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Analysis and selection of haze-relevant features for haze detection

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Abstract

Due to smog, haze, fog, etc., the visibility of outdoor images degrades significantly. Limited visibility leads to the failure of many computer vision applications like tracking an object, intelligent transportation, etc. Many image dehazing methods have been developed to resolve this problem. But, most of the existing dehazing techniques are applied directly to the image regardless of the presence or absence of haze, which results in image deterioration. For real-world applications, it is vital to know whether the obtained image needs to be processed by dehazing methods. Hence, haze detection plays an essential role. Most of the existing techniques of haze detection used multiple features without considering their need. Thus, the proposed method presents a study analyzing different haze-relevant features. The main contributions of the proposed approach include: (i) by using haze-relevant features on RESIDE and NH-Haze datasets, a proposed dataset is prepared. (ii) analysis of features is done using multiple feature selection methods(iii) mapping between selected features and classification models. The results demonstrate that a set of features performs better when compared with another set of features.

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References

  1. Akhtar F, Li J, Pei Y et al (2019) Optimal features subset selection for large for gestational age classification using gridsearch based recursive feature elimination with cross-validation scheme. In: International conference on frontier computing. Springer, pp 63–71

  2. Ancuti CO, Ancuti C, Hermans C et al (2010) A fast semi-inverse approach to detect and remove the haze from a single image. In: Asian conference on computer vision. Springer, pp 501–514

  3. Ancuti CO, Ancuti C, Timofte R (2020) 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

  4. Chen B H, Huang S C, Cheng F C (2016) A high-efficiency and high-speed gain intervention refinement filter for haze removal. J Disp Technol 12 (7):753–759

    Article  Google Scholar 

  5. Farge M (1992) Wavelet transforms and their applications to turbulence. Ann Rev Fluid Mech 24(1):395–458

    Article  MathSciNet  MATH  Google Scholar 

  6. Hasler D, Suesstrunk SE (2003) Measuring colorfulness in natural images. In: Human vision and electronic imaging VIII, international society for optics and photonics. pp 87–95

  7. 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 1956–1963

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

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  10. Kopf J, Neubert B, Chen B et al (2008) Deep photo: model-based photograph enhancement and viewing. ACM Trans Graph (TOG) 27(5):1–10

    Article  Google Scholar 

  11. Koschmieder H (1924) Theorie der horizontalen sichtweite. Beitrage zur Physik der freien Atmosphare. pp 33–53

  12. Kursa M B, Rudnicki W R et al (2010) Feature selection with the boruta package. J Stat Softw 36(11):1–13

    Article  Google Scholar 

  13. Li K, Chen H, Zhang S et al (2018) An svm based technology for haze image classification. Electron Opt Control 25(3):37–41

    Google Scholar 

  14. Li B, Ren W, Fu D et al (2018) Benchmarking single-image dehazing and beyond. IEEE Trans Image Process 28(1):492–505

    Article  MathSciNet  MATH  Google Scholar 

  15. Mittal A, Moorthy A K, Bovik A C (2012) No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 21(12):4695–4708

    Article  MathSciNet  MATH  Google Scholar 

  16. McCartney EJ (1976) Optics of the atmosphere: scattering by molecules and particles. New York

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

    Article  Google Scholar 

  18. Pan X, Xie F, Jiang Z et al (2015) Haze removal for a single remote sensing image based on deformed haze imaging model. IEEE Signal Process Lett 22(10):1806–1810

    Article  Google Scholar 

  19. Pizer S M, Amburn E P, Austin J D et al (1987) Adaptive histogram equalization and its variations. Comput Vis Graph Image Process 39 (3):355–368

    Article  Google Scholar 

  20. Schechner Y Y, Narasimhan S G, Nayar S K (2003) Polarization-based vision through haze. Appl Opt 42(3):511–525

    Article  Google Scholar 

  21. Shannon C E (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423

    Article  MathSciNet  MATH  Google Scholar 

  22. Shi H, Wang Q, Xie L (2016) A method of automatic detection of fog image based on svm classification. Revista de la Facultad de Ingenierí,a 31 (9):211–218

    Google Scholar 

  23. Sun W (2013) A new single-image fog removal algorithm based on physical model. Optik 124(21):4770–4775

    Article  Google Scholar 

  24. Tripathi A K, Mukhopadhyay S (2012) Single image fog removal using anisotropic diffusion. IET Image Process 6(7):966–975

    Article  MathSciNet  Google Scholar 

  25. Varga D (2021) No-reference image quality assessment with global statistical features. J Imaging 7(2):29

    Article  Google Scholar 

  26. Voicu L I, Myler H R, Weeks A R (1997) Practical considerations on color image enhancement using homomorphic filtering. J Electron Imaging 6 (1):108–113

    Article  Google Scholar 

  27. Xie B, Guo F, Cai Z (2010) Improved single image dehazing using dark channel prior and multi-scale retinex. In: 2010 international conference on intelligent system design and engineering application. IEEE, pp 848–851

  28. Xu H, Guo J, Liu Q et al (2012) Fast image dehazing using improved dark channel prior. In: 2012 IEEE international conference on information science and technology. IEEE, pp 663–667

  29. Yang HY, Chen PY, Huang CC et al (2011) Low complexity underwater image enhancement based on dark channel prior. In: 2011 second international conference on innovations in bio-inspired computing and applications. IEEE, pp 17–20

  30. Yu X, Xiao C, Deng M et al (2011) A classification algorithm to distinguish image as haze or non-haze. In: 2011 Sixth International Conference on Image and Graphics, IEEE, pp 286–289

  31. Zhang Y, Sun G, Ren Q et al (2013) Foggy images classification based on features extraction and svm. In: Proceeding of 2013 International Conference on Software Engineering and Computer Science, pp 142–14

  32. Zhao Z, Zhang R, Cox J et al (2013) Massively parallel feature selection: an approach based on variance preservation. Mach Learn 92(1):195–220

    Article  MathSciNet  MATH  Google Scholar 

  33. Zhou C, Wieser A (2018) Jaccard analysis and lasso-based feature selection for location fingerprinting with limited computational complexity. In: LBS 2018: 14th international conference on location based services. Springer, pp 71–87

  34. 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  MATH  Google Scholar 

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Acknowledgements

The authors are grateful to [3] for giving permission to use NH-Haze dataset which is available at https://data.vision.ee.ethz.ch/cvl/ntire20/nh-haze/. We are also thankful to [14] for RESIDE dataset.Reside Dataset is also available at https://www.kaggle.com/datasets/balraj98/indoor-training-set-its-residestandard. We also thank the reviewers for their helpful and valuable comments.

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  1. CSED, TIET, Patiala, 147004, Punjab, India

    Garima Kadian & Rajiv Kumar

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  1. Garima Kadian
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  2. Rajiv Kumar
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Correspondence to Garima Kadian.

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Kadian, G., Kumar, R. Analysis and selection of haze-relevant features for haze detection. Multimed Tools Appl 82, 39057–39076 (2023). https://doi.org/10.1007/s11042-023-14771-w

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