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-th...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 18; no. 5; pp. 1135 - 1140
Main Author: Deng, Guang
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.05.2009
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithms
Applied sciences
Biological system modeling
Detection, estimation, filtering, equalization, prediction
Entropy
Exact sciences and technology
Filters
Gray-scale
Human
Humans
Image contrast
Image enhancement
Image processing
information content
Information theory
Information, signal and communications theory
logarithmic image processing (LIP) model
Mathematical model
Mathematical models
Noise reduction
relative entropy
Signal and communications theory
Signal processing
Signal, noise
Telecommunications and information theory
Visual
Visual system
Image quality
Adaptive algorithm
Logarithmic function
Image processing
information content
relative entropy
Entropy
logarithmic image processing (LIP) model
Image enhancement
Noisy image
Information interpretation
Information theory
ISSN:1057-7149, 1941-0042
Online Access:Get full text
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Summary: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.
Bibliography:ObjectType-Article-2
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ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2009.2016796