Differentiation-Based Edge DetectionUsing the Logarithmic Image Processing Model
Journal of Mathematical Imaging and Vision
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
EURASIP Journal on Applied Signal Processing
General Logarithmic Image Processing Convolution
IEEE Transactions on Image Processing
The study of logarithmic image processing model and its application to image enhancement
IEEE Transactions on Image Processing
A solution to the deficiencies of image enhancement
Signal Processing
General Adaptive Neighborhood Choquet Image Filtering
Journal of Mathematical Imaging and Vision
EURASIP Journal on Advances in Signal Processing - Special issue on theory and application of general linear image processing
The symmetric logarithmic image processing model
Digital Signal Processing
Adaptive Shape Diagrams for Multiscale Morphometrical Image Analysis
Journal of Mathematical Imaging and Vision
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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.