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Balanced GHM Mutiwavelet Transform Based Contrast Enhancement Technique for Dark Images Using Dynamic Stochastic Resonance

S. Deivalakshmi*, P. Palanisamy1, X. Z. Gao2

*,1 Department of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli- 620015, Tamilnadu, India.
2 School of Computing, University of Eastern Finland, Kuopio, Finland

* Corresponding Author: S. Deivalakshmi, email

Intelligent Automation & Soft Computing 2019, 25(3), 459-471. https://doi.org/10.31209/2018.100000001

Abstract

The main aim of this paper is to propose a new technique for enhancing the contrast of dark images using Dynamic Stochastic Resonance (DSR) and Multi Wavelet Transform (MWT), which is computationally more efficient than the conventional methods. In the work, for enhancing the contrast of dark images, the intrinsic noise (darkness) of dark images has been used. The proposed MWT-based DSR scheme (MWT-DSR) can yield better performances in terms of visual information and color preservation than already reported techniques. The desired output response is validated by the Relative Contrast Enhancement Factor (F), Perceptual Quality Measures (PQM) and Color Enhancement Factor (CEF).

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APA Style
Deivalakshmi, S., Palanisamy, P., Gao, X.Z. (2019). Balanced GHM mutiwavelet transform based contrast enhancement technique for dark images using dynamic stochastic resonance. Intelligent Automation & Soft Computing, 25(3), 459-471. https://doi.org/10.31209/2018.100000001
Vancouver Style
Deivalakshmi S, Palanisamy P, Gao XZ. Balanced GHM mutiwavelet transform based contrast enhancement technique for dark images using dynamic stochastic resonance. Intell Automat Soft Comput . 2019;25(3):459-471 https://doi.org/10.31209/2018.100000001
IEEE Style
S. Deivalakshmi, P. Palanisamy, and X.Z. Gao, “Balanced GHM Mutiwavelet Transform Based Contrast Enhancement Technique for Dark Images Using Dynamic Stochastic Resonance,” Intell. Automat. Soft Comput. , vol. 25, no. 3, pp. 459-471, 2019. https://doi.org/10.31209/2018.100000001



cc Copyright © 2019 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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