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Enhancement of dark and low-contrast images using dynamic stochastic resonance
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Enhancement of dark and low-contrast images using dynamic stochastic resonance
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Enhancement of dark and low-contrast images using dynamic stochastic resonance
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Journal Article
Enhancement of dark and low-contrast images using dynamic stochastic resonance
2013
Overview
In this study, a dynamic stochastic resonance (DSR)-based technique in spatial domain has been proposed for the enhancement of dark- and low-contrast images. Stochastic resonance (SR) is a phenomenon in which the performance of a system (low-contrast image) can be improved by addition of noise. However, in the proposed work, the internal noise of an image has been utilised to produce a noise-induced transition of a dark image from a state of low contrast to that of high contrast. DSR is applied in an iterative fashion by correlating the bistable system parameters of a double-well potential with the intensity values of a low-contrast image. Optimum output is ensured by adaptive computation of performance metrics – relative contrast enhancement factor (F), perceptual quality measures and colour enhancement factor. When compared with the existing enhancement techniques such as adaptive histogram equalisation, gamma correction, single-scale retinex, multi-scale retinex, modified high-pass filtering, edge-preserving multi-scale decomposition and automatic controls of popular imaging tools, the proposed technique gives significant performance in terms of contrast and colour enhancement as well as perceptual quality. Comparison with a spatial domain SR-based technique has also been illustrated.
Publisher
The Institution of Engineering and Technology,Institution of Engineering and Technology,The Institution of Engineering & Technology
Subject
/ adaptive histogram equalisation
/ Color
/ Colour
/ Detection, estimation, filtering, equalization, prediction
/ dynamic stochastic resonance
/ Dynamics
/ edge‐preserving multiscale decomposition
/ Exact sciences and technology
/ Information, signal and communications theory
/ low‐contrast images enhancement
/ modified high‐pass filtering
/ Noise
/ relative contrast enhancement factor
/ Signal and communications theory
