Abstract
Contrast enhancement and brightness preservation of the image are two important issues of image enhancement in research field now-a-days. The objective is to enhance the image uniformly over different parts of the image. General Histogram Equalization doesn’t control degree of enhancement of the image. To overcome this drawback, another variant of Histogram Equalization method namely Adaptive Bi-histogram Equalization using Threshold (ABHET) is being proposed. The proposed method undergoes three steps, such as: Histogram segmentation using threshold, Clipping of histogram using mean value of occupied intensity and histogram equalization of each sub-images. Finally all the sub-images are combined into one complete image. Simulation results show that ABHET outperforms other existing HE-based methods and different image quality measures such as: Peak signal to noise ratio (PSNR), Absolute Mean Brightness Error (AMBE) and Structural Similarity Index (SSIM) are being used to test the robustness of the proposed method in terms of enhancement of contrast and preservation of brightness.
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References
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Englewood Cliffs (2009)
Kim, Y.T.: Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans. Consum. Electron. 43, 1–8 (1997)
Mohanty, M., Mishra, A., Routray, A.: A non-rigid motion estimation algorithm for yawn detection in human drivers. Int. J. Comput. Vis. Robot. 1(1), 89–109 (2009). Inder Science pub, SCOPUS, EI Comp., SNIP-1.108
Behera, S., Mohanty, M.N., Patnaik, S.: A comparative analysis on edge detection of colloid cyst: a medical image aproach. In: Patnaik, S., Yang, Y-M. (eds.) Soft Computing Techniques in Vision. SCI, 395. Springer, Heidelberg (2012)
Wan, Y., Chen, Q., Zhang, B.M.: Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Trans. Consum. Electron. 45, 68–75 (1999)
Chen, S.D., Ramli, A.R.: Minimum mean brightness error bi-histogram equalization in Contrast enhancement. IEEE Trans. Consum. Electron. 45, 1310–1319 (2003)
Chen, S.D., Ramli, A.R.: Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Trans. Consum. Electron. 49, 1301–1309 (2003)
Sim, K.S., Tso, C.P., Tan, Y.Y.: Recursive sub-image histogram equalization applied to gray scale image. Pattern Recogn. Lett. 28, 1209–1221 (2007)
Kim, M., Chung, M.G.: Recursively separated and weighted histogram equalization for brightness preservation and Contrast enhancement. IEEE Trans. Consum. Electron. 54, 1389–1397 (2008)
Ooi, C.H., Isa, N.A.M.: Adaptive contrast enhancement methods with brightness preserving. IEEE Trans. Consum. Electron. 54, 2543–2551 (2010)
Tang, J.R., Isa, N.A.M.: Adaptive image enhancement based on bi-histogram equalization with a clipping limit. Comput. Electr. Eng. 40, 86–103 (2014)
Anand, S., Gayathri, S.: Mammogram image enhancement by two-stage adaptive histogram equalizations. Optik 126, 3150–3152 (2015)
Yang, X., Shen, X., Long, J., Chen, H.: An improved median based otsu image thresholding algorithm. AASRI Procedia 3, 468–473 (2012)
Sahoo, S., Panda, J., Mohanty, M.N.: Performance analysis of HE methods for low contrast images. Procedia Comput. Sci. 92, 72–77 (2016)
Senthilkumaran, N., Vaithegi, S.: Image segmentation by using thresholding techniques for medical images. Comput. Sci. Eng. Int. J. (CSEIJ) 6(1), February 2016
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Sahoo, S., Panda, J., Mohanty, M.N. (2016). Adaptive Bi-Histogram Equalization Using Threshold (ABHET). In: Unal, A., Nayak, M., Mishra, D.K., Singh, D., Joshi, A. (eds) Smart Trends in Information Technology and Computer Communications. SmartCom 2016. Communications in Computer and Information Science, vol 628. Springer, Singapore. https://doi.org/10.1007/978-981-10-3433-6_19
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DOI: https://doi.org/10.1007/978-981-10-3433-6_19
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