Adaptive Contrast Enhancement by Entropy Maximization with a 1-K-1 Constrained Network
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چکیده
This paper uses the Maximum Entropy Principle to construct a 1-K-1 constrained sigmoidal neural network which adaptively adjusts its gain parameters to control the transfer function in order to maximize the entropy measure at the output for image contrast enhancement. We demonstrate how the model works with the standard lena image.
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تاریخ انتشار 1995