural Science Foundation of China (2019A1515010793), Guangdong Province Science and Technology Project (2016B090918071), and National Natural Science Foundation of China (61072028).

Author information

Authors and Affiliations

  1. School of Physics and Telecommunications Engineering, South China Normal University, Guangzhou, 510006, Guangdong, China

    Li-Ping Yao & Zhong-liang Pan

  2. Guangdong Institute of Medical Instruments, Guangzhou, 510000, Guangdong, China

    Li-Ping Yao

Authors
  1. Li-Ping Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhong-liang Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhong-liang Pan.

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

Yao, LP., Pan, Zl. The Retinex-based image dehazing using a particle swarm optimization method. Multimed Tools Appl 80, 3425–3442 (2021). https://doi.org/10.1007/s11042-020-09812-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09812-7

Keywords

Search

Navigation