Abstract
Histogram equalization or histogram specification is a widely-used method for image enhancement. In 2005, Wang and Ye used histogram specification to propose an image enhancement method based on variational calculus. However, their method often produces over-enhanced or unnatural images, especially when the input histogram has some high peaks around the middle of the intensity interval. Extending Wang and Ye’s approach, this paper proposes a new image enhancement method called MEDHS (Maximum Entropy Distribution based Histogram Specification), which uses the Gaussian distribution to maximize the entropy and preserve the mean brightness. Specifically, the mean of the Gaussian distribution is equal to the brightness mean of the input image, and the variance of the Gaussian distribution is chosen to maximize the entropy of the output image. Experimental results show that compared to the existing methods, our method preserves the mean brightness more accurately and generates more natural looking images.
This work was supported by a special research grant from Seoul Women’s University (2012).
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© 2012 Springer-Verlag Berlin Heidelberg
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Yoo, J.H., Ohm, S.Y., Chung, M.G. (2012). Brightness Preservation and Image Enhancement Based on Maximum Entropy Distribution. In: Lee, G., Howard, D., Kang, J.J., Ślęzak, D. (eds) Convergence and Hybrid Information Technology. ICHIT 2012. Lecture Notes in Computer Science, vol 7425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32645-5_46
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DOI: https://doi.org/10.1007/978-3-642-32645-5_46
Publisher Name: Springer, Berlin, Heidelberg
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