A Dynamic Histogram Equalization for Image Contrast Enhancement

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Presentation transcript:

A Dynamic Histogram Equalization for Image Contrast Enhancement Source: IEEE Transactions on Consumer Electronics, Vol. 53, No. 2, MAY 2007 Author: M. Abdullah-A-Wadud, Md. Hasanul Kabir, M. Ali Akber Dewan, and Oksam Chae Speaker: Chih-Hao Chen Date: 2008/04/30

Introduction Original Image Processed Image

Proposed Method(1/4) Input Image Output Image Phase1 Phase2 Histogram Partition Gary Level Allocation Histogram Input Image Output Image 1 2 3 4 255 1 2 3 4 255

Proposed Method(2/4) p1 p0 p2 One-dimensional smoothing filter: p0: The processing pixel H1 1 2 3 4 H2

Proposed Method(3/4) u: mean value s: standard deviation Marked area 1 2 3 1 2 3 4 5 6 u: mean value s: standard deviation If marked area is less than 68.3% of current sub-histogram, splits again.

Proposed Method(4/4) Sectioni: Gray level range of sub-histogram i Original image Output image Sectioni: Gray level range of sub-histogram i Si: The summation of all histogram values of ith sub- histogram x: The coefficient to control the strength of image contrast

Experimental Results(1/3) (a) Original image (b)-(e) DHEed image (x = 0, 1, 2, 4, accordingly).

Experimental Results(2/3) (a) Original image, (b) GHEed image, (c) DHSed image, (d) RMSHEed image (r = 2), (e) DHEed image (x = 0).

Experimental Results(3/3) (a) Original image (b) GHEed image (c) DHSed image (d) RMSHEed image with r = 2 (e) DHEed image with x= 0.

Conclusions DHE enhances the image contrast without making any loss in image details. This method is simple and easy to implement.