Investigations of image contrast space defined by variations on histogram equalization
CVGIP: Graphical Models and Image Processing
A histogram modification framework and its application for image contrast enhancement
IEEE Transactions on Image Processing
Contrast enhancement using brightness preserving bi-histogram equalization
IEEE Transactions on Consumer Electronics
Image enhancement based on equal area dualistic sub-image histogram equalization method
IEEE Transactions on Consumer Electronics
Adaptive image contrast enhancement using generalizations of histogram equalization
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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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.