This paper presents a preprocessing technique that can provide the improved quality of image robust to illumination changes. First, in order to enhance the image contrast, we proposed new adaptive histogram transformation combining histogram equalization and histogram specification. Here, by examining the characteristic of histogram distribution shape, we determine the appropriate target distribution. Next, applying the histogram equalization with an image histogram, we have obtained the uniform distribution of pixel values, and then we have again carried out the histogram transformation using an inverse of target distribution function. Finally we have conducted various experiments that can enhance the quality of image by applying our method with various standard images. The experimental results show that the proposed method can achieve moderately good image enhancement results.
 H. Ibrahim and N. S. P. Kong, “Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement,” Consumer Electronics, Vol. 53, No. 4, 2007, pp. 1752-1758. http://dx.doi.org/10.1109/TCE.2007.4429280
 H. Yoon, Y. Han and H. Hahn, “Image Contrast Enhancement Based Sub-Histogram Equalization Technique without Over-Equalization Noise,” International Journal of Electrical and Electronics Engineering, Vol. 3, No. 6, 2009, pp. 323-329.
 M. Kaur, J. Kaur and J. Kaur, “Survey of Contrast Enhancement Techniques Based on Histogram Equalization,” International Journal of Advanced Computer Science and Applications, Vol. 2, No. 7, 2011, pp. 137-141. http://dx.doi.org/10.14569/IJACSA.2011.020721
 V. Struc, J. Zibert and N. Pavesic, “Histogram Remapping as a Preprocessing Step for Robust Face Recognition,” WSEAS Transactions on Information Science and Applications, Vol. 6, No. 3, 2009, pp. 520-529.
 S. S. Agaian, K. P. Lentz and A. M. Grigoryan, “Transform-Based Image Enhancement Algorithms with Performance Measure,” IEEE Transactions on Image Processing, Vol. 10, No. 3, 2001, pp. 367-381. http://dx.doi.org/10.1109/83.908502