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A generic post-processing framework for image dehazing

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Abstract

There are several methods available for image dehazing. The challenges faced by most of these algorithms include under-exposure and leftover haze after dehazing, which eventually leads to low brightness and low contrast, respectively. Therefore, to overcome these drawbacks a post-processing method is required for image dehazing. Some post-processing techniques are implemented earlier for image dehazing such as contrast-limited adaptive histogram equalization, exposure enhancement, and adaptive tone remapping. However, these algorithms may not be applicable to all the dehazing methods as they may produce over-exposed, over-enhanced, and under-enhanced results. Hence, a generic post-processing (GPP) model is needed that can be applied to any dehazing algorithm and also overcome the drawbacks of the previous dehazing and post-processing techniques. A GPP framework is proposed in this paper which adaptively enhances the dehazed image based on its quality. The image quality assessment parameters called normalized brightness difference and haze score are introduced in this paper to detect the under-exposure and leftover haze in the dehazed images, respectively. If a dehazed image exhibits under-exposure, its brightness has to be improved. The fast low-light image enhancement is proposed to enhance the brightness of the under-exposed image. The contrast enhancement algorithm has to be applied when a hazy image exhibits leftover haze. The introduction of the proposed post-processing model adaptively enhances the dehazed image and also brings significant improvements for any dehazing method in terms of quantitative and qualitative assessments.

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References

  1. Huang, S.C., Chen, B.H., Cheng, Y.J.: An efficient visibility enhancement algorithm for road scenes captured by intelligent transportation systems. IEEE Trans. Intell. Transp. Syst. 15(5), 2321–2332 (2014)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  4. Li, Z., Tan, P., Tan, R.T., Zou, D., Zhiying Zhou, S., Cheong, L.F.: Simultaneous video defogging and stereo reconstruction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4988–4997 (2015)

  5. Choi, L.K., You, J., Bovik, A.C.: Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans. Image Process. 24(11), 3888–3901 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  6. Zhu, Z., Wei, H., Hu, G., Li, Y., Qi, G., Mazur, N.: A novel fast single image dehazing algorithm based on artificial multiexposure image fusion. IEEE Trans. Instrum. Meas. 70, 1–23 (2020)

    Article  Google Scholar 

  7. He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2010)

    Google Scholar 

  8. Berman, D., Avidan, S.: Non-local image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1674–1682 (2016)

  9. 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 

  10. Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 617–624 (2013)

  11. Bui, T.M., Kim, W.: Single image dehazing using color ellipsoid prior. IEEE Trans. Image Process. 27(2), 999–1009 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  12. Cho, Y., Jeong, J., Kim, A.: Model-assisted multiband fusion for single image enhancement and applications to robot vision. IEEE Robot. Autom. Lett. 3(4), 2822–2829 (2018)

    Google Scholar 

  13. Ngo, D., Lee, S., Kang, B.: Robust single-image haze removal using optimal transmission map and adaptive atmospheric light. Remote Sensing 12(14), 2233 (2020)

    Article  Google Scholar 

  14. Huo, F., Zhu, X., Zeng, H., Liu, Q., Qiu, J.: Fast fusion-based dehazing with histogram modification and improved atmospheric illumination prior. IEEE Sens. J. 21(4), 5259–5270 (2020)

    Article  Google Scholar 

  15. 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  MATH  Google Scholar 

  16. Haouassi, S., Wu, D.: Image dehazing based on (CMT net) cascaded multi-scale convolutional neural networks and efficient light estimation algorithm. Appl. Sci. 10(3), 1190 (2020)

    Article  Google Scholar 

  17. Koschmieder, H.: Theorie der horizontalen Sichtweite. Beitrage zur Physik der freien Atmosphare, pp. 33–53 (1924)

  18. Zhao, D., Xu, L., Yan, Y., Chen, J., Duan, L.Y.: Multi-scale optimal fusion model for single image dehazing. Signal Process. Image Commun. 74, 253–265 (2019)

    Article  Google Scholar 

  19. Kapoor, R., Gupta, R., Son, L.H., Kumar, R., Jha, S.: Fog removal in images using improved dark channel prior and contrast limited adaptive histogram equalization. Multimed. Tools Appl. 78(16), 23281–23307 (2019)

    Article  Google Scholar 

  20. Ngo, D., Lee, S., Nguyen, Q.H., Ngo, T.M., Lee, G.D., Kang, B.: Single image haze removal from image enhancement perspective for real-time vision-based systems. Sensors 20(18), 5170 (2020)

    Article  Google Scholar 

  21. Zuiderveld, K.: Contrast limited adaptive histogram equalization. Graphics gems 474–485 (1994)

  22. Cho, H., Kim, G.J., Jang, K., Lee, S., Kang, B.: Color image enhancement based on adaptive nonlinear curves of luminance features. JSTS J. Semicond. Technol. Sci. 15(1), 60–67 (2015)

    Article  Google Scholar 

  23. Ancuti, C.O., Ancuti, C., Timofte, R.: NH-HAZE: an image dehazing benchmark with non-homogeneous hazy and haze-free images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 444–445 (2020)

  24. Zhao, S., Zhang, L., Huang, S., Shen, Y., Zhao, S.: Dehazing evaluation: real-world benchmark datasets, criteria, and baselines. IEEE Trans. Image Process. 29, 6947–6962 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  25. Ancuti, C.O., Ancuti, C., Sbert, M., Timofte, R.: Dense-haze: a benchmark for image dehazing with dense-haze and haze-free images. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 1014–1018, IEEE (2019)

  26. Ancuti, C., Ancuti, C.O., Timofte, R., Vleeschouwer, C.D.: I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images. In: International Conference on Advanced Concepts for Intelligent Vision Systems, Springer, Cham, pp. 620–631 (2018)

  27. 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 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  28. 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)

  29. Varga, D.: No-reference image quality assessment with convolutional neural networks and decision fusion. Appl. Sci. 12(1), 101 (2018)

    Article  MathSciNet  Google Scholar 

  30. Yan, J., Li, J., Fu, X.: No-reference quality assessment of contrast-distorted images using contrast enhancement. Preprint http://arxiv.org/abs/1904.08879 (2019)

  31. Ngo, D., Lee, G.D., Kang, B.: Haziness degree evaluator: a knowledge-driven approach for haze density estimation. Sensors 21(11), 3896 (2021)

    Article  Google Scholar 

  32. Ying, Z., Li, G., Gao, W.: A bio-inspired multi-exposure fusion framework for low-light image enhancement. Preprint http://arxiv.org/abs/1711.00591 (2017)

  33. Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital image processing using MATLAB. Pearson Education India (2004)

  34. Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2012)

    Article  Google Scholar 

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Authors and Affiliations

  1. ECE Department, National Institute of Technology, Kurukshetra, Haryana, 136119, India

    Balla Pavan Kumar, Arvind Kumar & Rajoo Pandey

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  1. Balla Pavan Kumar
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  2. Arvind Kumar
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  3. Rajoo Pandey
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Kumar, B.P., Kumar, A. & Pandey, R. A generic post-processing framework for image dehazing. SIViP 17, 3183–3191 (2023). https://doi.org/10.1007/s11760-023-02540-z

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