© The Institution of Engineering and Technology
This study presents a new contrast-enhancement approach called entropy-based dynamic sub-histogram equalisation. The proposed algorithm performs a recursive division of the histogram based on the entropy of the sub-histograms. Each sub-histogram is divided recursively into two sub-histograms with equal entropy. A stopping criterion is proposed to achieve an optimum number of sub-histograms. A new dynamic range is allocated to each sub-histogram based on the entropy and number of used and missing intensity levels in the sub-histogram. The final contrast-enhanced image is obtained by equalising each sub-histogram independently. The proposed algorithm is compared with conventional as well as state-of-the-art contrast-enhancement algorithms. The quantitative results for a large image data set are statistically analysed using a paired t-test. The quantitative and visual assessment shows that the proposed algorithm outperforms most of the existing contrast-enhancement algorithms. The proposed algorithm results in natural-looking, good contrast images with almost no artefacts.
References
-
-
1)
-
26. Xue, W., Zhang, L., Mou, X., et al: ‘Gradient magnitude similarity deviation: a highly efficient perceptual image quality index’, IEEE Trans. Image Process., 2014, 23, (2), pp. 684–695.
-
2)
-
25. Larson, E.C., Chandler, D.M.: ‘Most apparent distortion: full-reference image quality assessment and the role of strategy’, J. Electron. Imaging, 2010, 19, (1), pp. 011006-1–011006-21.
-
3)
-
6. Sim, K.S., Tso, C.P., Tan, Y.: ‘Recursive sub-image histogram equalization applied to gray scale images’, Pattern Recognit. Lett., 2007, 28, pp. 1209–1221.
-
4)
-
20. Wei, Z., Lidong, H., Jun, W., et al: ‘Entropy maximisation histogram modification scheme for image enhancement’, IET Image Process., 2015, 9, (3), pp. 226–235.
-
5)
-
23. Arbeláez, P., Maire, M., Fowlkes, C., et al: ‘Contour detection and hierarchical image segmentation’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (5), pp. 898–916.
-
6)
-
4. Chen, S.-D., Ramli, A.R.: ‘Minimum mean brightness error bi-histogram equalization in contrast enhancement’, IEEE Trans. Consum. Electron., 2003, 49, (4), pp. 1310–1319.
-
7)
-
16. Abdoli, M., Sarikhani, H., Ghanbari, M., et al: ‘Gaussian mixture model-based contrast enhancement’, IET Image Process., 2015, 9, (7), pp. 569–577.
-
8)
-
8. Kim, M., Chung, M.G.: ‘Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement’, IEEE Trans. Consum. Electron., 2008, 54, (3), pp. 1389–1397.
-
9)
-
9. Singh, K., Kapoor, R.: ‘Image enhancement using exposure based sub image histogram’, Pattern Recognit. Lett., 2014, 36, pp. 10–14.
-
10)
-
22. Singh, K., Vishwakarma, D.K., Walia, G.S., et al: ‘Contrast enhancement via texture region based histogram equalization’, J. Mod. Opt., 2016, 65, (14), pp. 1444–1450.
-
11)
-
15. Celik, T., Tjahjadi, T.: ‘Automatic image equalization and contrast enhancement using Gaussian mixture modeling’, IEEE Trans. Image Process., 2012, 21, (1), pp. 145–156.
-
12)
-
10. Hanmandlu, M., Verma, O.P., Kumar, N.K., et al: ‘A novel optimal fuzzy system for color image enhancement using bacterial foraging’, IEEE Trans. Instrum. Meas., 2009, 58, (8), pp. 2867–2879.
-
13)
-
7. Wang, Q., Ward, R.K.: ‘Fast image/video contrast enhancement based on weighted thresholded histogram equalization’, IEEE Trans. Consum. Electron., 2007, 53, (2), pp. 757–764.
-
14)
-
18. Huang, S.-C., Cheng, F.-C., Chiu, Y.-S.: ‘Efficient contrast enhancement using adaptive gamma correction with weighting distribution’, IEEE Trans. Image Process., 2103, 22, (3), pp. 1032–1041.
-
15)
-
5. Chen, S.-D., Ramli, A.R.: ‘Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation’, IEEE Trans. Consum. Electron., 2003, 49, (4), pp. 1301–1309.
-
16)
-
1. Gonzalez, R.C., Woods, R.E.: ‘Digital image processing’ (Pearson, New Delhi, India, 2009, 3rd edn.).
-
17)
-
2. Kim, Y.-T.: ‘Contrast enhancement using brightness preserving bi-histogram equalization’, IEEE Trans. Consum. Electron., 1997, 43, (1), pp. 1–8.
-
18)
-
19. Wang, C., Ye, Z.: ‘Brightness preserving histogram equalization with maximum entropy: a variational prospective’, IEEE Trans. Consum. Electron., 2005, 51, (4), pp. 1326–1334.
-
19)
-
20)
-
27. Lind, D.A., Marchal, W.G., Wathen, S.A.: ‘Statistical technics in business and economics’ (TMH Education Pvt. Ltd., New Delhi, India, 2007, 13th edn.).
-
21)
-
13. Ibrahim, H., Kong, N.S.: ‘Brightness preserving dynamic histogram equalization for image contrast enhancement’, IEEE Trans. Consum. Electron., 2007, 53, (4), pp. 1752–1758.
-
22)
-
17. Celik, T.: ‘Two-dimensional histogram equalization and contrast enhancement’, Pattern Recognit., 2012, 45, pp. 3810–3824.
-
23)
-
21. Fu, X., Wang, J., Zeng, D., et al: ‘Remote sensing image enhancement using regularized-histogram equalization and DCT’, IEEE Geosci. Remote Sens. Lett., 2015, 12, (11), pp. 2301–2305.
-
24)
-
14. Sheet, D., Garud, H., Suveer, A., et al: ‘Brightness preserving dynamic fuzzy histogram equalization’, IEEE Trans. Consum. Electron., 2010, 56, (4), pp. 2475–2480.
-
25)
-
3. Wang, Y., Chen, Q., Zhang, B.: ‘Image enhancement based on equal area dualistic sub-image histogram equalization method’, IEEE Trans. Consum. Electron., 1999, 45, (1), pp. 68–75.
-
26)
-
12. Abdullah-Al-Wadud, M., Kabir, M.H., Dewan, M.A., et al: ‘A dynamic histogram equalization for image contrast enhancement’, IEEE Trans. Consum. Electron., 2007, 53, (2), pp. 593–600.
-
27)
-
11. Singh, K., Kapoor, R., Sinha, S.K.: ‘Enhancement of low exposure images via recursive histogram equalization algorithms’, Optik, 2015, 126, (20), pp. 2619–2625.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2016.0242
Related content
content/journals/10.1049/iet-ipr.2016.0242
pub_keyword,iet_inspecKeyword,pub_concept
6
6