Intensity exposure-based bi-histogram equalization for image enhancement

In this paper, we present a study of the usage of intensity exposure in histogram segmentation and its performance in histogram equalization. Two techniques are proposed: the mean-based bi-histogram equalization plateau limit (mean-BHEPL) or median-based BHEPL (median-BHEPL) and adaptive bi-histogram equalization algorithm (ABHE). Both techniques initially divide the input histogram into two subhistograms through a threshold value computed from the intensity exposure of the image. Histogram clipping for mean-BHEPL and median-BHEPL is then performed on these subhistograms using the mean and median values, respectively. Conventional histogram equalization is also implemented on each clipped subhistogram. The second proposed technique, ABHE, applies the modified version of the adaptive histogram equalization algorithm (AHEA) on both subhistograms. Results of extensive simulations reveal that mean-BHEPL and median-BHEPL perform comparably to the conventional BHEPL technique. ABHE exhibits excellent performance in image quality, naturalness, and mean brightness preservation. However, it is slightly inferior in image detail preservation to the conventional AHEA technique. In conclusion, segmenting the input histogram through the threshold value calculated based on the intensity exposure of the image yields good enhancement results.

PDF

___

[1] Wang Y, Chen Q, Zhang B. Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE T Consum Electr 1999; 45: 68-75.
[2] Zhu Y, Huang C. An adaptive histogram equalization algorithm on the image gray level mapping. In: International Conference on Solid State Devices and Materials Science; 12 April 2012; Macao. pp. 601-608.
[3] Abdullah-Al-Wadud M, Kabir MH, Dewan MAA, Oksam C. A dynamic histogram equalization for image contrast enhancement. IEEE T Consum Electr 2007; 53: 593-600.
[4] Kim YT. Contrast enhancement using brightness preserving bi-histogram equalization. IEEE T Consum Electr 1997; 43: 1-8.
[5] Ooi CH, Kong NSP, Ibrahim H. Bi-histogram equalization with a plateau limit for digital image enhancement. IEEE T Consum Electr 2009; 55: 2072-2080.
[6] Chen SD, Ramli R. Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE T Consum Electr 2003; 49: 1301-1309.
[7] Sim KS, Tso CP, Tan YY. Recursive sub-image histogram equalization applied to gray scale images. Pattern Recogn Lett 2007; 28: 1209-1221.
[8] Ibrahim H, Kong NSP. Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE T Consum Electr 2007; 53: 1752-1758.
[9] Zhang XY, Ge L, Wang TF. Entropy-based local histogram equalization for medical ultrasound image enhancement. In: 2nd International Conference on Bioinformatics and Biomedical Engineering; 1618 May 2008; Shanghai, China. pp. 2427-2429.
[10] Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, Romeny BTH, Zimmerman JB. Adaptive histogram equalization and its variations. Comput Vision Graph 1987; 39: 355-368.
[11] Abdullah-Al-Wadud M. A modified histogram equalization for contrast enhancement preserving the small parts in images. International Journal of Computer Science and Network Security 2012; 12: 1-4.
[12] Ooi CH, Isa NAM. Adaptive contrast enhancement methods with brightness preserving. IEEE T Consum Electr 2010; 56: 2543-2551.
[13] Ooi CH. New histogram equalization based detail and brightness preserving techniques for digital images. MSc, Universiti Sains Malysia, Penang, Malaysia, 2010.
[14] Hanmandlu M, Verma OP, Kumar NK, Kulkarni M. A novel optimal fuzzy system for color image enhancement using bacterial foraging. IEEE T Instrum Meas 2009; 58: 2867-2879.
[15] Hasikin K, Isa NAM. Fuzzy enhancement for nonuniform illumination of microscopic Sprague Dawley rat sperm image. In: IEEE International Symposium on Medical Measurements and Applications Proceedings; 1819 May 2012; Budapest, Hungary. pp. 1-6.
[16] Hasikin K, Isa NAM. Enhancement of the low contrast image using fuzzy set theory. In: 14th International Conference on Computer Modelling and Simulation; 2830 March 2012; Cambridge, UK. pp. 371-376.
[17] Zhu Y, Huang C. Histogram equalization algorithm for variable gray level mapping. In: 8th World Congress on Intelligent Control and Automation; 79 July 2010; Jinan, China. pp. 6022-6025.
[18] Shannon CE. A mathematical theory of communication. Mobile Comp Comm Rev 2001; 5: 3-33.
[19] Zadbuke AS. Brightness preserving image enhancement using modified dualistic sub image histogram equalization. International Journal of Scientific & Engineering Research 2012; 3: 1-6.
[20] Ooi CH, Isa NAM. Quadrants dynamic histogram equalization for contrast enhancement. IEEE T Consum Electr 2010; 56: 2552-2559.
[21] Srivastava G, Rawat TK. Histogram equalization: a comparative analysis and a segmented approach to process digital images. In: 6th International Conference on Contemporary Computing; 810 August 2013; Noida, India. pp. 81-85.
[22] Yoo JC, Ahn CW. Image matching using peak signal-to-noise ratio-based occlusion detection. IET Image Process 2012; 6: 483-495.
[23] Anand B, Thirugnanam K, Sebastian J, Kannan PG, Ananda AL, Chan MC, Balan RK. Adaptive display power management for mobile games. In: Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services; 28 June1 July 2011; Washington, DC, USA. pp. 57-70.
[24] Kim M, Min C. Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement. IEEE T Consum Electr 2008; 54: 1389-1397.
[25] Homnan B, Benjapolakul W. Comparison of the spatial resolution with the histogram equalization-obligated linear function on structural pixel position scheme. In: 13th International Symposium on Communications and Information Technologies; 46 September 2013; Surat Thani, Thailand. pp. 490-495.
[26] Pooja K, Yuvraj S. Comparison of different image enhancement techniques based upon PSNR & MSE. Int J Appl Eng Res 2012; 7: 1-5.
[27] Omprakash P, Maravi YPS, Sanjeev S. A comparative study of histogram equalization based image enhancement techniques for brightness preservation and contrast enhancement. Signal & Image Processing: An International Journal 2012; 4: 11-25.
[28] Khan MF, Khan E, Abbasi ZA. Weighted average multi segment histogram equalization for brightness preserving contrast enhancement. In: 2012 IEEE International Conference on Signal Processing, Computing and Control; 1517 March 2012; Waknaghat Solan, India. pp 1-6.
[29] Menotti D, Najman L, Facon J, de Araujo AA. Multi-histogram equalization methods for contrast enhancement and brightness preserving. IEEE T Consum Electr 2007; 53: 1186-1194.