The International Arab Journal of Information Technology (IAJIT)


Contrast Enhancement using Completely

Illumination pre-processing is an inevitable step for a real-time automatic face recognition system in solving challenges related to lighting variation for recognizing the face images. This paper proposes a novel framework namely Completely Overlapped Uniformly Decrementing Sub-Block Histogram Equalization (COUDSHE) to normalize or pre- process the illumination deficient images. COUDSHE is based on the idea that efficiency of the pre-processing technique mainly depends on the framework for application of the technique on the affected image. The primary goal of this paper is to bring out a new strategy for localizing a Global Histogram Equalization (GHE) Technique to help it adapt to the local light condition of the image. The Mean Squared Error (MSE), Histogram Flatness Measure, Absolute Mean Brightness Error (AMBE) are the objective measures used to analysis the efficiency of the technique. Experimental Results reveal that COUDSHE has better performance on Heavy shadow images and half lit image than the existing techniques.

[1] Aedla R., Dwarakish G., and Reddy V., “A Comparative Analysis of Histogram Equalization based Techniques for Contrast Enhancement and Brightness Preserving,” International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 6, no. 5, pp. 353-366, 2013.

[2] Agarwal T., Tiwari M., and Lamba S., “Modified Histogram based Contrast Enhancement using Homomorphic Filtering for Medical Images,” in Proceedings of Advance Computing Conference, Gurgaon, pp. 964-968, 2014.

[3] Chen S. and Ramli A., “Minimum Mean Brightness Error Bi-Histogram Equalization in Contrast Enhancement,” IEEE Transactions on Consumer Electronics vol. 49, no. 4, pp. 1310- 1319, 2003.

[4] Gonzalez R. and Woods R., Digital Image Processing, MA: Addison-Wesley, 1992.

[5] Han H., Shan S., Chen X., and Gao W., “A Comparative Study on Illumination Preprocessing in Face Recognition,” Pattern Recognition, vol. 46, no. 6, pp. 1691-1699, 2013.

[6] Ibrahim H. and Kong N., “Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement,” IEEE Transactions on Consumer Electronics, vol. 53, no. 4, pp. 1752- 1758, 2007.

[7] Juefei-Xu F. and Savvides M., “Encoding and Decoding Local Binary Patterns for Harsh Face Illumination Normalization,” in Proceedings of International Conference Image Processing IEEE Conference, Quebec City, pp. 3220-3224, 2015.

[8] Kabir H., Wadud A., and Chae O., “Brightness Preserving Image Contrast Enhancement Using Weighted Mixture Global and Local Transformation Functions,” The International Arab Journal of Information Technology, vol. 7, no. 4, pp. 403-410, 2010.

[9] Kim J., Kim L., and Hwang S., “An Advanced Contrast Enhancement Using Partially Overlapped Sub-Block Histogram Equalization,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 11, no. 4, pp. 475-484, 2001.

[10] Kim Y., “Contrast Enhancement Using Brightness Preserving Bi-Histogram Equalization,” IEEE Transaction Consumer Electronics, vol. 43, no. 1, pp. 1-8, 1997.

[11] Kim Y., Keun T., Paik J., and Kang B., “Contrast Enhancement System Using Spatially Adaptive Histogram Equalization with Temporal Filters,” IEEE Transactions Consumer Electronics, vol. 44, no. 1, pp. 82-87, 1998.

[12] Langer M. and Zucker S., Computer Vision- ECCV'94, Springer, 1994.

[13] Lee C., Lee C., and Kim C., “Contrast Enhancement Based on Layered Difference Representation of 2D Histograms,” IEEE Transactions on Image Processing, vol. 22, no. 12, pp. 5372-5384, 2013.

[14] Liu C., Chen W., Han H., and Shan S., “Effects of Image Preprocessing on Face Matching and Recognition in Human Observers,” Applied Cognitive Psychology, vol. 27, no. 6, pp. 718- 724, 2013.

[15] Marsico M., Nappi M., and Riccio D., Wechsler H., “Robust Face Recognition for Uncontrolled Pose and Illumination Changes,” IEEE Transactions on Systems, Man and Cybernetics vol. 43, no. 1, pp. 149-163, 2013.

[16] Ooi C. and Isa N., “Adaptive Contrast Enhancement Methods with Brightness Preserving,” IEEE Transactions on Consumer Electronics, vol. 56, no. 4, pp. 2543-2551, 2001.

[17] Rabbani M. and Chellappan C., “An Effective Approach to Frontal Face Recognition Using Distance Measures,” Asian Journal of Information Technology, vol. 4, no. 12, pp. 1110- 1115, 2005.

[18] Wang Y., and Chen Q., and Zhang B., “Image Enhancement Based on Equal Area Dualistic Sub-Image Histogram Equalization Method,” IEEE Transactions on Consumer Electronics, vol. 45, no. 1, pp. 68-75, 1999.

[19] Xie X., Zheng W., Lai J., Yuen P., and Suen C., “Normalization of Face Illumination Based on Large-and Small-Scale Features,” IEEE Transactions on Image Processing, vol. 20, no. 7, pp. 1807-1821, 2011.

[20] Zuo C., Chen Q., Sui X., and Ren J., “Brightness Preserving Image Contrast Enhancement using Spatially Weighted Histogram Equalization,” The International Arab Journal of Information Technology, vol. 11, no. 1, pp. 25-32, 2014.

[21] Zuo C., Chen Q., and Sui X., “Range Limited Bi- Histogram Equalization for Image Contrast Enhancement,” Optik, vol. 124, no. 5, pp. 425- 443, 2013. 396 The International Arab Journal of Information Technology, Vol. 16, No. 3, May 2019 Shree Devi Ganesan received B.Sc., Mathematics from University of Madras, India and M.C.A from Periyar University, India. After Post graduation she worked as Lecturer in Colleges affiliated to Anna University. She is currently employed as Assistant Professor (Sr. Grade) in Department of Computer Applications, B.S.Abdur Rahman University. and pursuing Ph.D degree in the field of Image Processing. Munir Rabbani M.Sc., B.Ed., M. Phil., PGDCA., MCA., Ph.D is a Professor in School of Computer, Information and Mathematical Sciences, B.S. Abdur Rahman University, India. He received Ph.D degree from Anna University, India. in year 2009. He Posses rich International Experience in the Field of Teaching and Research. He has 21 International journal publications and 22 International Conference proceedings. His research interests include Data Mining, Image processing and Networks.