International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
Dynamic Stochastic Resonance Technique
for Enhancement of Low Contrast Images
Ms .Malvika T. Deole1 , Dr. S. P. Hingway2 , Sheeja S. Suresh3
1
Research Scholar, GHRIETW, Nagpur
2,3
Assistant Professor, GHRIETW, Nagpur
ABSTRACT
Image enhancement is one of the most challenging and interesting areas of image processing because of its attractive objective.
This paper presents an image enhancement method using stochastic resonance (SR). Stochastic resonance (SR) is a phenomenon
in which the performance of a low-contrast image can be improved by addition of noise. In general, noise is always considered as
deteriorating the system performance and is therefore considered as a nuisance. However, it is observed that the presence of noise
can in fact enhance the weak signal strength or the low contrast images of certain non-linear systems including non-linear signal
and image processing. The performance of the proposed technique is going to be compared with various existing techniques.
Keywords: Bistable system, Contrast enhancement , Non-linear system , Stochastic resonance.
1. INTRODUCTION
Some features are hardly detectable from very low-contrast image by human eyes. Therefore it is needed to enhance the
contrast before display. Therefore contrast enhancement techniques are used widely in image processing. A Number of
methods have been established for contrast enhancement of low contrast images. However, none of the methods have used
the unwanted signal or noise as a potential utilization to extract the exact information from the weak signal or low
contrast image. The noise is usually thought to be a nuisance which disturbs the system. Stochastic resonance, on
contrary, is a phenomenon in which noise can be used to enhance rather than hinder the system performance. In other
words, noise can play a constructive role in enhancing weak signals. Stochastic resonance (SR) has been used as a
fundamental tool.
The concept of stochastic resonance was originally put forward by Benzi et al. [7]. Numerous research papers [8], [9] on
stochastic resonance have found. 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. Dynamic Stochastic
resonance (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. [1]. DSR has been used for enhancement of dark images previously. In
this paper, Dynamic Stochastic Resonance (DSR) is going to be used to enhance both dark as well as bright images.
2. DYNAMIC STOCHASTIC RESONANCE (DSR)
In order to exhibit stochastic resonance (SR), a system should possess three basic properties: a non-linearity in terms of
threshold, sub-threshold signals like signals with small amplitude and a source of additive noise. This phenomenon
occurs frequently in bistable systems or in systems with threshold-like behaviour. The general behaviour of SR
mechanism shows that at lower noise intensities the weak signal is unable to cross the threshold, thus giving a very low
SNR. For large noise intensities the output is dominated by the noise, also leading to a low SNR. But, for moderate noise
intensities, the noise allows the signal to cross the threshold giving maximum SNR at some optimum additive noise level.
SR is a phenomenon where the input signals of some nonlinear systems can be amplified by the addition of optimum
amount of noise. It has proved that maximizing the Signal -to- Noise Ratio (SNR) enhances the signal. For the
reconstruction of the signal, inherent noise has been used [1].
The most common quantifier of stochastic resonance is signal-to-noise ratio. Benzi’s double well theory suggests two
states of image contrast, i.e. low and high and the particles are analogous to state of coefficient magnitude [7]. Therefore,
in this bistable system, particle’s oscillation corresponds to number of iterations applied on DSR equation described by
following equation,
Volume 3, Issue 1, January 2014
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
X (n + 1) = X (n) + Δt [a X(n) – b3X(n) + Input]
Where input = B sin (ωt) + √ D ξ (t)
a = 2σ2 and b < (4a3)/27
σ is standard deviation of the image.
The image quality can be enhanced by maximizing the signal-to-noise ratio (SNR). The values of a and b are obtained by
differentiating the maximization of SNR signal.
The equation for SNR is as followsSNR =
)
3. PERFORMANCE METRICS
Performance measures such as peak signal-to-noise-ratio (PSNR), mean-square-error (MSE), quality index etc. are not
suitable for our purpose. These measures require distortion-free image or reference image. Since we want to measure
the performance of our technique in terms of contrast as well as perceptual quality, we have chosen two metrics, relative
contrast enhancement factor (F) and perceptual quality metric (PQM), respectively, to characterise each of them. The
parametric metrics are Colour Enhancement Factor (F), Contrast Enhancement Factor (CEF) and Perceptual Quality
Measure (PQM) described by [1], [4].
1. An estimate of relative contrast enhancement factor (F) can be obtained by computing ratio of values of quality index
post-enhancement (QB) and pre-enhancement (QA). Therefore
F=
2. Perceptual quality metric (PQM) –
PQM = α + β
Where α, β, γ1, γ2 and γ3 are model parameters that were estimated with the subjective test data [4]
B is the average blockiness, estimated as the average differences across block boundaries for horizontally and vertically.
A and Z constitute the activity of the signal.
3. Color enhancement factor (CEF) is defined as the ratio of colourfulness of enhanced image to that of original image.
4. PROPOSED ALGORITHM
The proposed algorithm performs contrast enhancement on images by applying Dynamic Stochastic Resonance (DSR)
iteratively.
Step1:- Convert RGB image to HSV colour space
Step2:- Apply DSR
Step3:- Compute RGB image after each iteration
Step4:- Calculate the performance metrics F(n), PQM(n) and CEF(n) after each iteration. the iterative process is
continued till F(n) + CEF(n) becomes maximum within the constraint that PQM is as close as possible to value 10.
In case the input is a grayscale image, omit the steps of colour conversion, remaining steps are same for grayscale image.
However this algorithm does not enhance bright image so it needs to be modify to enhance bright image by incorporating
adaptive local neighbourhood processing.
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 3, Issue 1, January 2014
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Block diagram of algorithm
5.
COMPARISON WITH EXISTING TECHNIQUES
The performance of the proposed technique is going to be compared with various existing techniques using three
performance metrics F, CEF and PQM on different images .
6. CONCLUSION
The proposed technique use internal noise of image for enhancement of image. This technique is going to be used to
enhance low contrast images both dark as well as bright. Research work could extend for reducing number of iterations
used in algorithm.
References
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Rajlaxmi Chouhan, Rajib Kumar Jha, Prabir Kumar Biswas : ‘Enhancement of Dark and Low-contrast Images
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Gonzales, R.C., Woods, E.: ‘Digital image processing’ (Addison-Wesley, Reading, MA, 1992)
Zhuyou XI , Yunju YAN , Yu ZHANG, Liu LIU ‘Stochastic Resonance and its Application in Detecting Weak
Signals’ 2010 3rd International Congress on Image and Signal Processing (CISP2010)
Wang, Z., Sheikh, H.R., Bovik, A.C.: ‘No-reference perceptual quality assessment of jpeg compressed images’. Proc
IEEE Int. Conf. on Image Processing, New York, USA, 2002, vol. 1, pp. 477–480
Zuiderveld, K.: ‘Contrast limited adaptive histogram equalization’ (Academic Press Professional Inc., San Diego,
CA, SA, 1994), pp. 474–485, http://portal.acm.org/citation.cfm?id = 180895.180940
Mukherjee, J., Mitra, S.K.: ‘Enhancement of color images by scaling the dct coefficients’, IEEE Trans. Image
Process., 2008, 17, (10),pp. 1783–1794
Volume 3, Issue 1, January 2014
Page 303
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
[7]
R. Benzi, A. Sutera and A. Vulpiani, "The mechanism of Stochastic Resonance," J. Physics A: Math. Journal,
vol. 14,pp. L453- L457, 1981.
[8] P.Jung and P. Hanggi, "Amplification of small signal via stochastic resonance," Physics Review A, vol. 44, no. 12,
pp .8032-8042, 1991.
[9]
L. Gammaitoni, P. Hanggi, P. Jung and F. Marchesoni, "Stochastic Resonance," Reviews of Modern Physics,
vol. 70,no. 1, pp. 223-287, 1998.
AUTHOR
Malvika T. Deole received the B.E. and pursuing M.Tech degree in VLSI from GHRIETW, Nagpur.
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