Abstract
A novel algorithmic approach for optimal contrast enhancement is proposed. A measure of expected contrast and a sister measure of tone subtlety are defined for gray level transform functions. These definitions allow us to depart from the current practice of histogram equalization and formulate contrast enhancement as a problem of maximizing the expected contrast measure subject to a limit on tone distortion and possibly other constraints that suppress artifacts. The resulting contrast-tone optimization problem can be solved efficiently by linear programming. The proposed constrained optimization framework for contrast enhancement is general, and the user can add and fine tune the constraints to achieve desired visual effects. Experimental results demonstrate clearly superior performance of the new technique over histogram equalization.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Kim, Y.T.: Enhancement using brightness preserving bi-histogram equalization. IEEE Trans. Consum. Electronics 43, 1–8 (1997)
Wang, Y., Chen, Q., Zhang, B.: Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Trans. Consum. Electronics 45, 68–75 (1999)
Gauch, J.M.: Investigations of image contrast space defined by variations on histogram equalization. In: Proc. CVGIP: Grap. Models Image Processing, pp. 269–280 (1992)
Stark, J.A.: Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans. Image Processing, 889–896 (2000)
Chen, Z.Y., Abidi, B.R., Page, D.L., Abidi, M.A.: Gray-level grouping(glg): An automatic method for optimized image contrast enhancement–part i: The basic method. IEEE Trans. Image Processing 15, 2290–2302 (2006)
Chen, Z.Y., Abidi, B.R., Page, D.L., Abidi, M.A.: Gray-level grouping(glg): An automatic method for optimized image contrast enhancement–part ii: The variations. IEEE Trans. Image Processing 15, 2303–2314 (2006)
Arici, T., Dikbas, S., Altunbasak, Y.: A histogram modification framework and its application for image contrast enhancement. IEEE Trans. Image Processing 18, 1921–1935 (2009)
Pisano, E.D., Zong, S., Hemminger, B., Deluca, M., Johnston, R.E., Muller, K., Braeuning, M.P., Pizer, S.: Contrast limited adaptive histogram image processing to improve the detection of simulated spiculations in dense mammograms. Journal of Digital Imaging 11, 193–200 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, X., Zhao, Y. (2010). A New Algorithmic Approach for Contrast Enhancement. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15567-3_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-15567-3_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15566-6
Online ISBN: 978-3-642-15567-3
eBook Packages: Computer ScienceComputer Science (R0)