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Visibility enhancement of fog degraded images using adaptive defogging function

  • 1204: Multimedia Technology for Security and Surveillance in Degraded Vision
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Abstract

In the field of image processing, analyzing fog-affected images is challenging, as their visibility is degraded. In the absence of state-of-the-art image processing techniques to mitigate the impact of high-density fog, an adaptive-function-based image-defogging technique is proposed in this paper. The proposed technique accurately enhances such degraded images by adjusting the contrast and brightness based on a suitable threshold operator. The images are subsequently characterized as foggy or non-foggy on the basis of objective evaluation. The experimental results have proven that the proposed method achieves superior performance in terms of qualitative evaluation on non-reference metric (i.e., in terms of e = 0.468, σ = 0, r = 1.8857) and reference metric (i.e. in terms of MSE = 1580, PSNR = 19.2126, NCC = 0.4873, SC = 0.4684, MD = 60, NAE = 0.2229) compared with nine state-of-the-art dehazing methods. Furthermore, based on the average computational time achieved by the proposed method (0.36 s using a test set of 2000 images), it can be highly suitable for real-time applications.

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References

  1. [Online]. Available: http://www.mkbhowmik.in/sameer.aspx

  2. [Online]. Available: http://www.lcpc.fr/english/products/image-databases/article/fridafoggy-road-image-database

  3. Ancuti CO, Ancuti C (2013) Single image dehazing by multi-scale fusion. IEEE Trans Image Process 22:3271–3282. https://doi.org/10.1109/TIP.2013.2262284

    Article  Google Scholar 

  4. Ansia S, Aswathy AL (2015) Single image haze removal using white balancing and saliency map. Procedia Comput Sci 46:12–19. https://doi.org/10.1016/j.procs.2015.01.042

    Article  Google Scholar 

  5. Anwar MI, Khosla A (2015) Classification of foggy images for vision enhancement. In: Proc. IEEE international conference on signal processing and communication (ICSC). IEEE, India, pp. 233-237. https://doi.org/10.1109/ICSPCom.2015.7150653

  6. Anwar MI, Khosla A (2018) Fog classification and accuracy measurement using SVM. In: Proc. IEEE international conference on secure cyber computing and communication (ICSCCC). IEEE, India, pp. 198-202. https://doi.org/10.1109/ICSCCC.2018.8703365

  7. Colores SS, Aceves IC, Arreguin JMR (2018) Single image dehazing using a multilayer perceptron. J Electronic imaging, SPIE 27(4):043022. https://doi.org/10.1117/1.JEI.27.4.043022

    Article  Google Scholar 

  8. Colores SS, Ramos-Arreguín JM, Pedraza-Ortega JC et al (2019) Efficient single image dehazing by modifying the dark channel prior. J Image Video Proc 66. https://doi.org/10.1186/s13640-019-0447-2

  9. Deng G (2009) An entropy interpretation of the logarithmic image processing model with application to contrast enhancement. IEEE Trans Image Process 18(5):1135–1140. https://doi.org/10.1109/TIP.2009.2016796

    Article  MathSciNet  MATH  Google Scholar 

  10. Enesi I, Miho R (2012) A fast algorithm for contrast restoration of weather degraded images. In: Proc. IEEE International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS). IEEE, Palermo, pp 636–641. https://doi.org/10.1109/CISIS.2012.179

  11. Fang F, Wang T, Wang Y, Zeng T, Zhang G (2020) Variational single image dehazing for enhanced visualization. IEEE Trans Multimed 22:2537–2550. https://doi.org/10.1109/TMM.2019.2958755

    Article  Google Scholar 

  12. Galdran A, Vazquez-Corral J, Pardo D, Bertalmío M (2015) Enhanced variational image dehazing. SIAM J Imaging Sci 8(3):1519–1546. https://doi.org/10.1137/15M1008889

    Article  MathSciNet  MATH  Google Scholar 

  13. Gibson KB, Nguyen TQ (2013) Fast single image fog removal using the adaptive wiener filter. In: Proc. IEEE international conference on image processing (ICIP). IEEE, Melbourne, pp 714-718. https://doi.org/10.1109/ICIP.2013.6738147

  14. Guo JM, Syue JY, Radzicki V et al (2017) An efficient fusion-based defogging. IEEE Trans Image Process 26:4217–4228. https://doi.org/10.1109/TIP.2017.2706526

    Article  MathSciNet  MATH  Google Scholar 

  15. Hautière N, Tarel JP, Aubert D et al (2008) Blind contrast enhancement assessment by gradient ratioing at visible edge. Image AnalStereol 27:87–95. https://doi.org/10.5566/ias.v27.p87-95

    Article  MathSciNet  MATH  Google Scholar 

  16. He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattr Anal Mach Intellig 33:1956–1963. https://doi.org/10.1109/TPAMI.2010.168

    Article  Google Scholar 

  17. Hiramatsu T, Ogawa T, Haseyama M (2008) A kalman filter-based approach for adaptive restoration of in-vehicle camera foggy images. In: Proc. IEEE international conference on image processing (ICIP).IEEE, San Diego, pp. 3160-3163. https://doi.org/10.1109/ICIP.2008.4712466

  18. Hongkun Z, Pucheng Z, Mogen X et al (2011) Single fogged image restoration using improved mean shift filtering. In: Proc. IEEE international congress on image and signal processing (CISP).IEEE, Shanghai, pp 803-806. https://doi.org/10.1109/CISP.2011.6100354

  19. Huang J, Ma Y, Zhang Y, Fan F (2017) Infrared image enhancement algorithm based on adaptive histogram segmentation. Appl Opt 56:9686–9697. https://doi.org/10.1364/AO.56.009686

    Article  Google Scholar 

  20. Imtiyaz MA, Khosla A (2017) Vision enhancement through single image fog removal. Eng Sci Technol 20:1075–1083. https://doi.org/10.1016/j.jestch.2016.11.015

    Article  Google Scholar 

  21. Kang M, Jung M (2020) A single image dehazing model using total variation and inter-channel correlation. Multidim Syst Sign Process 31:431–464. https://doi.org/10.1007/s11045-019-00670-7

    Article  MathSciNet  MATH  Google Scholar 

  22. Kim SE, Park TH, Eom IK (2020) Fast single image dehazing using saturation based transmission map estimation. IEEE Trans Image Process 29:1985–1998. https://doi.org/10.1109/TIP.2019.2948279

    Article  MathSciNet  MATH  Google Scholar 

  23. Kumari A, Thomas PJ, Sahoo SK (2014) Single image fog removal using gamma transformation and median filtering. In: Proc. IEEE annual India conference (INDICON). IEEE, Pune, pp. 1-5. https://doi.org/10.1109/INDICON.2014.7030384

  24. Kumari A, Sahoo SK, Chinnaiah MC (2021) Fast and efficient visibility restoration technique for single image dehazing and defogging. IEEE Access 9:48131–48146. https://doi.org/10.1186/s13640-019-0447-2

    Article  Google Scholar 

  25. Kutter M, Petitcolas FAP (1999) Fair benchmark for image watermarking systems. In: Proc. SPIE conference on security and watermarking of multimedia contents, vol. 3657, United States, pp. 226-239. https://doi.org/10.1117/12.344672

  26. Li J, Wang Y, Sun H et al (2010) Restoration of an atmospherically blurred image based on physical model fusion approach. In: Proc. IEEE International Conference on Signal Processing (ICSP).IEEE, Beijing, pp 801–804. https://doi.org/10.1109/ICOSP.2010.5655931

  27. Li Y, Guo F, Tan RT (2014) A contrast enhancement framework with JPEG artifacts suppression. In: Proc. European conference on computer vision (ECCV). Lecture notes in computer science, vol. 8690, Switzerland, pp. 174-188. https://doi.org/10.1007/978-3-319-10605-2_12

  28. Lin Y, Li H, Wang M (2017) Single image dehazing via large sky region segmentation and multiscale opening dark channel model. IEEE Access 5:8890–8903. https://doi.org/10.1109/ACCESS.2017.2710305

    Article  Google Scholar 

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

    Article  Google Scholar 

  30. Ma J, Chen C, Li C, Huang J (2016) Infrared and visible image fusion via gradient transfer and total variation minimization. Inf Fusion 31:100–109. https://doi.org/10.1016/j.inffus.2016.02.001

    Article  Google Scholar 

  31. Ma J, Yu W, Liang P, Li C, Jiang J (2019) Fusion GAN: a generative adversarial network for infrared and visible image fusion. Inf Fusion 48:11–26. https://doi.org/10.3390/e23030376

    Article  Google Scholar 

  32. Meng G, Wang Y, Duan J et al (2013) Efficient image dehazing with boundary constraint and contextual regularization. In: Proc. IEEE International Conference on Computer Vision (ICCV). IEEE, Sydney, pp 617–624. https://doi.org/10.1109/ICCV.2013.82

  33. Narasimhan SG (2004) Models and algorithms for vision through the atmosphere. Dissertation, Columbia University

  34. Narasimhan SG, Nayar SK (2002) Vision and the atmosphere. J Comp Vis 48(3):233–254. https://doi.org/10.1145/1508044.1508113

    Article  MATH  Google Scholar 

  35. Narasimhan SG, Nayar SK (2003) Contrast restoration of weather degraded images. IEEE Trans Pattr Anal Mach Intellig 25:713–724. https://doi.org/10.1109/TPAMI.2003.1201821

    Article  Google Scholar 

  36. Pal T (2018) Visibility enhancement of fog degraded image sequences on SAMEER TU dataset using dark channel strategy. In: Proc. international conference on computing, communication and networking technologies (ICCCNT). IEEE, Bangalore, pp 1-6. https://doi.org/10.1109/ICCCNT.2018.8494071

  37. Pal T (2019) A fast method for defogging of outdoor visual images. Recent Adv Comput Sci Commun 13:1–13. https://doi.org/10.2174/2213275912666190819105422

    Article  Google Scholar 

  38. Pal T, Bhowmik MK (2018) Quality enhancement of foggy images comprising of large sky region on SAMEER TU dataset. In: Proc. international conference on computing, communication and networking technologies (ICCCNT). IEEE, Bangalore, pp. 1-7. https://doi.org/10.1109/ICCCNT.2018.8493832

  39. Pal T, Bhowmik MK, Ghosh AK (2015) Contrast restoration of fog-degraded image sequences. In: Proc. international conference on soft computing for problem solving (SocPros). Advances in intelligent systems and computing, vol 335, New Delhi, India, pp. 325-338. https://doi.org/10.1007/978-81-322-2217-0_28

  40. Pal T, Bhowmik MK, Ghosh AK (2015) Defogging of visual images using SAMEER-TU database. In: Proc. international conference on information and communication technologies (ICICT). Procedia computer science, vol.46, India, pp.1676-1683. https://doi.org/10.1016/j.procs.2015.02.108

  41. Pal T, Bhowmik MK, Bhattacharjee D et al (2016) Visibility enhancement techniques for fog degraded images: a comparative analysis with performance evaluation. In: Proc. IEEE international conference on Technologies for Smart Nation (TENCON). IEEE, Singapore, pp. 2583-2588. https://doi.org/10.1109/TENCON.2016.7848504

  42. Sakarya O (2015) Applying fuzzy clustering method to color image segmentation. In: Proc. IEEE federated conference on computer science and information systems (FedCSIS). IEEE, Poland, pp. 1049-1054. https://doi.org/10.15439/2015F222

  43. Salazar-Colores S, Ramos-Arreguin J, Pedraza-Ortega J et al (2019) Efficient single image dehazing by modifying the dark channel prior. J Image Video Proc 66:1–8. https://doi.org/10.1186/s13640-019-0447-2

    Article  Google Scholar 

  44. Salazar-Colores S, Cabal-Yepez V, Ramos-Arreguin JM (2019) A fast image dehazing algorithm using morphological reconstruction. IEEE Trans Image Process 28:2357–2366. https://doi.org/10.1109/TIP.2018.2885490

    Article  MathSciNet  Google Scholar 

  45. Sridhar S (2016) Digital image processing. Oxford University Press, India

    Google Scholar 

  46. Tan RT (2008) Visibility in bad weather from a single image. In: Proc. IEEE international conference on communications and signal processing (CVPR).IEEE, Anchorage, pp 1-8. https://doi.org/10.1109/CVPR.2008.4587643

  47. Tarel J, Hautiere N, Caraffa L et al (2012) Vision enhancement in homogeneous and heterogeneous fog. J IEEE Intell Transp Syst Mag 4(2):6–20. https://doi.org/10.1109/MITS.2012.2189969

    Article  Google Scholar 

  48. Tripathi AK, Mukhopadhyay S (2012) Single image fog removal using bilateral filter. In: Proc. IEEE international conference on signal processing, computing and control (ISPCC). IEEE, Waknaghat Solan, pp 1-6. https://doi.org/10.1109/ISPCC.2012.6224342

  49. Wang Y, Wu B (2010) Improved single image dehazing using dark channel prior. In: Proc. IEEE international conference on international conference on intelligent system design and engineering application (ISDEA). IEEE, China, pp 789-792. https://doi.org/10.1109/ISDEA.2010.141

  50. Wang D, Zhu J, Yan F (2016) Dehazing for single image with sky region via self-adaptive weighted least squares model. Int J of Optical Engg 55(4):043106. https://doi.org/10.1117/1.OE.55.4.043106

    Article  Google Scholar 

  51. Wang W, Yuan X, Wu X, Liu Y (2017) Dehazing for images with large sky region. Neurocomputing 238:365–376. https://doi.org/10.1016/j.neucom.2017.01.075

    Article  Google Scholar 

  52. Xiao J, Shen M, Lei J, Zhou J, Klette R, Sui HG (2020) Single image dehazing based on learning of haze layers. Neurocomputing 389:108–122. https://doi.org/10.1016/j.neucom.2020.01.007

    Article  Google Scholar 

  53. Xing Z, Yu L, Xiaoling T et al (2011) A fog-removing method of colorized images based on high pass filtering. In: Proc. IEEE International Symposium on Computational Intelligence and Design (ISCID).IEEE, Hangzhou, pp 99–102. https://doi.org/10.1109/ISCID.2011.126

  54. Xu H, Guo J, Liu Q et al (2012) Fast image dehazing using improved dark channel prior. In: Proc. IEEE International Conference on Information Science and Technology (ICST).IEEE, Hubei, pp 663–667. https://doi.org/10.1109/ICIST.2012.6221729

  55. Yadav SK, Sarawadekar K (2020) Steering kernel-based guided image filter for single image dehazing. In: Proc. IEEE international conference on Technologies for Smart Nation (TENCON). IEEE, Singapore, pp. 444-449. https://doi.org/10.1109/TENCON50793.2020.9293825

  56. Zhai YS, Liu XM (2007) An improved fog-degraded image enhancement algorithm. In: Proc. IEEE international conference on wavelet analysis and pattern recognition (ICWAPR). IEEE, Beijing, pp 522-526. https://doi.org/10.1109/ICWAPR.2007.4420725

  57. Zhang H, Ye Q (2010) Fog-degraded image clearness based on wavelet fusion. In: Proc. IEEE International Conference on Intelligent System Design and Engineering Application (ISDEA). IEEE, Changsha, pp 759–761. https://doi.org/10.1109/ISDEA.2010.312

  58. Zubaidy YA, Salam RA (2013) Removal of atmospheric particles in poor visibility outdoor images. Telkomnika Indones J Electr Eng Comput Sci 11(8). https://doi.org/10.11591/telkomnika.v11i8.2872

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All data included in this study are available from the corresponding author upon request.

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All the codes included in this study are available from the corresponding author upon request.

Funding

The work depicted here has been funded in part by the Grant no: SMR/PD(R)/NER/2012–13/Thermography,Dated 22/03/2013 from the Society of Applied Microwave Electronics Engineering & Research (SAMEER), IIT Bombay, India.

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Tannistha Pal- formal analysis, methodology, validation, data curation, writing. Debotosh Bhattacharjee-conceptualization, Review & Editing, visualization, supervision

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Correspondence to Tannistha Pal.

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Pal, T., Bhattacharjee, D. Visibility enhancement of fog degraded images using adaptive defogging function. Multimed Tools Appl 81, 35317–35347 (2022). https://doi.org/10.1007/s11042-022-12182-x

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