International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 2, July-September (2012), © IAEME
209
SPATIAL DOMAIN IMAGE ENHANCEMENT USING
PARAMETERIZED HYBRID MODEL
1I.Suneetha and
2Dr.T.Venkateswarlu
1Associate Professor,ECE Department,AITS,Tirupati,INDIA Pin-517520.
2 Professor,ECE Department,S.V.University College of Engineering,Tirupati,INDIA Pin-517501.
Abstract Images are very powerful tools to provide information to the viewers in every field i.e. medical
images for doctors, forensic images for police investigation, text images for readers etc. In the
process of image acquisition, contrast of an image becomes poor because of lighting, weather,
distance, or equipment used for image capture. Noise corrupts the images during sensing with
malfunctioning cameras, storing in faulty memory locations or sending through a noisy channel.
Sometimes quality of the image may be corrupted by poor contrast and unwanted noise. This
paper proposes a method for image enhancement through contrast improvement and noise
suppression using a Parameterized Hybrid Model in spatial domain. The proposed method
provides good results subjectively as well as objectively for both gray scale and true color
images. The proposed method is better, faster, and also useful for interactive image processing
applications as it provides various enhancement images for an image.
Key Words-Parameterized Gradient Intercept (PGI), Parameterized Adaptive Recursive (PAR), Mean
Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Digital Image Processing(DIP).
I. Introduction
Image enhancement improves digital
image quality without knowing the source of
degradation and provides visually acceptable
images for human viewers and/or automated
image processing techniques. We reviewed
enhancement techniques for gray scale
images in spatial domain and implemented
them using MATLAB [1]. These techniques
have been extended successfully to true
color images [2]. Image enhancement
through noise suppression can be done using
a Nonlinear Parameterized Adaptive
Recursive (PAR) model [3]. Image
enhancement through contrast improvement
can be done by using a Linear Parameterized
Gradient Intercept (PGI) model [4]. Linear
INTERNATIONAL JOURNAL OF ELECTRONICS AND
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ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
Volume 3, Issue 2, July- September (2012), pp. 209-216
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6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 2, July-September (2012), © IAEME
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and nonlinear models work well when an
image is corrupted by either poor contrast or
unwanted noise, but fails when corrupted by
both. This paper proposes a method for
image enhancement through contrast
improvement and noise suppression using a
Parameterized Hybrid model in spatial
domain. The type of noise considered is salt
and pepper noise. Sections II and III cover
related work done about linear PGI model
and nonlinear PAR model.
II. Linear PGI Model
Relation between the input image
and output image in a linear PGI model is
g�x, y� = G × f�x, y� + I �0 ≤ x < �0 ≤ y < �� where G is Gradient and I is Interception of
the transformation. G and I values can be
zero, positive, or negative. When G and/or I
values are varied for improving the image
contrast, above transformation becomes
simple linear or nonlinear but not
exponential or logarithmic as in traditional
point processing methods and does not
require PDF calculations as in histogram
processing operations.
PGI model works well for a gray
scale image and results are much more
pronounced for true color image by
preserving maximum color details. Results
indicate that mean square error (MSE) and
Computational time (tc) of PGI method is
smaller when compared to Traditional
Histogram Equalization (THE) and Adaptive
Histogram Equalization (AHE) methods.
(a) (b)
Fig. 1: (a) Man image, darken image, THE image,
AHE image and PGI image (b) Their Histograms
Table 1: MSE and tc for Man image
MSE tc THE AHE PGI THE AHE PGI
0.0124 0.0972 2.5e-9 0.0057 0.0334 0.0003
III. Nonlinear PAR Model
The relation between input image and
output image for nonlinear PAR model is
g(x,y) = imed[fn(x,y)]
where imed means Intentional median filter
that performs filtering to noisy pixels
intentionally. Let A be the window size that
is adaptive and R be the Recursive order. A
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 2, July-September (2012), © IAEME
211
and/or R can be varied. Results indicate that
PAR method has small tc and high PSNR
when compared to TMF, RMF, and AMF.
Table 2: PSNR and tc for Man image
PSNR(dB)
Original TMF RMF AMF PAR
63.29 74.25 73.87 71.54 81.39
tc( sec)
TMF RMF AMF PAR
0.439644 0.633756 0.370175 0.182324
(a) (b) (c)
(d) (e) (f)
Fig. 2: (a) Man image (b) noisy image (c) TMF
(d) RMF (e) AMF, and (f) PAR images
IV. Proposed Method
PGI model improves contrast with
smallest mean square error and low
computational time. PAR model suppresses
noise with highest PSNR and low
computational time. Hence linear PGI model
and nonlinear PAR model can be
combinable to enhance an image to that is
corrupted by both poor contrast and
unwanted noise. As we are combining a
linear and nonlinear model, the resultant
model can be named as Parameterized
Hybrid Model (PHM) in which G and/or I,
A and/or R are varied.
The proposed PHM has smallest
MSE and highest PSNR at low
computational cost in spatial domain. The
following are the steps involved in PHM
algorithm simulation.
Gray scale image:
1. Consider a good contrast and noiseless
image i(x,y).
2. Get poor contrast image fc(x,y) by
amplitude scaling of i(x,y)
3. fn(x, y) is a noisy image of fc(x,y).
4. Select appropriate values of A and R.
5. Ensquare noisy image with (A-1)/2
zeros to get padded image fp(x,y)
6. gp(x,y) is imed filter of fp(x,y) .
7. If gp(x,y) is noisy, vary A and/or R.
8. If A varies, go to 4th step otherwise
go to 5th step.
9. Remove the ensquared zeros in gp(x,y) to
get denoisy image fdn(x,y).
10. Select appropriate values of G and I.
11. Multiply fdn(x,y) by G and add I to get
g(x,y)
12. Observe the enhanced image g(x,y).
13. If g(x,y) is not good in contrast, then
change G and/or I, go to11th step.
True Color image:
1. Consider a good contrast and noiseless
image i(x,y).
2. Get poor contrast image fc(x,y) by
amplitude scaling of i(x,y)
3. fn(x, y) is a noisy image of fc(x,y).
4. Select appropriate values of A and R.
5. Extract r,g,b components from fn(x,y)
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
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6. Select appropriate values of A and R.
7. Ensquare noisy rgb images of fn (x,y)
with (A-1)/2 zeros to get padded images
rpgpbp.
8. Perform imed filter to rpgpbp separately.
9. Get color image gp(x,y) from filtered
rpgpbp.
10. If gp(x,y) is noisy, vary A and/or R .
11. If A varies go to 7th step otherwise
go to 8th step.
12. Remove the ensqured zeros in gp(x,y)
to get denoisy image fdn(x,y).
13. Extract Y from fdn(x,y) using RGB to
YIQ conversion to get l(x,y).
14. Select appropriate values of G and I.
15. Multiply l(x,y) by G and add I to get
f(x,y).
16. Get enhanced image g(x,y) from f(x,y)
using YIQ to RGB conversion.
17. Observe the enhanced image g(x,y).
18. If g(x,y) is not good in contrast, then
change G and/or I, go to15 th step.
V. Results
The PHM performance can be
verified by not only by visual inspection of
the resultant images but also by evaluating
the mean square error and Peak Signal to
Noise Ratio in decibels (PSNR) [5-7]. The
subjective results and objective results are
shown in the following figures and tables.
���� = 20����� �� − 1√#$%&
#$% = 1��''()�*, +� − ��*, +�,-
.
/0�
1
20�
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 2, July-September (2012), © IAEME
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International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 2, July-September (2012), © IAEME
214
(a) (b)
Fig. 3: (a) Darken and noisy gray scale images
(b) Parameterized Hybrid Model images
(a) (b)
Fig. 4: (a) Darken and noisy true color images
(b) Parameterized Hybrid Model images
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 2, July-September (2012), © IAEME
215
(a) (b)
Fig. 6: (a) Brighten and noisy true color images
(b) Parameterized Hybrid Model images.
(a) (b)
Fig. 5: (a) Brighten and noisy gray scale
images
(b) Parameterized Hybrid Model
images
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 2, July-September (2012), © IAEME
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Visual inspection of subjective
results indicates that, the Parametric Hybrid
Model works very well by enhancing gray
scale and true color images that are
corrupted by decreased contrast and
unwanted noise. Visual inspection of
objective results shows that, MSE was
decreased and PSNR was increased for the
gray scale images and also for the R, G, and
B components of true color images. The
limitation in the proposed model is small
decrement in mse and small improvement in
PSNR for enhancing gray scale and true
color images that are corrupted by increased
contrast and unwanted noise. The reason is,
while increasing contrast for getting
simulation results some pixel values reach
maximum value which are then treated as
salt during denoising. Therefore resultant
images are not having very good contrast.
This problem can be overcome by slight
change in PHM algorithm.
VI. Conclusions Spatial Domain Image enhancement
using Parameterized Hybrid Model has been
successfully implemented using MATLAB.
This paper considers gray scale and true
color images from different fields. Choice of
A and R depends on noise intensity where as
choice of G and I depend on amount of poor
contrast. As proposed algorithm is a faster
and better, PHM can be used as a tool for
Photo editing software like Photoshop or
any existing image processing software by
attaching two sliding bars for A and R that
suppresses noise and two sliding bars for G
and I that improves contrast. The PHM
model can be used for suppressing high
level salt and pepper noise or other types of
noises with slight changes in algorithm.
Future scope will be the development of
local parameterized models for image
enhancement in Region Of Interest (ROI)
when an image is corrupted differently in
various regions.
REFERENCES [1] Ms. I.Suneetha and Dr.T.Venkateswarlu,
“Enhancement Techniques for Gray scale Images in
Spatial Domain”, International Journal of Emerging
Technology and Advanced Engineering, website:
www.ijetae.com(ISSN 2250-2459) Volume 2, Issue 4,
April 2012, pp.13-20.
[2] Ms. I.Suneetha and Dr.T.Venkateswarlu,
“Enhancement Techniques for True Color Images in
Spatial Domain”, International Journal of Computer
Science & Technology (IJCST), Website:
www.ijcst.com(ISSN 0976-8491) Volume 3, Issue 2,
Version 5, April - June 2012, pp. 814-820.
[3] Ms. I.Suneetha and Dr.T.Venkateswarlu, “Image
Enhancement Through Noise Suppression Using
Nonlinear Parameterized Adaptive Recursive Model”,
International Journal of Engineering Research and
Applications (IJERA), Website: www.ijera.com (ISSN
2248-9622), Volume 2, Issue 4, July-August 2012, pp.
1129-1136.
[4] Ms. I.Suneetha and Dr.T.Venkateswarlu, “Image
Enhancement Through Contrast Improvement Using
Parameterized Gradient Intercept Model”, ARPN
Journal of Engineering and Applied Sciences (ARPN-
JEAS), Website:www.arpnjournals.com (ISSN
1819-6608), Volume 7, No. 8, August 2012.
[5] J Rafael C Gonzalez, Richard E. Woods, and Steven L.
Eddins, Digital Image Processing Using MATLAB®
(Second Edition, Gates mark Publishing, 2009).
[6] J. Y. im, L. S. Kim, S. H Hwang, “An advanced
Contrast Enhancement Using Partially Overlapped
Sub–Block Histogram Equalization”, IEEE
Transactions on Circuits and Systems for Videc
Technology, Vol. 11, No. 4, pp.475-484,2001.
[7] Prof. A. Senthilrajan, Dr. E. Ramaraj, “High Density
Impulse Noise Removal in Color Images Using Region
of Interest Median Controlled Adaptive Recursive
Weighted Median Filter”, Proceedings of the
International MultiConferenceof Engineers and
Computer Scientists (IMECS), Vol. II, March 17-19,
2010, Hong Kong.