A Global Two-Stage Histogram Equalization Method for Gray-Level Images

Khaled Almotairi


Digital image histogram equalization is an important technique in image processing to improve the quality of the visual appearance of images. However, the available methods suffer from several problems such as side effects and noise, brightness and contrast problems, loss of information and details, and failure in enhancement and in achieving the desired results. Therefore, the Adaptive Global Two-Stage Histogram Equalization (GTSHE) method for visual property enhancement of gray-level images is proposed. The first stage aims to clip the histogram and equalize the clipped histogram based on the number of occurrences of gray-level values. The second stage adaptively adjusts the space between occurrences by using a probability density function and different cumulative distribution functions that depend on the available and missing gray-level occurrences. Experiments were conducted using a number of benchmark datasets of images such as the Galaxies, Biomedical, Miscellaneous, Aerials, and Texture datasets. The results of the experiments were compared with a number of well-known methods, i.e. HE, AHEA, ESIHE, and MVSIHE, to evaluate the performance of the proposed method. The evaluation analysis showed that the proposed GTSHE method achieved a higher accuracy rate compared to the other methods.


gray-scale images; histogram equalization; image processing; image enhancement; images quality; visual appearance

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Choudhary, R. & Gawade, S., Survey on Image Contrast Enhancement Techniques, International Journal of Innovative Studies in Sciences and Engineering Technology 2(3), pp. 21-25, 2016.

Agarwal, M. & Mahajan, R., Medical Image Contrast Enhancement using Range Limited Weighted Histogram Equalization, Procedia Computer Science, 125, pp. 149-156, 2018.

Tekalp, A.M., Digital video processing, Prentice Hall Press, 2015.

Burger, W. & Burge, M.J., Digital Image Processing: An Algorithmic Introduction Using Java, Springer, 2016.

Gu, J.P., Hua, L., Wu, X., Yang, H. & Zhou, Z.T., Color Medical Image Enhancement Based on Adaptive Equalization of Intensity Numbers Matrix Histogram, International Journal of Automation and Computing, 12(5), pp. 551-558, 2015.

Suganya, P., Gayathri, S. & Mohanapriya, N., Survey on Image Enhancement Techniques, International Journal of Computer Applications Technology and Research, 2(5), pp. 623, 2013.

Kong, N.S.P., Ibrahim, H. & Hoo, S.C., A Literature Review on Histogram Equalization and Its Variations for Digital Image Enhancement, International Journal of Innovation, Management and Technology, 4(4), pp. 386, 2013.

Jain, K. & Arya, I.B., A Survey of Contrast Enhancement Technique for Remote Sensing Images, International Journal of Electrical, Electronics and Computer Engineering, 3(2), pp.1, 2014.

Reddy, E. & Reddy, R., Dynamic Clipped Histogram Equalization Technique for Enhancing Low Contrast Images, Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 89(4), pp. 673-698, 2019.

Zabani, F. N., Jaafar, H., Radzuan, N.R.R.M. & Nasir, A.S.A., Preliminary Studies for Detection of Penicillium Species Using Adaptive Histogram Equalization Technique, In 2019 IEEE 9th International Conference on System Engineering and Technology (ICSET), 8, pp. 236-240, 2019.

Zhuang, L. & Guan, Y., Image Enhancement via Subimage Histogram Equalization Based on Mean and Variance, Computational intelligence and neuroscience 2017, 2017.

Gonzalez, R.C. & Woods, R.E., Digital Image Processing, In. Prentice hall Upper Saddle River, NJ, 2002.

Tang, J.R. & Isa, N.A.M., Bi-Histogram Equalization Using Modified Histogram Bins, Applied Soft Computing, 55, pp. 31-43, 2017.

Kaur, R. & Sharma, M., Histogram Equalization and Histogram Matching for the Biomedical Image Enhancement and Visualization, European Journal of Advances in Engineering and Technology, 2(11), pp. 49-55, 2015.

Subburaj, P., Wavelet Shrinkage Adaptive histogram Equalization for Medical Images, International Journal of Computing, 13(3), pp. 191-196, 2014.

Longkumer, N., Kumar, M. & Saxena, R., Contrast Enhancement Techniques using Histogram Equalization: A Survey, International Journal of Current Engineering and Technology, 4(3), pp. 1561-1565, 2014.

Aboshosha, S., Zahran, O., Dessouky, M.I. & El-Samie, F.A., Resolution and Quality Enhancement of Images Using Interpolation and Contrast Limited Adaptive Histogram Equalization, Multimedia Tools and Applications, 78(13), pp. 18751-18786, 2019.

Wang, Y., Chen, Q. & Zhang, B., Image Enhancement Based on Equal Area Dualistic Sub-Image Histogram Equalization Method, IEEE Transactions on Consumer Electronics 45(1), pp. 68-75, 1999.

Chen, S.D. & Ramli, A.R., Minimum Mean Brightness Error Bi-histogram Equalization in Contrast Enhancement, IEEE transactions on Consumer Electronics 49(4), pp. 1310-1319, 2003.

Chen, S.D. & Ramli, A.R., Contrast Enhancement Using Recursive Mean-Separate Histogram Equalization for Scalable Brightness Preservation, IEEE Transactions on consumer Electronics, 49(4), pp. 1301-1309, 2003.

Sun, C.C., Ruan, S.J., Shie, M.C. & Pai, T.W., Dynamic Contrast Enhancement Based on Histogram Specification, IEEE Transactions on Consumer Electronics, 51(4), pp. 1300-1305, 2005.

Sim, K., Tso, C. & Tan, Y., Recursive Sub-image Histogram Equalization Applied to Gray Scale Images, Pattern Recognition Letters, 28(10), pp. 1209-1221, 2007.

Singh, K. & Kapoor, R., Image Enhancement Using Exposure Based Sub Image Histogram Equalization, Pattern Recognition Letters, 36, pp. 10-14, 2014.

Huang, S.C., Cheng, F.C. & Chiu, Y.S., Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution, IEEE Transactions on Image Processing, 22(3), pp. 1032-1041, 2013.

Singh, N., Kaur, L. & Singh, K., Histogram Equalization Techniques for Enhancement of Low Radiance Retinal Images for Early Detection of Diabetic Retinopathy, Engineering Science and Technology, an International Journal, 22(3), pp.736-45, 2019.

Zhu, Y. & Huang, C., An Adaptive Histogram Equalization Algorithm on the Image Gray Level Mapping, Physics Procedia, 25, pp. 601-608, 2012.

Tan, S. F., & Isa, N. A. M., Exposure Based Multi-Histogram Equalization Contrast Enhancement for Non-Uniform Illumination Images, IEEE Access, 7, pp. 70842-70861, 2019.

Lu, L., Zhou, Y., Panetta, K. & Agaian, S., Comparative Study of Histogram Equalization Algorithms for Image Enhancement, In: Mobile Multimedia/Image Processing, Security, and Applications 2010 2010, p. 770811. International Society for Optics and Photonics

Singh, K., Kapoor, R. & Sinha, S.K., Enhancement of Low Exposure Images Via Recursive Histogram Equalization Algorithms, Optik-International Journal for Light and Electron Optics, 126(20), pp. 2619-2625, 2015.

Kim, Y.T., Contrast Enhancement Using Brightness Preserving Bi-Histogram Equalization, IEEE transactions on Consumer Electronics 43(1), pp. 1-8, 1997.

Agarwal, T.K., Tiwari, M. & Lamba, S.S., Modified Histogram Based Contrast Enhancement Using Homomorphic Filtering for Medical Images, IEEE International In: Advance Computing Conference 2014 (IACC), 7, pp. 964-968, 2014.

CVG-UGR Image Database. Available online: http://sipi.usc.edu/database/ (1 September 2019)

The USC-SIPI Image Database, The USC Signal and Image Processing Institute, http://sipi.usc.edu/database/ (1 September 2019)

DOI: http://dx.doi.org/10.5614%2F10.5614%2Fitbj.ict.res.appl.2020.14.2.1


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