22 SPATIAL DOMAIN IMAGE ENHANCEMENT USING€¦ · Image enhancement improves digital image quality...

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.

[email protected],

2 [email protected]

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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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



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).


5. Extract r,g,b components from fn(x,y)



212


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�

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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



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



216

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.

22 SPATIAL DOMAIN IMAGE ENHANCEMENT USING€¦ · Image enhancement improves digital image quality...

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