R.Sharmila* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 7, Issue No. 2, 206 - 211

A New Approach To Image Contrast Enhancement using Weighted Threshold Histogram Equalization with Improved Switching Median Filter

Abstractâ€” Image contrast enhancement plays a vital role in

into two broad categories they are Spatial domain method and Transform 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 is transformed back to the spatial domain. There are two techniques in contrast enhancement: Direct methods and Indirect method [1][2]. There are several subgroups in Indirect methods[3] i) techniques that decay an image into high and low frequency signals for manipulation ii)histogram modification techniques and iii)Transform based techniques.

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digital image processing especially in biomedical applications and secures digital image transmission. The objective of image enhancement technique is to improve the 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 BiHistogram Equalization (BBHE) and Weighted Threshold Histogram Equalization (WTHE) has been proposed which failed to eliminate the impulse noise. In this paper we 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 simulation results 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).

R. Uma

Rajiv Gandhi College of Engineering and Technology Electronics and Communication Engineering Puducherry, India Uma.ramadass1@gmail.com

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R.Sharmila

Rajiv Gandhi College of Engineering and Technology Electronics and Communication Engineering Puducherry, India Sharmila1ece@gmail.com

Keywords- Histogram Equalization (HE), contrast enhancement, Impulse noise, Improved Switching Median Filter (ISMF) I.

INTRODUCTION

The image enhancement is to improve the interpretability of the information present in images for human viewers. An enhancement algorithm is one that yields a better quality image for the purpose of some particular application which can be done by either suppressing the noise or increasing the image contrast. Image enhancement algorithms are employed to emphasize, sharpen or smoothen image features for display and analysis. Image enhancement techniques can be classified

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Elementary enhancement techniques are histogram based because they are simple, fast and produces acceptable results for some applications can be achieved. Histogram modification basically modifies the histogram of an input image so as to improve the visual quality of the image. Histogram equalization is a process that attempts to spread out the gray levels in an image so that they are evenly distributed across their range. In the histogram equalization without any modification, it over enhanced the output image and raised noise level. A number of methods have been proposed [3] for limiting the level of enhancement by modifying the histogram equalization. The recent method proposed to enhance an image using HE is Adaptive HE methods and Global methods based on Histogram equalization [4]. In the above two proposed method, AHE methods can provide high level of enhancement compare to the Global methods. Bi-Histogram Equalization was proposed to reduce the mean brightness change in an image. 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 applies histogram equalization on each section individually. A similar improved Histogram based method is Dualistic Sub-Image Histogram Equalization (DSIHE)[3,4]. In these methods, initially histogram is partitioned into two fragments based on entropy and then applies histogram equalization on both the fragment. In the above method the enhanced image is more satisfying than Histogram equalization but not possible to

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R.Sharmila* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 7, Issue No. 2, 206 - 211

The general idea adopted by the WTHE [4] method is to modify the histogram before equalization is conducted. Such modifications reduce visual artifacts. During the image enhancement the impulse noise in the image also enhanced [5]. To avoid this effect, the enhanced image is passed through a filter. In digital signal and image processing; an image is often 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 filtering techniques have been reported are linear filtering; averaging filtering 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 of impulse noise due to some advantages like preservation of edges. The nonlinear filtering is used in various fields like image processing, Telecommunication, Geo physical signal processing.

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 level of [0,L-1].The probability density function(PDF) is given by

un for kn 0,1,....., L 1 (1) M Where is u n the number of times level k appears in an image. P (kn )

The running sum or cumulative density function (CDF) of histogram values is obtained by k

C (kn ) P (m) for kn 0,1,....., L 1

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This paper is organized as follows. Section II gives the Histogram Equalization technique procedure. Following section III explains the details about Weighted Threshold Histogram Equalization. Section IV presents the Improved Switching 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.

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(2)

m0

Using the running sum, histogram equalization plots an input level k n into an output level k n

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The median filter is an effective method for the removal of impulse based noise on images [5]. It is also called as sliding window filter, which replace the center value in the window with the median of all pixel values in the window. This is due to the partial averaging effect of the median filter and its biasing of the input stream, rather than straight mathematical averaging. The drawback of using this median filter is that they cannot detect whether the pixel is noisy or not, so they replace both noisy as well as noise free pixel which makes the image 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 assign weight to each pixel values; it duplicates the pixel values for several times. The existing median filtering technique decreases the quality of the enhanced images rather than improving the quality of an image. The Improved Switching Median 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 image contrast enhancement using Weighted Threshold Histogram Equalization with Improved Switching Median Filter.

II. HISTOGRAM EQUALIZATION TECHNIQUE The main objective of histogram equalization is to obtain a uniform histogram for the output image [3]. The procedure to perform histogram equalization techniques is as follows:

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change the level of enhancement [3]. The aforementioned problems can be overcome by Weighted Threshold Histogram Equalization.

kn ( L 1) C (kn )

(3)

kn ( L 1) P (kn )

(4)

The output level can be increment by

The above equation indicates that distance between k n and

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

gray level k n . In the Histogram Equalization, the pixel values are uniformly distributed with gray level values across their range. HE over enhances the background of the image and often produces the improbable effects in the image. III WEIGHTED THRESHOLD HISTOGRAM EQUALIZATION

One of the recent methods proposed to overcome the visual artifacts of the HE method and add more flexibility to it is Weighted 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 before equalization. The weighted and threshold value of probability density function P ( k n ) is obtained by the following equation given by,

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R.Sharmila* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 7, Issue No. 2, 206 - 211

IV IMPROVED SWITCHING MEDIAN FILTERING TECHNIQUE

Where Pwt ( k n ) is the weighted and threshold parameter value of P ( k n ) . Pu is upper threshold value, that is the PDF is clamped to maximum gray level value

of P ( k n ) .In the

WTHE method the value of Pu is given by

Pu v.Pmax

0< v

1

(6)

Where Pmax is the peak value of the P ( k n )

The cumulative distribution function (CDF) of original image G (i,j) is given by the equation k

Cwt (kn ) Pwt (m) m0

for kn 0,1,....., L 1 (7)

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The histogram Equalization maps an input level k n into an output level k n is given by

kn ( L 1) Cwt (kn )

(8)

The output level can be increment by

kn ( L 1) Pwt (k n )

(9)

G(i, j ) Wout Cwt (G(i, j )) M adj

(10)

The resultant image G (i,j) is given by

The parameter Wout and M adj are constant and it gives dynamic range of the output image and reimburse the mean change after enhancement. In the WTHE, it reduces the visual artifacts resulting from the histogram equalization and it is capable to control the level of the enhancement.

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Algorithm 1: Improved Switching Median Filter ___________________________________ 1. Median value n (i, j), for every window 2. if ( n(i,

j) c(i, j) ) threshold1

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The original PDF of P ( k n ) is clamped to an upper and lower thresholds value by using a normalized power law function with index r 0 .The value of r is an important parameter which control the degree of enhancement and depends upon the value of r ,it gives higher and lower weights to the probability density function. After the weighting and threshold operation the remaining procedure is similar to Histogram Equalization.

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(5)

Filtering plays an important role in any signal processing application. The main goal of filtering is to enhance a fine detail in an image. In the improved switching median filtering technique, first find the difference between the median value of pixels and center pixel value in the filtering window and then compared with a two threshold values to obtain a presence of noise in an image. The decision based filtering procedure is used to modify the corrupted pixels; it does not affect uncorrupted pixels. The median filtering have some undesirable features like 1) thin lines and other fine details are distorted 2) it replace both the noisy as well as noise less pixel which makes the image to appear blur. The improved switching median filter has proved to be good because it effectively modifies only those pixels that are considered to be corrupted pixel. The window size may be either 3 x 3, 5x5 or 7x7. In this method, the impulse noise is obtained by using the following algorithm,

3. a min{cn (i, j ) c(i, j )}; 4. if (a threshol 2)

d (i, j ) c(i, j ) 6. else d (i, j ) n(i, j ) 7. 8. end ; 9. end ; 5.

_______________________________________

Consider an image of dimension 255 255 on which ISMF the window size may be either 3 3 or 5 5. First step is to find the median value. The conventional median operation is performed by arranging the pixel values in ascending order and the middle value is taken as the median value n (i, j) .In step 2, find the difference between the median value n (i, j) and the current pixel value c(i, j ) . In step3 set the first threshold value and the difference value is being compared with the threshold value. If the difference is less than the threshold value difference between the each pixel value in the window and center pixel value is calculated. The minimum value a is compared with the second threshold value .If the minimum value is less than the threshold value then the current pixel value is considered to be noise free and retained as such. Otherwise the current pixel is considered to be noisy and that value is replaced by the median value. By using this modified algorithm, improved switching median filter has proved to be good because it effectively modifies only those pixels that are considered to be corrupted pixel.

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V PROPOSED METHOD In this section, a Weighted Threshold Histogram Equalization with Improved Switching Median Filtering technique (WTHEISMF) is presented. As mentioned in section I ,there are some problems in histogram modification techniques .We overcome these problems in our proposed method by the Weighted Threshold Histogram Equalization with Improved Switching Median Filtering technique (WTHEISMF). The proposed method consist of two steps 1. Enhance the image using Weighted Threshold Histogram Equalization 2. Enhanced image is passed through an ISMF

In order to test the proposed method, experiments are performed on CT scan Brain and Face of size 256 256.To evaluate the image enhancement and restoration performance, Absolute Mean Brightness Error(AMBE), Measure Of Enhancement(EME), Peak Signal To Noise Ratio(PSNR), Mean Squared Error(MSE) used as the criterion. The AMBE is defined as total difference between input and output mean values [15].

AMBEn E( X ) En (Y )

(11)

The measure of enhancement (EME), it gives an average contrast of an image by partition an image into non overlapping segments. The measurement is based on maximum and minimum intensity of each segment and averaging them. The MSE value is given by

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Input Histogram

VI SIMULATION RESULTS AND DISCUSSION

1 i 1 j 1 ( x(m, n) y(i, j))2 (12) ij m 0 n 0 Where the size of an image is i j .The original and restored images are x(m, n) and y (i, j ) .The PSNR is the ratio MSE

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

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Modify the PDF OF histogram using Weighted and Threshold Method

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

Histogram equalization

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Enhanced image of WTHE

Improved Switching Median filter

The performance of the proposed method is compared with the conventional histogram equalization technique followed by median and improved switching median filter and the result of weighted threshold histogram equalization followed ISMF is compared with the median filter. Only a few of the results are shown in this paper. Fig. (2,3) shows the original images and their corresponding contrast enhanced image using HE and WTHE followed by median and ISMF techniques.

Enhanced Image

Fig. 1 Flowchart of Proposed method

The flow chart of proposed method is represented in fig.1.In this method using the weighted threshold histogram equalization techniques the image is enhanced, preserves the over 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.

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(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

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TABLE 1 QUANTITATIVE MEASUREMENT RESULTS Image

AMBE

Brain

(f)

HE

WTHE

HE

WTHE

559.2421

28.0946

9.3772

6.1649

69.9803

10.9344

5.8623

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(e)

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 (proposed method)

441.2384

Face

EME

TABLE 2 .MSE and PSNR for proposed and existing method

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Image

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MSE WTHE followed by Median filter

WTHE followed by ISMF

PSNR WTHE followed by Median filter

WTHE followed by ISMF

Brain

5.7227

3.4125

40.5548

42.8000

Face

3.8017

2.9057

42.3310

43.4983

The proposed algorithm is compared with histogram equalization. In the Histogram equalization, the pixels are spreading uniformly. Usually Histogram Equalized resultant images are unnatural look that means it over enhances the images. The WTHE reduces the effect caused by the histogram equalization. When the contrast of an image is enhanced, as an upshot the noise induced in the image will be high and the image looks unnatural. In this paper, the proposed technique can be effectively used to improve the contrast 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 weighted threshold histogram equalization are listed in table I.

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Comparison of AMBE and EME values show that the proposed methods outperform well compared to the histogram equalization. For the proposed method, the computed value of PSNR and MSE value for WTHE followed by Median and ISMF are listed in Table II .From the table, we conclude that the PSNR and MSE values are better than the median filter.

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

[15] N. Phanthuna, F. Cheevasuvit and S. Chitwong,‖ Contrast enhancement for minimum mean brightness error from histogram partitioning‖, ASPRS, Mar.2009.

VII CONCLUSION

REFERENCES

Sharmila.R received her B.E (Electronics & Communication Engineering) from Annamalai University in 2006 & M.E (Computer & Communication Engineering) from Anna University in 2010. Currently she has been working as a lecturer in the Department of Electronics and Communication Engineering in Rajiv Gandhi College of Engineering & Technology Puducherry .Her area of interests are image processing and wireless sensor network. She is a member of ISTE.

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[1] R.Sherrier and G.Johnson, ‖Regionally adaptive histogram equalization of the chest,” IEEE Tran.Med.Image.,vol.MI06,no.1,pp.1-7,Jan. 1987. [2] A.Polesel, G.Ramponi, and V.Mathews, ― Image enhancement via adaptive 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 contrast enhancement based on weighted thresholded histogram equalization,‖ IEEE Trans.Consum. Electron., vol. 53, no. 2, pp. 757–764, May 2007. [5] P.Jagaatheeswari, S.Suresh kumar and M.Rajaram,‖A novel approach for contrast enhancement based on histogram equalization followed by median filter,‖APRN.,vol. 4,no. 7, pp.41-45, Sep 2009 [6] S.Zhang,M.A.Karim,‖A new impulse detector for switching median filters,‖IEEE Signal Processing Letters,vol. 9,no.11,pp.360363,2002. [7] Rajoo pandey,‖An improved switching median filter for uniformly distributed impulse noise removal,‖ World Academy of Science. 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 based on 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 bihistogram equalization in contrast enhancement‖, IEEE Trans. on Consumer 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, 2nd ed.Englewood Cliffs, NJ: Prentice-Hall, 2002. [13] Kim, Y, ― Contrast Enhancement using Brightness preserving

AUTHORS PROFILE

She is graduated B.E (EEE) from Bharathiyar University Coimbatore in the year 1998, Post graduated in M.E (VLSI Design) from Anna University Chennai in the year 2004. Currently she has been working as Assistant Professor in Electronics and Communication Engineering, Rajiv Gandhi College of Engineering and Technology, Puducherry. She has been teaching VLSI Design, Embedded Systems, Microprocessor and Microcontrollers for PG and UG students. She authored books on VLSI Design. She has published several papers on national conference and symposium. She is the guest faculty for Pondicherry University for M.Tech Electronics. He has 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 Analog VLSI Design, Low power VLSI Design, Testing of VLSI Circuits, Embedded systems and Image processing. She is a member of ISTE.

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A novel approach to image enhancement technique using WTHEISMF is proposed which is better than the existing histogram equalization method and WTHE followed by median filtering. The proposed method depicts that the image enhancement and restoration parameters like AMBE, EME, PSNR and MSE are outperform well as compared to the existing method. It effectively reduces over-enhancement, level clamping artifacts and impulse noise produced due to enhancement. The proposed method is computationally easy and can be implemented on FPGA and work well on real time processing system.

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

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