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A hue preserving uniform illumination image enhancement via triangle similarity criterion in HSI color space

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Abstract

Color images are essential in computer vision applications such as surveillance and security systems. However, due to the low light conditions, the color of the obtained image deviates from the original color. Moreover, it would produce inaccurate results that limit the performance of these applications. Therefore, this paper proposes a hue preserving uniform illumination image enhancement via triangle similarity criterion in hue–saturation–intensity (HSI) color space. Based on this, the proposed method develops translation and scaling operations to enhance the intensity and saturation. These enhancement processes maintain the hue and features while having minimal effect on the mean brightness. The proposed method and five model-based enhancement methods are evaluated from five perspectives: subjective visual evaluation, the hue and features preservation capabilities, the ability to enhance the contrast and visual information, mean brightness preservation capability, and computational complexity. Results indicate that the proposed method produces better-enhanced images than the other five model-based enhancement methods.

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Availability of data and code

The data and code generated during and/or analyzed during the current study are available at https://github.com/eerkifm.

References

  1. Buch, N., Velastin, S.A., Orwell, J.: A review of computer vision techniques for the analysis of urban traffic. IEEE Trans. Intell. Transp. Syst. 12, 920–939 (2011)

    Google Scholar 

  2. Zhang, X., Feng, Y., Angeloudis, P., Demiris, Y.: Monocular visual traffic surveillance: a review. IEEE Trans. Intell. Transp. Syst. 23, 14148–14165 (2022)

    Google Scholar 

  3. Liu, S., Chen, P., Wozniak, M.: Image enhancement-based detection with small infrared targets. Remote Sens. 14, 3232 (2022)

    Google Scholar 

  4. Yue, S.S., Hassan, M.F., Shibghatullah, A.S.: A hue preserving enhancement method for uniform low light images. In: Proceedings of Mechanical Engineering Research Day (MERD 22), pp. 101–102 (2022)

  5. Li, M., Zhao, L., Zhou, D., Nie, R., Liu, Y., Wei, Y.: Aems: an attention enhancement network of modules stacking for lowlight image enhancement. Visual Comput. 38, 1–17 (2021)

    Google Scholar 

  6. Liu, X., Chen, S., Song, L., Wozniak, M., Liu, S.: Self-attention negative feedback network for real-time image super-resolution. J. King Saud Univ. Comput. Inf. Sci. 34, 6179–6186 (2022)

    Google Scholar 

  7. Wang, F., Zhang, B., Zhang, C., Yan, W., Zhao, Z., Wang, M.: Low-light image joint enhancement optimization algorithm based on frame accumulation and multi-scale retinex. Ad Hoc Netw. 113, 102398 (2021)

    Google Scholar 

  8. Singh, N., Bhandari, A.K.: Principal component analysis-based low-light image enhancement using reflection model. IEEE Trans. Instrum. Meas. 70, 1–10 (2021)

    Google Scholar 

  9. Guo, S., Wang, W., Wang, X., Xu, X.: Low-light image enhancement with joint illumination and noise data distribution transformation. Visual Comput. 1–12 (2022)

  10. Yu, X., Li, H., Yang, H.: Two-stage image decomposition and color regulator for low-light image enhancement. Visual Comput. 1–11 (2022)

  11. Wang, W., Wu, X., Yuan, X., Gao, Z.: An experiment-based review of low-light image enhancement methods. IEEE Access 8, 87884–87917 (2020)

    Google Scholar 

  12. Katırcıoğlu, F.: Colour image enhancement with brightness preservation and edge sharpening using a heat conduction matrix. IET Image Proc. 14, 3202–3214 (2020)

    Google Scholar 

  13. Petro, A.B., Sbert, C., Morel, J.-M.: Multiscale retinex. Image Process. On Line 4, 71–88 (2014)

    Google Scholar 

  14. Guo, X.J., Li, Y., Ling, H.B.: Lime: low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26, 982–993 (2017)

    MathSciNet  MATH  Google Scholar 

  15. Dai, Q., Pu, Y.-F., Rahman, Z., Aamir, M.: Fractional-order fusion model for low-light image enhancement. Symmetry 11, 1–17 (2019)

    Google Scholar 

  16. Tian, H., Cai, M., Guan, T., Hu, Y.: Low-light image enhancement method using retinex method based on YCbCr color space. Acta Photonica Sin. 49, 173–184 (2020)

    Google Scholar 

  17. Hao, S., Han, X., Guo, Y., Xu, X., Wang, M.: Low-light image enhancement with semi-decoupled decomposition. IEEE Trans. Multimed. 22, 3025–3038 (2020)

    Google Scholar 

  18. Li, M., Liu, J., Yang, W., Sun, X., Guo, Z.: Structure-revealing low-light image enhancement via robust retinex model. IEEE Trans. Image Process. 27, 2828–2841 (2018)

    MathSciNet  MATH  Google Scholar 

  19. Ooi, C.H., Mat Isa, N.A.: Adaptive contrast enhancement methods with brightness preserving. IEEE Trans. Consum. Electron. 56, 2543–2551 (2010)

    Google Scholar 

  20. Singh, K., Kapoor, R.: Image enhancement using exposure based sub image histogram equalization. Pattern Recognit. Lett. 36, 10–14 (2014)

    Google Scholar 

  21. Kim, D., Kim, C.: Contrast enhancement using combined 1-d and 2-d histogram-based techniques. IEEE Signal Process. Lett. 24, 804–808 (2017)

    Google Scholar 

  22. Dong, X., Wang, G., Pang, Y., Li, W., Wen, J., Meng, W., Liu, Y.: Fast efficient algorithm for enhancement of low lighting video. In: International Conference on Multimedia and Expo (ICME), pp. 1–6 (2011)

  23. Zhang, L., Shen, P., Peng, X., Zhu, G., Song, J., Wei, W., Song, H.: Simultaneous enhancement and noise reduction of a single low-light image. IET Image Proc. 10, 840–847 (2016)

    Google Scholar 

  24. Ko, S., Yu, S., Park, S., Moon, B., Kang, W., Paik, J.: Variational framework for low-light image enhancement using optimal transmission map and combined l1 and l2-minimization. Signal Process. Image Commun. 58, 99–110 (2017)

    Google Scholar 

  25. Wang, W., Chen, Z., Yuan, X., Wu, X.: Adaptive image enhancement method for correcting low-illumination images. Inf. Sci. 496, 25–41 (2019)

    MathSciNet  Google Scholar 

  26. Li, G., Rana, M.N., Sun, J., Song, Y., Qu, J.: Real-time image enhancement with efficient dynamic programming. Multimed. Tools Appl. 79, 30883–30903 (2020)

    Google Scholar 

  27. Hassan, M.F.: A uniform illumination image enhancement via linear transformation in cielab color space. Multimed. Tools Appl. 81, 1–13 (2022)

    Google Scholar 

  28. Lore, K.G., Akintayo, A., Sarkar, S.: Llnet: a deep autoencoder approach to natural low-light image enhancement. Pattern Recognit. 61, 650–662 (2017)

    Google Scholar 

  29. Cai, J., Gu, S., Zhang, L.: Learning a deep single image contrast enhancer from multi-exposure images. IEEE Trans. Image Process. 27, 2049–2062 (2018)

    MathSciNet  MATH  Google Scholar 

  30. Li, C., Guo, J., Porikli, F., Pang, Y.: Lightennet: a convolutional neural network for weakly illuminated image enhancement. Pattern Recognit. Lett. 104, 15–22 (2018)

    Google Scholar 

  31. Ma, S., Ma, H., Xu, Y., Li, S., Lv, C., Zhu, M.: A low-light sensor image enhancement algorithm based on HSI color model. Sensor 18, 3583 (2018)

    Google Scholar 

  32. Tao, R., Zhou, T., Qiao, J.: Improved retinex for low illumination image enhancement of nighttime traffic. In: International Conference on Computer Engineering and Artificial Intelligence (ICCEAI), pp. 226–229 (2022)

  33. Yu, N., Li, J., Hua, Z.: Fla-net: multi-stage modular network for low-light image enhancement. Visual Comput. 1–20 (2022)

  34. Dixit, A.K., Yadav, R.K.: A review on image contrast enhancement in colored images. Int. J. Comput. Sci. Eng. 7, 263–273 (2019)

    Google Scholar 

  35. Mu, Q., Wang, X., Wei, Y., Li, Z.: Low and non-uniform illumination color image enhancement using weighted guided image filtering. Comput. Visual Media 7, 529–546 (2021)

    Google Scholar 

  36. Huang, C., Fang, Y., Wu, T., Zeng, T., Zeng, Y.: Quaternion screened Poisson equation for low-light image enhancement. IEEE Signal Process. Lett. 29, 1417–1421 (2022)

    Google Scholar 

  37. Hu, P., Zhu, H., Lin, J., Peng, D., Zhao, Y.-P., Peng, X.; Unsupervised contrastive cross-modal hashing. IEEE Trans. Pattern Anal. Mach. Intell. 1 (2022)

  38. Hu, P., Peng, X., Zhu, H., Zhen, L., Lin, J., Yan, H., Peng, D.: Deep semisupervised multiview learning with increasing views. IEEE Trans. Cybern. 52, 12954–12965 (2022)

  39. Xu, Y., Wen, J., Fei, L., Zhang, Z.: Review of video and image defogging algorithms and related studies on image restoration and enhancement. IEEE Access 4, 165–188 (2015)

  40. Akhand, M., Roy, S., Siddique, N., Kamal, M.A.S., Shimamura, T.: Facial emotion recognition using transfer learning in the deep CNN. Electronics 10, 1036 (2021)

    Google Scholar 

  41. Li, J., Pei, Z., Zeng, T.: From beginner to master: a survey for deep learning-based single-image super-resolution (2021). arXiv preprint arXiv:2109.14335

  42. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 4th edn. Pearson India (2018)

    Google Scholar 

  43. Ponomarenko, N., Jin, L., Ieremeiev, O., Lukin, V., Egiazarian, K., Astola, J., Vozel, B., Chehdi, K., Carli, M., Battisti, F., Kuo, C.-C.J.: Image database tid2013: peculiarities, results and perspectives. Signal Process. Image Commun. 30, 57–77 (2015)

    Google Scholar 

  44. Plataniotis, K., Venetsanopoulos, A.N.: Color Image Processing and Applications. Springer (2000)

    Google Scholar 

  45. Naik, S.K., Murthy, C.: Hue-preserving color image enhancement without gamut problem. IEEE Trans. Image Process. 12, 1591–1598 (2003)

    Google Scholar 

  46. Chien, C.-L., Tsai, W.-H.: Image fusion with no gamut problem by improved nonlinear IHS transforms for remote sensing. IEEE Trans. Geosci. Remote Sens. 52, 651–663 (2013)

    Google Scholar 

  47. Ghani, A.S.A., Aris, R.S.N.A.R., Zain, M.L.M.: Unsupervised contrast correction for underwater image quality enhancement through integrated-intensity stretched-Rayleigh histograms. J. Telecommun. Electron. Comput. Eng. (JTEC) 8, 1–7 (2016)

    Google Scholar 

  48. Chen, B., Shi, S., Sun, J., Chen, B., Guo, K., Du, L., Yang, J., Xu, Q., Song, S., Gong, W.: Using HSI color space to improve the multispectral lidar classification error caused by measurement geometry. IEEE Trans. Geosci. Remote Sens. 59, 3567–3579 (2020)

    Google Scholar 

  49. Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20, 2378–2386 (2011)

    MathSciNet  MATH  Google Scholar 

  50. Economopoulos, T.L., Asvestas, P.A., Matsopoulos, G.K.: Contrast enhancement of images using partitioned iterated function systems. Image Vis. Comput. 28, 45–54 (2010)

    Google Scholar 

  51. Sheikh, H.R., Bovik, A.C.: A visual information fidelity approach to video quality assessment. In: The First International Workshop on Video Processing and Quality Metrics for Consumer Electronics, vol. 7, pp. 2117–2128 (2005)

  52. Maurya, L., Lohchab, V., Kumar Mahapatra, P., Abonyi, J.: Contrast and brightness balance in image enhancement using cuckoo search-optimized image fusion. J. King Saud Univ. Comput. Inf. Sci. 34, 7247–7258 (2021)

    Google Scholar 

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Acknowledgements

The initial work of this paper has been accepted and presented at the Proceedings of Mechanical Engineering Research Day 2022 (MERD’22).

Funding

The research work is financially supported by the International University of Malaya-Wales (IUMW), under IUMW Internal Research Grant 2022, Grant Number: RMC/2022-23/03. In addition, the research of Tarmizi Adam is financially supported by Universiti Teknologi Malaysia, under the UTM Encouragement Research (UTMER) grant PY/2021/01263.

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Authors and Affiliations

  1. Faculty of Arts and Science, International University of Malaya-Wales, Jalan Tun Ismail, 50480, Kuala Lumpur, Kuala Lumpur, Malaysia

    Mohd Fikree Hassan

  2. Faculty of Computing, Universiti Teknologi Malaysia, Jalan Iman, 81310, Skudai, Johor, Malaysia

    Tarmizi Adam

  3. Institute of Computer Science and Digital Innovation, UCSI University, Jalan Puncak Menara Gading, Taman Connaught, 56000, Cheras, Kuala Lumpur, Malaysia

    Heshalini Rajagopal

  4. Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Jalan Profesor Diraja Ungku Aziz, 50603, Kuala Lumpur, Kuala Lumpur, Malaysia

    Raveendran Paramesran

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  1. Mohd Fikree Hassan
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Correspondence to Mohd Fikree Hassan.

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Hassan, M.F., Adam, T., Rajagopal, H. et al. A hue preserving uniform illumination image enhancement via triangle similarity criterion in HSI color space. Vis Comput 39, 6755–6766 (2023). https://doi.org/10.1007/s00371-022-02761-2

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