Adaptive Bi-Histogram Equalization Using Threshold (ABHET)

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.

[1]  Yeong-Taeg Kim,et al.  Contrast enhancement using brightness preserving bi-histogram equalization , 1997 .

[2]  Min Gyo Chung,et al.  Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement , 2008, IEEE Transactions on Consumer Electronics.

[3]  S. Anand,et al.  Mammogram image enhancement by two-stage adaptive histogram equalization , 2015 .

[4]  Nor Ashidi Mat Isa,et al.  Adaptive contrast enhancement methods with brightness preserving , 2010, IEEE Transactions on Consumer Electronics.

[5]  Aurobinda Routray,et al.  A non-rigid motion estimation algorithm for yawn detection in human drivers , 2009, Int. J. Comput. Vis. Robotics.

[6]  Xiaolu Yang,et al.  An Improved Median-based Otsu Image Thresholding Algorithm , 2012 .

[7]  Y. Y. Tan,et al.  Recursive sub-image histogram equalization applied to gray scale images , 2007, Pattern Recognit. Lett..

[8]  Nor Ashidi Mat Isa,et al.  Adaptive Image Enhancement based on Bi-Histogram Equalization with a clipping limit , 2014, Comput. Electr. Eng..

[9]  Srikanta Patnaik,et al.  A Comparative Analysis on Edge Detection of Colloid Cyst: A Medical Imaging Approach , 2012, Soft Computing Techniques in Vision Science.

[10]  Qian Chen,et al.  Image enhancement based on equal area dualistic sub-image histogram equalization method , 1999, IEEE Trans. Consumer Electron..

[11]  Abd. Rahman Ramli,et al.  Minimum mean brightness error bi-histogram equalization in contrast enhancement , 2003, IEEE Trans. Consumer Electron..

[12]  Abd. Rahman Ramli,et al.  Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation , 2003, IEEE Trans. Consumer Electron..

[13]  Subhasmita Sahoo,et al.  Performance Analysis of HE Methods for Low Contrast Images , 2016 .