4.ijaest vol no 7 issue no 2 a new approach to image contrast enhancement using weighted threshold...

8/6/2019 4.IJAEST Vol No 7 Issue No 2 a New Approach to Image Contrast Enhancement Using Weighted Threshold Histogra

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A New Approach To Image Contrast Enhancement

using Weighted Threshold Histogram Equalization

with Improved Switching Median Filter

R.Sharmila

Rajiv Gandhi College of Engineering and Technology

Electronics and Communication EngineeringPuducherry, India

[email protected]

R. Uma

Rajiv Gandhi College of Engineering and Technology

Electronics and Communication EngineeringPuducherry, India

[email protected]

AbstractImage contrast enhancement plays a vital role in

digital image processing especially in biomedical

applications and secures digital image transmission. The

objective of image enhancement technique is to improvethe characteristics or quality of an image, such that the

resulting image is better than the original image. To

improve the image contrast, numerous enhancement

techniques have been proposed. One of the conventional

methods adopted is the Histogram Equalization (HE)

technique. The main limitation of this technique causes

unpleasant visual artifacts, such as over enhancement,level saturation and raised noise level. To overcome these

limitations technique like Dualistic Sub-Image Histogram

Equalization (DSIHE), Brightness Preserving Bi-

Histogram Equalization (BBHE) and Weighted Threshold

Histogram Equalization (WTHE) has been proposed

which failed to eliminate the impulse noise. In this paperwe proposed a novel method called Weighted Threshold

Histogram Equalization with Improved Switching Median

Filtering technique (WTHEISMF) which eliminates the

impulse noise and preserves the edges of an image. This

paper presents the parameters comparison and simulationresults of HE, WTHE and WTHEISMF with Absolute

Mean Brightness Error (AMBE), Measure of

Enhancement (EME), Peak Signal to Noise Ratio (PSNR),

Mean Square Error (MSE).

Keywords- Histogram Equalization (HE), contrast

enhancement, Impulse noise, Improved Switching Median

Filter (ISMF)

I. INTRODUCTIONThe image enhancement is to improve the interpretability ofthe information present in images for human viewers. An

enhancement algorithm is one that yields a better qualityimage for the purpose of some particular application whichcan be done by either suppressing the noise or increasing the

image contrast. Image enhancement algorithms are employedto emphasize, sharpen or smoothen image features for displayand analysis. Image enhancement techniques can be classified

into two broad categories they are Spatial domain method andTransform domain method. The spatial domain method

operates directly on pixels, whereas the transform domain

method operates on the Fourier transform of an image and it istransformed back to the spatial domain. There are two

techniques in contrast enhancement: Direct methods andIndirect method [1][2]. There are several subgroups in Indirectmethods[3] i) techniques that decay an image into high andlow frequency signals for manipulation ii)histogram

modification techniques and iii)Transform based techniques.

Elementary enhancement techniques are histogram based

because they are simple, fast and produces acceptable resultsfor some applications can be achieved. Histogrammodification basically modifies the histogram of an inputimage so as to improve the visual quality of the image.

Histogram equalization is a process that attempts to spread outthe gray levels in an image so that they are evenly distributedacross their range. In the histogram equalization without any

modification, it over enhanced the output image and raisednoise level. A number of methods have been proposed [3] forlimiting the level of enhancement by modifying the histogramequalization.

The recent method proposed to enhance an image using HE is

Adaptive HE methods and Global methods based onHistogram equalization [4]. In the above two proposedmethod, AHE methods can provide high level of enhancementcompare to the Global methods. Bi-Histogram Equalization

was proposed to reduce the mean brightness change in animage. BHE is a modified version of Histogram Equalization[3, 4]. In this method initially an image is partitioned into two

sections based on average of the histogram and then applieshistogram equalization on each section individually. A similarimproved Histogram based method is Dualistic Sub-ImageHistogram Equalization (DSIHE)[3,4]. In these methods,

initially histogram is partitioned into two fragments based onentropy and then applies histogram equalization on both thefragment. In the above method the enhanced image is more

satisfying than Histogram equalization but not possible to

R.Sharmila* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES

Vol No. 7, Issue No. 2, 206 - 211

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change the level of enhancement [3]. The aforementionedproblems can be overcome by Weighted Threshold HistogramEqualization.

The general idea adopted by the WTHE [4] method is tomodify the histogram before equalization is conducted. Suchmodifications reduce visual artifacts. During the imageenhancement the impulse noise in the image also enhanced

[5]. To avoid this effect, the enhanced image is passed througha filter. In digital signal and image processing; an image isoften corrupted by noise in its acquisition or transmission.Noise is undesirable information that contaminates an image.So the conventional filtering techniques are used to eliminate

the noise and to preserve the edges. Several filteringtechniques have been reported are linear filtering; averagingfiltering and nonlinear filtering [6]. The linear filtering tends

to blur the edges, and do not remove the noise effectively. Nonlinear filtering is generally used for the removal ofimpulse noise due to some advantages like preservation ofedges. The nonlinear filtering is used in various fields like

image processing, Telecommunication, Geo physical signalprocessing.

The median filter is an effective method for the removal ofimpulse based noise on images [5]. It is also called as slidingwindow filter, which replace the center value in the window

with the median of all pixel values in the window. This is dueto the partial averaging effect of the median filter and itsbiasing of the input stream, rather than straight mathematicalaveraging. The drawback of using this median filter is thatthey cannot detect whether the pixel is noisy or not, so theyreplace both noisy as well as noise free pixel which makes theimage to become visible blur. The aforesaid problems can be

overcome by modified forms of the median filter have been proposed [5].The weighted median filtering method assignweight to each pixel values; it duplicates the pixel values for

several times. The existing median filtering techniquedecreases the quality of the enhanced images rather thanimproving the quality of an image. The Improved SwitchingMedian Filtering technique [7] is used to reduce the noise

level in enhanced image and preserving the edges of an image.So this paper presents a new novel approach for imagecontrast enhancement using Weighted Threshold Histogram

Equalization with Improved Switching Median Filter.

This paper is organized as follows. Section II gives the

Histogram Equalization technique procedure. Followingsection III explains the details about Weighted ThresholdHistogram Equalization. Section IV presents the ImprovedSwitching Median Filtering technique and its advantage. In

section V presents the detail about proposed method,Simulation results and discussions are presented in Section VI.Finally Conclusion is provided in section VII. The simulation

result is tested using MAT Lab tool.

II. HISTOGRAMEQUALIZATION TECHNIQUEThe main objective of histogram equalization is to obtain a

uniform histogram for the output image [3]. The procedure toperform histogram equalization techniques is as follows:

1. Find the running sum of the histogram values.2. Normalize the running sum values by dividing by the total

number of pixels.3. Multiply the normalized values by maximum gray level and

round off to the nearest integer value.4. Map the gray level values using a one to one mapping.

For a given digital image G (i,j) with M pixels and a gray levelof [0,L-1].The probability density function(PDF) is given by

( ) nn

uP k

M for 0,1,....., 1nk L (1)

Where isn

u the number of times level k appears in an image.

The running sum or cumulative density function (CDF) ofhistogram values is obtained by

0

( ) ( )k

n

m

C k P m

for 0,1,....., 1nk L (2)

Using the running sum, histogram equalization plots an input

leveln

k into an output leveln

k

( 1) ( )n nk L C k (3)

The output level can be increment by

( 1) ( )n nk L P k (4)

The above equation indicates that distance betweenn

k and

nk +1 has direct relation with PDF of the input image at the

gray leveln

k . In the Histogram Equalization, the pixel values

are uniformly distributed with gray level values across theirrange. HE over enhances the background of the image and

often produces the improbable effects in the image.

IIIWEIGHTEDTHRESHOLDHISTOGRAMEQUALIZATION

One of the recent methods proposed to overcome the visualartifacts of the HE method and add more flexibility to it isWeighted Threshold Histogram Equalization [4]. The general

idea adopted by WTHE is to modify the histogram before

equalization. Weighted Threshold Histogram Equalization is performed by modifying the histogram of an image by

assigning the weight and threshold to each pixel beforeequalization.

The weighted and threshold value of probability density

function ( )nP k is obtained by the following equation given

by,


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(5)

Where ( )wt nP k is the weighted and threshold parameter value

of ( )nP k . uP is upper threshold value, that is the PDF is

clamped to maximum gray level value of ( )nP k .In the

WTHE method the value ofu

P is given by

max.uP v P 0


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Input Histogram

Modify the PDF OF

histogram using Weighted

and Threshold Method

Histogram equalization

Enhanced image of WTHE

Improved Switching

Median filter

Enhanced Image

V PROPOSED METHOD

In this section, a Weighted Threshold Histogram Equalizationwith Improved Switching Median Filtering technique(WTHEISMF) is presented. As mentioned in section I ,thereare some problems in histogram modification techniques .Weovercome these problems in our proposed method by the

Weighted Threshold Histogram Equalization with ImprovedSwitching Median Filtering technique (WTHEISMF).

The proposed method consist of two steps1. Enhance the image using Weighted Threshold

Histogram Equalization2. Enhanced image is passed through an ISMF

Fig. 1 Flowchart of Proposed method

The flow chart of proposed method is represented in fig.1.In

this method using the weighted threshold histogramequalization techniques the image is enhanced, preserves theover enhancement and level clipping. While the enhanced

image is passed through an improved switching median filter,it reduces the impulse noise created during the enhancement.

VI SIMULATION RESULTS AND DISCUSSION

In order to test the proposed method, experiments are

performed on CT scan Brain and Face of size 256 256.Toevaluate the image enhancement and restoration performance,Absolute Mean Brightness Error(AMBE), Measure OfEnhancement(EME), Peak Signal To Noise Ratio(PSNR),Mean Squared Error(MSE) used as the criterion.

The AMBE is defined as total difference between input andoutput mean values [15].

( ) ( )n nMBE E X E Y (11)

The measure of enhancement (EME), it gives an average

contrast of an image by partition an image into nonoverlapping segments. The measurement is based onmaximum and minimum intensity of each segment andaveraging them. The MSE value is given by

112

0 0

1( ( , ) ( , ))

ji

m n

SE x m n y i j

ij

(12)

Where the size of an image is i j .The original and restored

images are ( , )m n and ( , )i j .The PSNR is the ratio

between the maximum possible power of a signal and thepower of corrupted noise.

PSNR =20 log10 (255/sqrt (MSE)).

The performance of the proposed method is compared with theconventional histogram equalization technique followed by

median and improved switching median filter and the result ofweighted threshold histogram equalization followed ISMF is

compared with the median filter. Only a few of the results areshown in this paper.

Fig. (2,3) shows the original images and their correspondingcontrast enhanced image using HE and WTHE followed bymedian and ISMF techniques.

(a) (b)


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(c) (d)

(e) (f)Fig. 2 Results for CT Scan Brain image (a) Original Image (b)HE image (c) HE followed by Median filter (d)HE followed by

ISMF (e) WTHE Image (f) WTHE followed by ISMF (proposedmethod)

(a ) (b)

(c) (d)

(e) (f)

Fig.3 Results for Face image (a) Original Image (b) HE image (c)HE followed by Median filter (d)HE followed by ISMF (e)WTHE Image (f) WTHE followed by ISMF

TABLE 1 QUANTITATIVE MEASUREMENT RESULTS

Image AMBE EME

HE WTHE HE WTHE

Brain 559.2421 28.0946 9.3772 6.1649

Face 441.2384 69.9803 10.9344 5.8623

TABLE 2 .MSE and PSNR for proposed and existing method

Image MSE PSNR

WTHE

followedbyMedianfilter

WTHE

followedbyISMF

WTHE

followedbyMedianfilter

WTHE

followedbyISMF

Brain 5.7227 3.4125 40.5548 42.8000

Face 3.8017 2.9057 42.3310 43.4983

The proposed algorithm is compared with histogramequalization. In the Histogram equalization, the pixels are

spreading uniformly. Usually Histogram Equalized resultantimages are unnatural look that means it over enhances the

images. The WTHE reduces the effect caused by thehistogram equalization. When the contrast of an image isenhanced, as an upshot the noise induced in the image will behigh and the image looks unnatural. In this paper, the

proposed technique can be effectively used to improve thecontrast of images and filter out the noises in the image and preserves the edges. Computed quantitative measures AMBE

and EME value for histogram equalization and weightedthreshold histogram equalization are listed in table I.


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Comparison of AMBE and EME values show that theproposed methods outperform well compared to the histogramequalization. For the proposed method, the computed value of

PSNR and MSE value for WTHE followed by Median andISMF are listed in Table II .From the table, we conclude thatthe PSNR and MSE values are better than the median filter.

VII CONCLUSION

A novel approach to image enhancement technique usingWTHEISMF is proposed which is better than the existing

histogram equalization method and WTHE followed bymedian filtering. The proposed method depicts that the imageenhancement and restoration parameters like AMBE, EME,

PSNR and MSE are outperform well as compared to theexisting method. It effectively reduces over-enhancement,level clamping artifacts and impulse noise produced due toenhancement. The proposed method is computationally easy

and can be implemented on FPGA and work well on real timeprocessing system.

REFERENCES

[1] R.Sherrier and G.Johnson, Regionally adaptive histogramequalization of the chest, IEEE Tran.Med.Image.,vol.MI-06,no.1,pp.1-7,Jan. 1987.[2] A.Polesel, G.Ramponi, and V.Mathews, Image enhancement viaadaptive unsharp masking, IEEE Trans.Image Process., vol.9. no.3,pp.505-510, Mar.2000.[3] Tarik Arici, salih Dikbas.A Histogram modification framework

and its application for image contrast enhancement ,IEEE Trans.

Image Process., vol.18, no.9,pp. 1921-1935,Sep 2009.[4] Q. Wang and R. K. Ward, Fast image/video contrastenhancement based on weighted thresholded histogram equalization,

IEEE Trans.Consum. Electron., vol. 53, no. 2, pp. 757764, May2007.

[5] P.Jagaatheeswari, S.Suresh kumar and M.Rajaram,A novel

approach for contrast enhancement based on histogram equalizationfollowed by median filter,APRN.,vol. 4,no. 7, pp.41-45, Sep 2009

[6] S.Zhang,M.A.Karim,A new impulse detector for switchingmedian filters,IEEE Signal Processing Letters,vol. 9,no.11,pp.360-363,2002.

[7] Rajoo pandey,An improved switching median filter foruniformly distributed impulse noise removal, World Academy ofScience.pp.349-351., 2008.[8] Y-T. Kim, Contrast enhancement using brightness preserving bihistogram equalization, IEEE Trans. on Consumer Electronics,vol.43,no. 1pp. 1 8, Feb. 1997.

[9] Y. Wan, Q. Chen, and B-M. Zhang, Image enhancement basedon equal area dualistic sub-image histogram equalization method,

IEEE Trans. on Consumer Electronics, vol. 45,no. 1, pp. 68 75,Feb. 1999.[10] S-D Chen and A. Ramli, Minimum mean brightness error bi-

histogram equalization in contrast enhancement, IEEE Trans. onConsumer Electronics, vol. 49,nNo. 4, pp. 1310 1319 ,November.2003.

[11] Y-T. Kim, Quantized bi -histogram equalization, ICASSP-97,vol. 4, pp. 2797 2800, April 1997.[12]R. C. Gonzalez and R. E.Woods , Digital Image processing, 2nded.Englewood Cliffs, NJ: Prentice-Hall, 2002.

[13] Kim, Y, Contrast Enhancement using Brightness preserving

Bi-histogram equalization, IEEE Transactions on ConsumerElectronics, vol.43, Issue 1, pp 1-8,1997.

[14] G.R.Arce and M.P.McLoughlin,Theoretical analysis of theMAX/Median filter, IEEE Trans.Acoust.Speeach,SignalProcessing,vol.ASSP-35,pp.60-69,1987.

[15] N. Phanthuna, F. Cheevasuvitand S. Chitwong,Contrastenhancement for minimum mean brightness error from

histogram partitioning, ASPRS, Mar.2009.

AUTHORS PROFILE

She is graduated B.E (EEE) from BharathiyarUniversity Coimbatore in the year 1998, Postgraduated in M.E (VLSI Design) from Anna

University Chennai in the year 2004. Currently shehas been working as Assistant Professor inElectronics and Communication Engineering, RajivGandhi College of Engineering and Technology,

Puducherry. She has been teaching VLSI Design, EmbeddedSystems, Microprocessor and Microcontrollers for PG and UG

students. She authored books on VLSI Design. She has publishedseveral papers on national conference and symposium. She is theguest faculty for Pondicherry University for M.Tech Electronics. Hehas been actively guiding PG and UG students in the area of VLSI,Embedded and image processing. She has received the best teacher

award for the year 2006 and 2007. Her research interests are AnalogVLSI Design, Low power VLSI Design, Testing of VLSI Circuits,Embedded systems and Image processing. She is a member of ISTE.

Sharmila.R received her B.E (Electronics &Communication Engineering) from AnnamalaiUniversity in 2006 & M.E (Computer &

Communication Engineering) from Anna Universityin 2010. Currently she has been working as a lecturer

in the Department of Electronics and Communication Engineering inRajiv Gandhi College of Engineering & Technology Puducherry .Her

area of interests are image processing and wireless sensor network.She is a member of ISTE.


Vol No. 7, Issue No. 2, 206 - 211


4.ijaest vol no 7 issue no 2 a new approach to image contrast enhancement using weighted threshold...

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