An Entropy Interpretation of the Logarithmic Image Processing Model With Application to Contrast Enhancement

The logarithmic image processing (LIP) model is a mathematical theory that provides new operations for image processing. The contrast definition has been shown to be consistent with some important physical laws and characteristics of human visual system. In this paper, we establish an information-theoretic interpretation of the contrast definition. We show that it can be expressed as a combination of the relative entropy and Shannon's information content. Based on this new interpretation, we propose an adaptive algorithm for enhancing the contrast and sharpness of noisy images.