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Single image haze removal using contrast limited adaptive histogram equalization based multiscale fusion technique

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Abstract

Outdoor photography is often affected by a common problem termed haze which causes poor visibility of distant objects. This occurs mainly due to the absorption and scattering of light by atmospheric particles. To mitigate the issue, this paper proposes a contrast-limited adaptive histogram equalization based multiscale fusion (CLAHEMSF) technique where two images are derived by applying white balance and contrast limited adaptive histogram equalization technique from a hazy image. Although the quality of the image is improved, it still suffers from the noise problem inherent due to adaptive histogram equalization and lower visibility in dense hazy regions. Hence, controlled contrast enhancement inhomogeneous areas are leveraged to overcome the prior issue while the latter one is taken care of by utilizing luminance, chromatic, and saliency weight maps along with an effective fusion technique. Moreover, the guided image filter is employed to preserve the structure and smoothing, which generates images with enhanced visibility as observed after various experimentation. Several empirical analyses are carried out on a large variety of images from hazy datasets, including HazeRD datasets. The proposed technique is evaluated using qualitative and quantitative analyses and obtained the mean square error, structural similarity, and peak signal-to-noise ratio of 1788.63, 0.848, and 15.143, respectively. The proposed technique can achieve enhanced visibility with preserved structure and vivid color compared to other state-of-the-art methods. The algorithm can also be used in several applications such as surveillance, traffic monitoring, runway hazard detection, obstacle tracking, etc.

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References

  1. Achanta R, Hemami S, Estrada F (2009) Frequency-tuned salient region detection. In: IEEE Conference on computer vision and pattern recognition, pp 1597–1604

  2. Al-Ameen Z (2016) Visibility enhancement for images captured in dusty weather via tuned tri-threshold fuzzy intensification operators. Int J Intell Syst Appl 8(8):10–17

    Google Scholar 

  3. Ancuti CO, Ancuti C (2013) Single image dehazing by multi-scale fusion. IEEE Trans Image Process 22:3271–3282

    Article  Google Scholar 

  4. Berman D, Treibitz T, Avidan S (2016) Non-local image dehazing. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 1674–1682

  5. Buchsbaum G (1980) A spatial processor model for object colour perception. J. Franklin Inst. 310(1):1–26

    Article  Google Scholar 

  6. Burt P J, Adelson E H (1983) The laplacian pyramid as a compact image code. IEEE Trans Commun 31(4):532–540

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  8. Chaudhury KN, Sage D, Unser M (2011) Fast 0(1) bilateral filtering using trigonometric range kernels. IEEE Trans Image Process 20 (12):3376–3382

    Article  MathSciNet  Google Scholar 

  9. Ebner M (2007) Color constancy. Wiley, first edn

  10. Fan X, Wang L (2019) Image defogging approach based on incident light frequency. Multimedia Tools Appl 78(13):17653–17672

    Article  Google Scholar 

  11. Fattal R (2008) Single image dehazing. ACM Trans Graph 27(3):9

    Article  Google Scholar 

  12. Finlayson GD, Trezzi E (2004) Shades of gray and colour constancy.. In: Twelfth color imaging conference: Color science and engineering systems, technologies, and applications, pp 37–41

  13. Guo F, Tang JP, Cai Z (2013) Fusion strategy for single image dehazing. International Journal of Digital Content Technology and its Applications 7 (1):19–28

    Article  Google Scholar 

  14. Hautiere N, Tarel J P, Aubert D, et al. (2008) Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal Stereology 27(2):87–95

    Article  MathSciNet  Google Scholar 

  15. 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 

  16. He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(16):1397–1409

    Article  Google Scholar 

  17. He LY, Zhao JZ, Bi DY (2018) Effective haze removal under mixed domain and retract neighborhood. Neurocomputing 293:29–40

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Huang S, Liu Y, Wang Y et al (2020) A new haze removal algorithm for single urban remote sensing image. IEEE Access 8:100870–100889

    Article  Google Scholar 

  20. Jiang B, Meng H, Zhao J, Ma X, Jiang S, Wang L, Zhou Y, Ru Y, Ru C (2017) Single image fog and haze removal based on self-adaptive guided image filter and color channel information of sky region. Multimed Tools Appl 77:1–18

    Google Scholar 

  21. Koschmieder H (1924) Theorie der horizontalen Sichtweite. no. v. 2 in Beitrage zur Physik der freien Atmosphare. Keim & Nemnich:171–181

  22. Li Y, Guo F, Tan RT, Brown MS (2014) A contrast enhancement framework with JPEG artifacts suppression. In: Computer vision – ECCV 2014, pp 174–188

  23. Li Y, Tan RT, Brown MS (2015) Nighttime haze removal with glow and multiple light colors. In: IEEE International conference on computer vision (ICCV), pp 226–234

  24. Liu P, Horng S, Lin J, et al. (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 

  25. Liu W, Chen X, Chu X, Wu Y, Lv J (2016) Haze removal for a single inland waterway image using sky segmentation and dark channel prior. IET Image Process 10(12):468–481

    Article  Google Scholar 

  26. Luan Z, Shang Y, Zhou X, et al. (2017) Fast single image dehazing based on a regression model. Neurocomputing 245:10–22

    Article  Google Scholar 

  27. Meng G, Wang Y, Duan J, Xiang S, Pan C (2013) Efficient image dehazing with boundary constraint and contextual regularization. In: 2013 IEEE International conference on computer vision, pp 617–624

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

    Article  Google Scholar 

  29. Mondal K, Rabidas R, Dasgupta R, Midya A, Chakraborty J (2020) Enhancement of hazy images using atmospheric light estimation technique. J Circuits Syst Comput 30(5):2150078

    Article  Google Scholar 

  30. Ren W, Liu S, Zhang H, Pan J, Cao X, Yang MH (2016) Single image dehazing via multi-scale convolutional neural networks. In: European conference on computer vision, pp 154–169

  31. Tan RT (2008) Visibility in bad weather from a single image. In: IEEE Conference on computer vision and pattern recognition, pp 1–8

  32. Tarel JP, Hautire N, Cord A, Gruyer D, Halmaoui H (2010) Improved visibility of road scene images under heterogeneous fog. IEEE Intell Veh Symp:478 –485

  33. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Sixth international conference on computer vision, pp 839–846

  34. van de WJ, Gevers T (2005) Color constancy based on the grey-edge hypothesis. In: Proc. IEEE Int. conf. image process., pp 722–725

  35. Wang C, Zhu B (2020) Image segmentation and adaptive contrast enhancement for haze removal. In: IEEE 63rd International midwest symposium on circuits and systems, pp 1036–1039

  36. Wang JB, He N, Zhang LL, et al. (2015) Single image dehazing with a physical model and dark channel prior. Neurocomputing 149(PB):718–728

    Article  Google Scholar 

  37. Wang Z, Bovik A C, Sheikh H R, et al. (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  38. Wang Z, Luo J, Qin K, Li H, Li G (2017) Model based edge-preserving and guided filter for real-world hazy scenes visibility restoration. Cognit Comput 9(4):468–481

    Article  Google Scholar 

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

    Article  Google Scholar 

  40. Zhang Y, Ding L, Sharma G (2017) An outdoor scene dataset and benchmark for single image dehazing. In: IEEE International Conference on Image Processing (ICIP), pp 3205–3209

  41. 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 

  42. Zhu Y, Tang G, Zhang X, Jiang J, Tian Q (2018) Haze removal method for natural restoration of images with sky. Neurocomputing 275:499–510

    Article  Google Scholar 

  43. Zuiderveld K (1994) Contrast limited adaptive histogram equalization. Academic Press, Inc

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Authors and Affiliations

  1. Department of Electronics and Instrumentation Engineering, National Institute of Technology Silchar, Assam, 788010, India

    Kalimuddin Mondal & Rajdeep Dasgupta

  2. Department of Electronics and Communication Engineering, Assam University, Assam, 788011, India

    Rinku Rabidas

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  1. Kalimuddin Mondal
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  2. Rinku Rabidas
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  3. Rajdeep Dasgupta
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Correspondence to Kalimuddin Mondal.

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Mondal, K., Rabidas, R. & Dasgupta, R. Single image haze removal using contrast limited adaptive histogram equalization based multiscale fusion technique. Multimed Tools Appl 83, 15413–15438 (2024). https://doi.org/10.1007/s11042-021-11890-0

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