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Real-time fast fog removal approach for assisting drivers during dense fog on hilly roads

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Abstract

Dense fog is a vital cause of risk in driving perception, especially on a hilly road. In hilly areas, drivers are unable to view the roads prominently and sometimes unable to notice a nearby object properly due to dense fog. The dense fog causes many road accidents and also reduces the speed of the vehicle. In this article, a vision-based real-time fast fog removal approach is proposed to obtain a prominent view of the hilly road during heavy fog by video processing techniques. The proposed approach is established on an atmospheric based scattering model and least filtering technique with a dynamic patch followed by independent histogram equalization built on color channels. First, the atmospheric light is estimated at an interval of 6000 frames using the least filtering technique with a dynamic patch. Then, frames are inverted to estimate the transmission map, and fog-free scenes are recovered. Finally, the independent histogram equalization based on color channel is used to eliminate the haze noise with proper contrast adjustment. The proposed approach can provide a fog-free vision of roads as well as any distant object for any driver in minimum processing time. Several real-time exploratory tests are conducted during heavy fog on hilly roads. Qualitative and quantitative test analysis exposes that the proposed approach provides enhanced performance compared to the latest approaches. After defogging, the distance of visibility rises by more than 92% during dense fog with great accuracy of 97.41%.

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References

  • Badhe MV, Ramteke PL (2016) A survey on haze removal using image visibility restoration technique. Int J Comput Sci Mobile Comput 5(2):96–101

    Google Scholar 

  • Bai L, Wu Y, Xie J, Wen P (2015) Real time image haze removal on multi-core DSP. Asia-Pacific International Symposium on Aerospace Technology, China

  • Cai B, Xu X, Jia K (2016) Dehazenet: an end-to-end system for single image haze removal. IEEE Trans Image Process 25(11):5187–5198

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  • Choi KY, Jeong KM, Song BC (2017) Fog detection for de-fogging of road driving images. IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, 2017, pp. 1–6

  • Colores SS, Yepez EC, Arreguin JMR, Botella G, Carrillo LML, Ledesma S (2019) A fast image dehazing algorithm using morphological reconstruction. IEEE Trans Image Process 28(5):2357–2366

    Article  MathSciNet  Google Scholar 

  • Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B (2016) The Cityscapes Dataset for Semantic Urban Scene Understanding. in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas

  • Fattal R (2008) Single Image Dehazing. Proc. ACM SIGGRAPH 08, Los Angeles, California

  • Gu K, Lin W, Zhai G, Yang X, Zhang W, Chen CW (2017) No-reference quality metric of contrast-distorted images based on information maximization. IEEE Trans Cybernetics 47(12):4559–4565

    Article  Google Scholar 

  • 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 

  • 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 

  • Hu HM, Guo Q, Zheng J, Wang H, Li B (2019) Single image defogging based on illumination decomposition for visual maritime surveillance. IEEE Trans Image Process 28(6):2882–2897

    Article  MathSciNet  Google Scholar 

  • Jayaraman S, Esakkirajan S, Veerakumar T (2009) Digital Image Processing. New Delhi, McGraw Hill Education

  • Jeong K, Choi K, Kim D, Song B C (2018) Fast fog detection for de-fogging of road driving images. IEICE Transactions on Information and Systems E101.D (2): 473–480

  • Jiang Y, Sun C, Zhao Y (2017) Fog density estimation and image defogging based on surrogate modeling for optical depth. IEEE Trans Image Process 26(7):3397–3409

    Article  MathSciNet  Google Scholar 

  • Ju M, Ding C, Guo YJ, Zhang D (2019) IDGCP: image dehazing based on gamma correction prior. IEEE Trans Image Process 29:3104–3118

    Article  Google Scholar 

  • Kumari A, Kodati H, Sahoo S K (2015) Fast and efficient contrast enhancement for real time video dehazing and defogging. IEEE Workshop on Computational Intelligence: theories, Applications and Future Directions (WCI), Kanpur, 2015, pp. 1-5

  • Li XG, Jiao WT (2012) A new fog removing method based on the degradation model. Adv Future Comput Control Syst 159:597–601

    Article  Google Scholar 

  • Li B, Ren W, Fu D, Tao D, Feng D, Zeng W, Wang Z (2018) Benchmarking single image dehazing and beyond. IEEE Trans Image Process 14(8):492–505

    MathSciNet  MATH  Google Scholar 

  • Li L et al (2020) Semi-supervised image dehazing. IEEE Trans Image Process 29:2766–2779

    Article  Google Scholar 

  • Lin Z, Wang X (2012) Dehazing for image and video using guided filter. Open J Appl Sci 2(4B):123–127

    Article  Google Scholar 

  • Linan Y, Yan P, Xiaoyuan Y (2012) Video defogging based on adaptive tolerance. TELKOMNIKA 10(7):1644–1654

    Article  Google Scholar 

  • Liu P, Horng S, Lin J, Li T (2019) Contrast in haze removal: configurable contrast enhancement model based on dark channel prior. IEEE Trans Image Process 28(5):2212–2227

    Article  MathSciNet  Google Scholar 

  • Ma ZL, Wen J, Zhang C, Liu QY, Yan DN (2016) An effective fusion defogging approach for single sea fog image. Neurocomputing 173(3):1257–1267

    Article  Google Scholar 

  • Maa N, Xu J, Li H (2018) A fast video haze removal algorithm via dark channel prior. 8th International Congress of Information and Communication Technology, Xiamen, China

  • Makandar A, Patrot A, Halalli B (2014) Color image analysis and contrast stretching using histogram equalization. Int J Adv Inform Sci Technol 27(27):119–125

    Google Scholar 

  • Nenavath H (2014) Implementation of Real-Time Image De-Hazing for Vehicles in Foggy Weather. Global J Adv Eng Technol CTCNSF-2014: 6–9

  • Organization WH (2013) Violence and injury prevention and World Health Organization: global status report on road safety 2013: Supporting a Decade of Action. Global Status Report on Road Safety 2013: Supporting a Decade of Action, Geneve

  • Organization WH (2018) Violence and injury prevention and World Health Organization: global status report on road safety 2018: Supporting a Decade of Action. Global Status Report on Road Safety 2018: Supporting a Decade of Action, Geneve

  • Sejal R, Mitul P (2014) Removal of the fog from the image using filters and colour model. Int J Eng Res Technol (IJERT) 3(1):253–257

    Google Scholar 

  • Shuai Y, Liu R, He W (2012) Image Haze Removal of Wiener Filtering Based on Dark Channel Prior. Eighth International Conference on Computational Intelligence and Security, Shuai

  • Sridhar S (2011) Digital Image Processing. Second Edition. Oxford University Press India

  • Tian Y, Xiao C, Chen X, Yang D, Chen Z (2016) Haze removal of single remote sensing image by combining dark channel prior with superpixel. International Symposium on Electronic Imaging 2016: Visual Information Processing and Communication VII, California, USA

  • Toka V, Sankaramurthy NH, Kini RPM, Avanigadda PK, Kar S (2016) A fast method of fog and haze removal. International Conference on Acoustics, Speech, and Signal Processing, Lujiazui, Shanghai, China

  • Transport Research Wing M R T H: Government of India (2017) Road Accidents in India– 2017. New Delhi

  • Tripathi A K, Mukhopadhyay S (2012) Single Image Fog Removal Using Bilateral Filter. IEEE International Conference on Signal Processing, Computing and Control, Waknaghat Solan, India

  • Tufail Z, Khurshid K, Salman A, Nizami IF, Khurshid K, Jeon B (2018) Improved dark channel prior for image defogging using RGB and YCbCr color space. IEEE Access 6:32576–32587

    Article  Google Scholar 

  • Wang W, Li Z, Wu S, Zeng L (2020) Hazy image decolorization with color contrast restoration. IEEE Trans Image Process 29:1776–1787

    Article  MathSciNet  Google Scholar 

  • Wu Q, Ren W, Cao X (2020) Learning interleaved cascade of shrinkage fields for joint image dehazing and denoising. IEEE Trans Image Process 29:1788–1801

    Article  MathSciNet  Google Scholar 

  • Xu Z, Liu X, Ji N (2009) Fog removal from color images using contrast limited adaptive histogram equalization. 2nd International Congress on Image and Signal Processing, Tianjin, China

  • Yang M, Liu J, Li Z (2018) Super-pixel based single nighttime image haze removal. IEEE Trans Multimedia 20(11):3008–3018

    Article  Google Scholar 

  • Yawale RP, Kapse AS (2016) Digital image defogging using dark channel prior and histogram stretching method. Int J Adv Res Comput Commun Eng 5(4):889–894

    Google Scholar 

  • Yeh WC, Chen BH, Wang WJ (2014) Visibility restoration of single hazy images captured in real-world weather conditions. IIEEE Trans Circuits Syst Video Technol 24(10):1814–1824

    Article  Google Scholar 

  • Yeh C, Huang C, Kang L (2020) Multi-scale deep residual learning-based single image haze removal via image decomposition. IEEE Trans Image Process 29:3153–3167

    Article  Google Scholar 

  • Zhang J, Tao D (2020) FAMED-Net: a fast and accurate multi-scale end-to-end dehazing network. IEEE Trans Image Process 29:72–84

    Article  MathSciNet  Google Scholar 

  • Zhou Li, Bi DY, He LY (2016) Variational histogram equalization for single color image defogging. Math Problems Eng 2016:1–17

    MathSciNet  MATH  Google Scholar 

  • 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 

Download references

Acknowledgements

The authors would like to acknowledge the National Institute of Technology Agartala, Tripura, India for providing a world-class research environment including a research laboratory.

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  1. Department of Computer Science and Engineering, National Institute of Technology Agartala, Agartala, Tripura, India

    Gouranga Mandal, Parthasarathi De & Diptendu Bhattacharya

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  1. Gouranga Mandal
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  2. Parthasarathi De
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  3. Diptendu Bhattacharya
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Correspondence to Gouranga Mandal.

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Mandal, G., De, P. & Bhattacharya, D. Real-time fast fog removal approach for assisting drivers during dense fog on hilly roads. J Ambient Intell Human Comput 12, 9877–9889 (2021). https://doi.org/10.1007/s12652-020-02734-0

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