VLSI Architecture of Switching Median Filter for Salt and Pepper … · 2016-02-28 · Abstract— In this paper, VLSI architecture of new switching based median filter to remove

Abstract— In this paper, VLSI architecture of new switching

based median filter to remove high density salt and pepper noise in digital images is proposed. The absolute difference between center pixel and the median of trimmed array obtained from a 3 x 3 sliding window is compared with the predefined threshold value to identify the pixel is noisy or not. In the filtering stage, the noisy pixels are replaced by median of noise free pixels in the 3 x 3 filtering window. The experimental results for various test images show that the performance of the proposed algorithm is superior to existing algorithms, namely SMF, ACWMF, TMF, PWMAD, ARWMF, REBF, MDBUTMF and NAWMF in terms of visual quality and edge preservation. The proposed algorithm is also implemented with VHDL and simulated using Xilinx 10.1. The quantitative analysis in terms of logic elements, power and delay are observed in Altera Quartus II and compared with existing state of art algorithms, namely SMF, DBA, Parallel sorting, REBF, MDBUTMF and NAWMF.

Index Terms— Impulse Noise, edge preservation, Median Filter, Noise Detector, VLSI implementation,

I. INTRODUCTION N the process of image acquisition and transmission over the channel, the images are frequently influenced by some

external environment and corrupted with impulses. The fixed valued impulse noise corrupts the true intensity value in random position with corruptive values in the extreme ranges, ie ‘0’ (pepper) and ‘255’ (salt) called as salt and pepper noise. Another type of impulse noise is random valued impulse noise which affects the true intensity value with corruptive values in the range [0, 255], which is also the dynamic range of the image. The objective of filtering is to restore the original image from the noise corrupted image. Generally, linear filters can remove the impulse noise, but it blurs the image. Hence the best known nonlinear filter namely standard median filter (SMF) [1] is widely used due to its simplicity and computational efficiency. But it exhibits blurring effect for larger window size and less noise suppression for smaller

window size, at higher noise density. The weighted median filter (WMF) [2], center weighted median filter (CWMF) [3] and adaptive center weighted median filter (ACWMF) [4] are proposed to improve the performance of the standard median filter by giving more weight to some selected pixels in the filtering window.

However, most of the median based filters are applied to all the pixels in the noisy image which affects both noise and noise free pixel intensity which leads to blurring of an output image. Hence, many filtering algorithms with a switching strategy which discriminates the corrupted pixels and uncorrupted pixels namely, tristate median filter (TMF)[5], advanced impulse detection based on pixel wise median of absolute deviation (PWMAD)[6] and new decision based algorithm (DBA) [7] are proposed.

In TMF, the corrupted pixel is replaced by either the median value or the center weighted median value based on the threshold value and noise free pixels are left unaltered. But it performs well for the images corrupted with slightly higher impulse noise ranges to 50% only. The DBA used 3 x 3 fixed window for detection and filtering process. If the processing pixel is either ‘0’ or ‘255’, then it is replaced with a median value of local neighborhood pixels in the 3 x 3 sliding window, otherwise retained. At higher noise level all the pixels in the selected window are corrupted, and the median value may also be a noisy value. In that case, the left neighborhood pixel is used to replace the corrupted center pixel which produces the streaking effect. Also the edges are not recovered satisfactorily, since the local feature in filtering window is not taking into account.

To overcome the above drawback, adaptive recursive weighted median filter (ARWMF) [8] and robust estimation based filter (REBF) [9] are proposed. The ARWMF used median controlled algorithm for weight calculation to achieve a high degree of noise suppression and edge preservation. The REBF used an influence function based on the local estimate of image standard deviation to calculate the estimated value of corrupted pixel which gives better restoration results.

The decision based unsymmetric trimmed median filter (DBUTMF) proposed in [10] uses the trimmed median value to replace the noisy pixel. At higher noise level if the selected window contains all the pixels as noisy pixels, then the trimmed median value cannot be obtained. In addition to that, DBUTMF does not provide better restoration results when the noise level is more than 60%. The modified decision based unsymmetric trimmed median filter

VLSI Architecture of Switching Median Filter for Salt and Pepper Noise Removal

V. R. Vijaykumar, G. Santhanamari, S. Elango

I

Manuscript received November 27, 2014; revised August04, 2015 V. R. Vijaykumar is currently working as an associate professor in the department of ECE, Anna University-Regional Center, Coimbatore. (e-mail: [email protected]) G. Santhanamari is currently working as an assistant professor in the department of ECE, Tamilnadu College of Engineering, Coimbatore. (e-mail: [email protected]) S. Elango is currently working as an assistant professor in the department of ECE, Bannari Amman Institute Technology, Sathiyamangalam. (e-mail: [email protected])

IAENG International Journal of Computer Science, 43:1, IJCS_43_1_06

(Advance online publication: 29 February 2016)

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(MDBUTMF) is proposed [11] as a remedy for the above drawback. When all the pixels in the selected window are corrupted at higher noise density, it takes the mean value of all the pixels in the sliding window to replace the corrupted center pixel which may also be a noisy value. In addition to that, unlike the median filter, mean filter, smoothens the image. To overcome the above said issue, the threshold based noise detection mechanism is proposed in this paper, instead of direct detection. Recently a new adaptive weighted mean filter (NAWMF) has been proposed in [12].

Over the years, hardware implementation of median filter has been attempted in software and also available in the DSP processor environment. Since the VLSI implementation of median filters employs a sorting technique, it is the major concern to implement the median filter in hardware for real time application. In [13], an architecture of rank based 2D median filter is implemented in FPGA. The high throughput VLSI architecture for an existing median filter introduced in [14] and pipelined median filter architecture introduced in [15] reduce the cell count, but they have not processed the real time image. The optimized sorting architecture for the median filter introduced in [16] is an efficient architecture, but it is not implemented in image processing applications. The high speed pipelined architecture for adaptive median filtering algorithm proposed in [17] uses parallel sorting architecture to find the median value. Though parallel sorting architecture increases the speed of operation, hardware complexity is also increased. The major challenges in various kinds of the architecture oriented median filtering algorithms are their computational time and hardware cost.

In this paper, an efficient VLSI architecture for the proposed switching based median filter is also presented. The rest of the paper is organized as follows. In Section II, the proposed median filtering algorithm is described. The VLSI architecture of proposed median filter is discussed in Section III. Section IV presents the simulation and implementation results. Finally, the conclusion is given in Section V.

II. PROPOSED SWITCHING MEDIAN FILTER The proposed algorithm process each and every pixel in

the noisy image to detect the presence of noise and filtering is applied if it is corrupted otherwise left unaltered. The fixed valued impulse noise corrupted pixels can take either maximum (Smax) or minimum (Smin) intensity values in the dynamic range [0, 255]. If the processing pixel lies within the range Smin < Xij < Smax, then it is noise free and actual value is retained. If it is identified as noisy as described in the following algorithm steps, then the median value of neighborhood pixels is used to replace the noisy intensity value. The proposed algorithm is described in the following steps. Algorithm Steps: Step 1: Apply a 3 x 3 filtering mask Sij as shown in figure.1, centered about the processing pixel Xij in the noisy image. The minimum intensity values (Smin) and maximum intensity value (Smax) are determined by sorting the elements in Sij

Step 2: If Smin = 0 or Smax = 255, then continue, else go to step6. Step 3: Get the noise free pixel intensity values in the filtering mask whose coordinates are defined in equation (1) into an array U. If the array U is empty, then going to step5, else continue.

Sij = Xi+k,j+l│k,l;-1:1≠ Smin^ Smax (1) Step 4: Median of the array U is found. If the absolute difference |Xij –median (U)| > T then Xij is the impulse corrupted pixel and it is replaced with the median (U) otherwise left unaltered and go to step7.

Xi-1,j-1 Xi-1,j Xi-1,j+1

Xi,j-1 Xi,j Xi,j+1

Xi+1,j-1 Xi+1,j Xi+1,j+1

Fig 1. 3 x 3 filtering mask

Step 5: The median of Sij as defined in equation (2) is used to replace corrupted Xij and go to step7.

Median (Sij) = Median (Xi+k,j+l│-1< k,l < 1) (2) Step 6: The processing pixel Xij is uncorrupted and actual intensity value is retained as such. Step 7: Repeat the process from step1 for all the pixels in the noisy image to restore the corrupted pixel intensities. The proposed algorithm is tested on different images with different characteristic like, images with smooth regions and images with high frequency details. The optimum threshold (T) value is obtained based on trial and error approach and found that, it is in the range of 25 ≤ T ≤ 30 for better restoration and edge preservation.

III. ARCHITECTURE OF PROPOSED MEDIAN FILTER The architecture of proposed median filter mainly consists

of noise detector, sorting network and switching stage as shown in figure 2.

A. Noise Detectors Generally the images are corrupted with salt and pepper

noise during the image acquisition and transmission process. Due to the addition of salt and pepper noise also called as fixed valued impulse noise, the pixel value gets modified to

Fig 2. Block Diagram of Proposed Median Filter



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either minimum gray scale value (0) or maximum gray scale value (255). The two types of noise detector namely salt noise detector (all one detector) which detects the maximum gray scale value and pepper noise detector (all zero detector) which detects the minimum gray scale value are used to detect the fixed valued impulse noise. (i) Salt Noise detector

The figure 3 shows the logic diagram and boolean expression of the salt noise detector. It detects whether all the bits of a pixel intensity value are one (255) or not.

(ii) Noise Detector

The figure 4 shows the logic diagram and boolean

expression of pepper noise detector. It detects whether all the bits of a pixel intensity value are zero (0) or not.

B. Sorting network Let us consider a sorting of, 9 elements in an array, where

‘I’ represents the current pixel intensity value to be sorted and ‘J’ represents the rest of the elements in an array. Algorithm Step 1: The value ‘I’ is compared with the rest of the ‘J’ values. Step 2: If ‘I’ is less than ‘J’, then shift the ‘I’ value to the temporary register ‘t’. Step 3: Shift the ‘J’ value to ‘I’ register. Step 4: Shift the value in temporary register to ‘J’ register. Step 5: If ‘I’ is not less than ‘J’, then go to step 1

C. Switching Stage The switching stage as shown in figure 5 consists of

mainly a threshold detector and multiplexer. where, TV- threshold value.

CV- current pixel intensity value.

MV-median value of noise free neighborhood. The threshold detector checks whether the input value, that is the absolute difference between processing pixel intensity value and the median of noise free neighborhood in 3 x 3 sliding window lies in the range of 27 to 30 or not. If it lies in that range, then the output of threshold detector is one, otherwise it is zero. The figure 6 shows the logic diagram and boolean expression of the threshold detector. The inputs to the threshold detector are 8 bits of the absolute difference T(8), T(7),…T(0) and their inverted values T(8)’, T(7)’,…T(0)’. It detects whether the absolute difference lies within the threshold range [27-30] with a high level output, which is given to the selection line of the multiplexer. If the output of threshold detector is logic ‘1’, then the multiplexer selects the actual pixel intensity value Xij otherwise it selects the median value already obtained.

T(6)'

T(3)

T(7)'T(8)'

T(3)

F

T(4)

T(2)

T(2)'

T(5)

T(1)

Fig 6. Logic Diagram of Threshold Detector

Fig 4. Logic Diagram of Pepper Noise Detector

Let us consider an input A=A7, A6…A0 Z = A7 | A6| A5 | A4 | A3 | A2 | A1 | A0 ; f (0) = not(Z); If f (0) = 1 then A is corrupted pixel (Pepper Noise detected); Else A is uncorrupted Pixel;

Fig 3. Logic Diagram of Salt Noise detector

Let us consider an input A=A7, A6…A0 f (1)= A7 & A6& A5 & A4 & A3 & A2 & A1 & A0 ;

If f (1) = 1 then A is corrupted pixel (Salt Noise detected); Else A is uncorrupted Pixel;

)7()6()5()4()3()2()1()0()1( zzzzzzzzf

)7()6()5()4()3()2()1()0( '0 zzzzzzzzf



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)2())1()3()2()3()4()5()6()7()8( TTTTTTTTTTF

IV. ILLUSTRATION The proposed denoising algorithm is illustrated by

considering a 3 x 3 window for three different cases as given below and the simulation result of VHDL implementation for the same three cases is also shown in figure. 7.

Case (i):

The intensity value of the center pixel in a 3 x 3 sliding window lies between ‘0’ and ‘255’ (ie.uncorrupted) and few remaining pixels are with both salt and pepper noisy intensity value. The noise free pixel intensity values (other than ‘0’ and ‘255’) in the filtering mask are collected in an array ‘U’. Since the absolute difference between center pixel and median value of the array ‘U’ is lesser than 30, Xij is retained with the same intensity value (67).

257543067255

78560

U = (43, 56, 75, 78) Median value of ‘U’ is ‘66’. (Xij – median) is 67-66=1 and 1 < 30

Case (ii): In this case center pixel is having the noisy intensity

value ‘0’ (ie.corrupted) and 60% of remaining pixels are also with both salt and pepper noisy intensity value. The noise free pixel intensity values (other than ‘0’ and ‘255’) in the filtering mask are collected in an array ‘U’. The absolute difference between center pixel and the median value is greater than 30 and Xij is replaced with the median value (153).

255025500148

161255153

U = (148, 153, 161) Median value of ‘U’ is’153’. (Xij – median) is 0-153 = 153 and 153 > 30

Case (iii): In this case center pixel is having the noisy intensity value ‘0’ and all other remaining pixels are also with both salt and pepper noisy intensity value. Since all the elements in the filtering mask are ‘0’ and ‘255’ (ie.corrupted), the median value of the 3 x 3 filtering mask ‘0’ is used to replace Xij.

25525500025502550

Median value of ( 0, 0, 0, 0, 0, 255, 255, 255) is ‘0’.

IV. RESULTS AND DISCUSSION

A. Simulation Results In this section the extensive experiments are conducted on

a variety of standard gray scale test images like Darkhair,

Living room, Satellite and Mandril of size 512 x 512 with gray level intensity of 8 bits/pixel for noise level varying from 10% to 90% to evaluate the performance of the proposed algorithm. The visual results and quantitative results of proposed filter are compared with existing algorithms, namely SMF, ACWMF, TMF, PWMAD, ARWMF, REBF, MDBUTMF and NAWNF. The restoration performance and processing time for proposed filter and existing filters are analyzed under the following subsections (i) and (ii). Based on the experimental results, it is observed that to attain better visual quality and PSNR value, the threshold value for noise detection is found as 30. (i). Quantitative and visual results Comparisons

The quantitative results in terms of peak signal-to-noise ratio (PSNR), Mean Absolute Error (MAE), and Image Enhancement Factor (IEF) are presented in table I-XII to illustrate the performance of the proposed filtering algorithm. The experiments are conducted using three test images, namely Darkhair, Satellite and Mandril that contain different characteristics like more smooth region and more edge detail. The SMF, ACWMF and PWMAD perform well only for very low noise density. The PSNR value obtained by TMF algorithm is slightly better than the above said algorithm due to its noise detection capability. The ARWMF uses adaptive window size to remove higher density noise and weight calculation for weighted median filtering is done iteratively till the least mean square error is obtained. Hence the better PSNR value obtained and minimum mean absolute error are obtained than SMF, ACWMF, PWAMD and TMF.

The quantitative and qualitative performance of robust estimation based filter is equally good to proposed algorithm due to the following reasons. The first one is that, REBF uses adaptive window size based on noise density and the second is due to the calculation of influence function based on the local estimate of image standard deviation which is also used to find the estimated value of the corrupted pixel. The MDBUTMF works equally better to proposed filter for noise density up to 70%, but the performance is degraded at higher noise level. The recently proposed NAWMF gives better PSNR value and less MAE value than proposed filtering algorithm, since the window size is enlarged till the minimum and maximum intensity values of two successive windows are equal which leads to better noise detection and filtering mechanism. But it takes longer CPU time to run and needs complex hardware architecture for real time implementation.

The MAE and IEF displayed in tables II, III, V, VI, VIII & IX show that the proposed filtering algorithm produces better IEF and less MAE than other existing algorithms for test images with various characteristic. The visual result of proposed algorithm and other existing algorithms for all the four test images, namely, Dark hair, Living room, Satellite and Mandril corrupted with 30%, 60% and 90% are presented in figures 8, 9 and 10 which show that the proposed filter performs better in qualitative aspect also.



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F ig 7. Simulation result of VHDL implementation of the proposed algorithm for the cases (i-iii). TABLE I

PSNR COMPARISION OF VAIROUS FILTERS FOR DIFFERENT NOISE DENSITY

Noise%

DARKHAIR IMAGE

SMF TMF ACWMF PWMAD ARWMF REBF MDBUTMF NAWMF PROPOSED

10 39.22 40.95 39.40 35.57 32.05 42.38 45.04 42.60 45.70 20 3084 35.51 33.73 29.70 31.96 40.71 41.32 41.21 42.43 30 24.3 30.11 29.25 24.13 31.78 38.28 38.71 39.72 40.43 40 19.06 25.65 26.11 18.98 31.54 36.31 36.11 38.27 38.53 50 15.09 22.03 23.12 15.14 31.433 34.76 33.94 36.88 37.19 60 12.05 19.03 19.72 12.03 29.86 33.30 31.27 35.26 34.92 70 9.63 16.34 16.07 9.60 28.99 31.44 27.87 33.55 32.75 80 7.71 14.29 12.37 7.71 24.45 29.44 23.90 31.37 29.67 90 6.21 12.41 8.37 6.21 21.88 25.76 17.70 28.66 25.03

TABLE II

MAE COMPARISION OF VAIROUS FILTERS FOR DIFFERENT NOISE DENSITY

Noise%

DARKHAIR IMAGE


10 0.736 0.449 0.309 0.808 3.243 0.263 0.086 0.145 0.085 20 0.969 1.156 0.781 1.123 3.315 0.432 0.188 0.206 0.180 30 1.563 2.692 1.513 1.830 3.645 0.671 0.303 0.465 0.278 40 3.180 5.932 2.683 3.729 3.857 0.968 0.468 0.718 0.392 50 6.694 12.12 4.537 7.410 3.962 1.253 0.644 1.332 0.521 60 11.82 22.60 8.036 13.05 4.135 1.611 0.922 1.388 0.704 70 19.47 39.95 15.16 21.98 4.263 2.070 1.376 1.502 0.978 80 29.70 63.00 29.16 32.61 6.493 2.685 2.261 1.663 1.439 90 41.056 94.95 63.72 43.90 8.817 3.779 5.183 2.815 2.793



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TABLE III

IEF COMPARISION OF VAIROUS FILTERS FOR DIFFERENT NOISE DENSITY

Noise%

DARKHAIR IMAGE


10 99.18 114.7 272.3 207.0 625.9 2278 2364 2630 2340 20 195.1 79.94 65.69 446.3 3245 2189 2222 4041 2261 30 138.4 77.08 42.76 335.1 4218 1835 1842 4215 2234 40 52.30 55.87 24.88 130.3 2568 1428 1580 3941 2091 50 19.48 30.78 28.57 48.48 1463 1329 1228 3991 1916 60 7.936 17.76 18.20 18.30 878.0 885 924 3332 1690 70 3.768 8.706 12.14 8.66 636.1 556 532 2724 1451 80 2.132 4.810 8.255 4.724 325.5 349 258 2056 1120 90 1.372 2.878 2.814 2.933 95.46 116 62 1320 643

TABLE IV

PSNR COMPARISION OF VAIROUS FILTERS FOR DIFFERENT NOISE DENSITY

Noise%

SATELITE IMAGE


10 26.36 35.31 28.84 26.12 25.00 38.38 31.52 31.64 31.64 20 24.47 31.56 25.72 23.91 24.89 34.62 26.93 33.18 29.21 30 21.14 27.92 23.02 21.05 24.76 31.80 24.55 31.40 27.82 40 17.95 24.44 21.000 17.55 24.62 29.57 22.36 29.68 26.63

50 14.69 21.57 18.83 14.35 24.40 27.44 20.55 27.11 25.50 60 12.08 18.76 16.54 11.77 23.63 25.64 18.72 26.47 24.03 70 9.90 16.36 14.05 9.64 22.64 23.78 16.66 24.58 22.70 80 8.07 14.36 11.41 7.85 20.78 21.49 14.44 22.31 20.92 90 6.59 12.70 8.407 6.54 18.15 18.72 12.05 21.33 18.03

TABLE V

MAE COMPARISION OF VAIROUS FILTERS FOR DIFFERENT NOISE DENSITY

Noise%

SATELITE IMAGE


10 3.685 1.939 1.971 3.951 8.630 0.434 0.495 2.032 0.809 20 4.387 3.8888 3.836 4.835 8.758 0.863 1.020 3.407 1.434 30 5.599 6.847 6.353 6.327 8.988 1.432 1.716 3.578 1.943

40 8.022 11.63 9.413 9.061 9.057 2.160 2.538 4.966 2.505 50 12.02 18.80 13.51 14.51 9.202 2.915 3.403 4.994 3.139 60 19.07 30.56 19.54 22.31 10.36 3.966 4.705 5.851 4.014 70 28.50 48.19 28.54 32.45 11.12 5.265 6.589 7.414 4.836 80 41.07 71.67 44.09 45.73 14.48 9.344 9.682 7.804 6.423 90 53.87 100.1 73.31 59.61 17.68 11.07 14.64 7.888 10.25

TABLE VI

IEF COMPARISION OF VAIROUS FILTERS FOR DIFFERENT NOISE DENSITY

Noise%

SATELITE IMAGE


10 3.103 11.01 4.243 6.707 45.15 59.52 63.81 93.90 49.76 20 7.970 7.789 3.677 17.76 72.58 54.68 59.05 230.5 57.57

30 10.16 6.823 2.750 25.16 101.6 51.66 51.89 290.8 64.51

40 9.464 6.128 2.774 22.61 245.5 48.34 41.05 310.6 68.10

50 6.100 4.778 2.293 15.94 325.6 43.69 33.89 287.7 67.23

60 4.035 3.452 1.997 10.09 183.8 39.78 25.02 236.7 61.92

70 2.694 2.233 1.113 6.019 154.7 37.25 17.36 166.8 53.37

80 1.807 1.762 0.897 4.022 93.64 32.66 11.76 97.10 43.84

90 1.235 1.269 0.510 2.900 72.55 28.78 6.162 95.23 27.59



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TABLE VII PSNR COMPARISION OF VAIROUS FILTERS FOR DIFFERENT NOISE DENSITY

Noise%

MANDRIL IMAGE


10 28.93 37.63 32.09 28.58 22.45 34.77 33.90 31.15 36.44 20 26.81 33.79 28.81 26.48 22.31 32.84 30.17 29.66 33.74

30 22.73 29.39 26.48 22.49 22.18 30.78 27.53 28.53 32.10

40 18.70 25.62 24.12 18.69 22.09 28.74 25.51 27.58 30.64

50 15.2412. 22.32 21.65 15.12 22.00 27.22 23.50 26.25 29.22

60 12.44 19.39 19.06 12.22 21.12 25.56 21.62 24.94 27.72

70 10.08 16.94 16.28 9.91 20.96 23.97 19.81 23.30 25.92

80 8.28 14.79 12.75 8.11 18.82 22.46 17.07 21.43 23.81

90 6.81 12.99 8.886 6.73 18.22 20.57 15.87 19.00 21.11

TABLE VIII

MAE COMPARISON OF VAIROUS FILTERS FOR DIFFERENT NOISE DENSITY

Noise%

MANDRIL IMAGE


10 2.684 1.366 1.319 2.794 12.18 0.9416 0.332 1.781 0.402 20 3.166 2.789 2.603 3.360 12.42 1.566 0.747 0.899 0.776

30 4.323 5.204 4.159 4.660 12.66 2.411 1.235 1.677 1.143

40 6.505 9.152 6.164 7.023 12.81 3.548 1.770 1.151 1.568

50 10.55 15.85 8.930 11.39 12.92 4.648 2.533 4.147 2.079

60 16.70 26.90 13.06 19.01 13.63 6.169 3.427 4.858 2.639

70 25.97 43.21 19.63 29.07 14.68 7.942 4.638 4.962 3.510

80 36.43 66.55 34.14 40.94 17.15 10.29 6.095 5.662 4.753

90 49.91 97.17 67.37 52.96 20.79 13.87 8.155 6.174 7.116

TABLE IX

IEF COMPARISON OF VAIROUS FILTERS FOR DIFFERENT NOISE DENSITY

Noise%

MANDRIL IMAGE


10 5.811 22.92 26.26 11.82 96.52 218.6 145.8 84.73 221.2 20 16.04 19.65 14.58 33.62 105.6 152.3 134.6 131.4 242.8 30 19.65 14.634 8.607 43.52 111.7 107.9 110.5 158.7 256.3

40 15.53 9.124 6.508 37.30 93.48 84.88 89.78 163.0 237.9 50 9.444 6.333 3.403 21.74 61.25 68.86 71.83 156.3 223.5 60 5.249 4.877 2.219 12.26 42.58 50.12 55.36 137.5 188.0 70 3.059 2.932 2.262 6.911 35.23 40.56 41.91 108.1 151.1 80 1.900 2.252 1.264 4.213 22.68 31.23 29.71 80.7 109.7 90 1.334 1.709 0.820 2.834 18.96 17.56 15.96 51.95 70.52

(ii). Computation time Comparison

The performance of the proposed algorithm is also evaluated quantitatively in terms of CPU time and compared with other existing algorithm for all four test images, namely Lena, Living room, Satellite and Mandril by varying noise density from 10% to 90%. The proposed filter and other existing algorithms are simulated in MATLAB7.1 on a PC equipped with 2GHZ operating speed 2GB RAM and demonstrated in the tables X, XI & XII.

The run time for SMF, PWMAD and TMF is lesser than the proposed algorithm, since there is no or inefficient detection mechanism involved in those filtering algorithms. Also, their visual results and other performance metrics are not as good as proposed algorithm. It is seen from the table that, the adaptive window algorithms ACWMF, NAWMF

and REBF take more computation time than the proposed fixed window algorithm, since the number of pixels to be processed is more, especially when the window size is very large at higher noise density. Similarly, the ARWMF is also executed with much more CPU time than all the other algorithms, due to the number of iterations invoked in weight calculation is more. It is also inferred from the table that, though CPU takes lesser run time to execute MDBUTMF for low noise density, execution time is increased for noise ratio greater than 70% which is more than the proposed algorithm. Hence it is clear from the above analysis that, the proposed algorithm shows better performance quantitatively without compromising quality in visual results.



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TABLE X

RUN TIME COMPARISION OF VAIROUS FILTERS FOR DIFFERENT NOISE DENSITY

Noise%

DARKHAIR IMAGE

SMF TMF ACWMF PWMAD ARWMF RE BF MDBUTMF NAWMF PROPOSED

10 3.432 0.843 14.25 0.392 952.6 19.02 1.356 29.85 4.326 20 3.349 0.286 14.72 0229 937.1 20.92 2.495 20.21 5.375

30 3.556 0.321 15.03 0.226 892.0 23.89 3.524 14.99 5.383

40 3.310 0.345 14.85 0.229 976.5 26.24 4.770 14.75 5.871

50 3.375 0.299 14.49 0.231 1003 29.08 5.800 10.82 5.448

60 3.395 0.295 14.62 0.230 1334 32.21 6.868 10.32 6.082

70 3.722 0.308 14.72 0.225 1565 38.63 8.516 10.33 5.673

80 3.498 0.303 14.56 0.216 3168 46.06 9.289 11.18 6.515

90 3.724 0.326 14.37 0.204 4308 60.39 11.33 15.95 5.744

TABLE XI

RUN TIME RUN TIME COMPARISION OF VAIROUS FILTERS FOR DIFFERENT NOISE DENSITY

Noise%

SATELITE IMAGE


10 0.829 0.174 3.682 0.054 779.3 17.64 0.359 32.51 1.027 20 0.911 0.162 3.897 0.055 781.5 19.38 0.647 20.04 1.197 30 09.12 0.1577 3.787 0.056 787.2 23.15 0.913 14.88 1.381 40 0.997 0.159 3.997 0.056 842.1 25.26 1.207 12.71 1.286

50 09.02 0.158 3.791 0.060 910.6 28.52 1.485 11.27 1.326 60 0.950 0.194 3.685 0.055 986.5 31.87 1.790 10.93 1.332 70 0.947 0.163 3.794 0.052 1453 37.52 2.047 10.63 1.385 80 0.944 0.162 3.765 0.049 2947 45.84 2.521 11.53 1.406 90 0.932 0.169 3.795 0.059 3978 59.18 2.618 11.52 1.358

TABLE XII

RUN TIME COMPARISION OF VAIROUS FILTERS FOR DIFFERENT NOISE DENSITY

Nois

e%

MANDRIL IMAGE


10 3.750 0.274 15.21 0.231 725.4 18.16 1.442 31.14 3.933 20 3.469 0.289 14.38 0.237 733.5 19.65 2.552 20.00 4.933

30 3.765 0.279 15.06 0.242 738.6 22.10 3.712 15.21 5.333

40 3.546 0.334 15.01 0.243 798.5 25.76 4.723 12.75 5.487

50 3.599 0.286 14.98 0.245 864.2 28.60 6.052 11.02 5.925

60 3.461 0.290 14.44 0.236 1158 32.87 6.927 10.24 5.965

70 3.652 0.287 14.77 0.228 1410 37.82 8.174 10.15 5.962

80 3.401 0.291 14.69 0.217 2938 45.72 10.27 11.17 5.709

90 3.482 0.311 15.17 0.204 4136 59.31 11.77 15.94 5.740

B. Hardware implementation results The VLSI architecture of proposed trimmed median

filtering algorithm is described with VHDL. The Xilinx

10.1 Modelsim 6.2. The developed algorithm is tested with 256 x is used to produce a gate level net list and synthesized using 256, 8-bits/pixel gray scale Lena image for 60% salt and pepper noise density. The intensity values in rows of noisy image are stored as column vectors in a file and processed in proposed architecture and restored intensity values are stored in another file. Finally MATLAB tool is used to convert the estimated value of all pixel intensities available as column vectors in a file into an image as per the experimental setup shown in figure 11. The restoration result of 60% noise corrupted Lena image is also shown in figure 12.

The logic element comparisons were made in ultra Quartus II [23]. The Quartus II power play analyzer tool is used to measure the power Consumption and delay analysis

Fig 11. Experimental Setup



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is made in Quartus II classic timing analyzer tool. The table XIII shows the simulation results of the proposed

architecture for a 3x3 window. It can be seen from the

table XIII that, the architecture of REBF and NAWMF need more logic element which increases the power consumption and hence more PDP, due to the requirement of more comparator and multiplier units. Also at higher noise density the logic elements required for processing the current pixel and calculating the estimated value of the corrupted pixel have increased much due to larger window size. Since the ARWMF is executed in an iterative manner, the hardware implementation is much complicated. It is also inferred from the table that, the VLSI architecture for proposed filtering algorithm achieves better PDP than other existing filtering algorithm except MDBUTMF. The reason is that, there is no threshold comparison step in MDBUTMF as used in the proposed filtering algorithm to identify the presence of noise. Since the efficient noise detection mechanism is involved to achieve better PSNR value, the architecture of the proposed filter needs additional circuitry which increases the required logic elements which in turn increases the PDP. Though logic elements required and PDP obtained for the proposed filter are almost equivalent to the other fixed window algorithms like SMF and MDBUTMF, the proposed algorithm gives better quantitative and qualitative results than the same.

TABLE XIII

COMPARISON OF LOGIC ELEMENTS, POWER, DELAY AND PDP FOR A SINGLE 3X3 WINDOW PROCESSING

PARAMETER/ ALGORITHM

LOGIC ELELMENTS

TOTAL POWER

(mW)

DELAY (nS)

PDP (pJ)

SMF [3] 589 167.73 7.646 1282

Decision Based Algorithm [10]

594

171.51

7.737

1326

Parallel Sorting

[17] 613 187.28 9.110 1703

MDBUTMF [11] 603 168.94 7.949 1342

REBF [10] 654 181.5 9.32 1691

NAWMF [9] 666 203.8 10.95 2231

Proposed 621 170.46 7.974 1359

(a). Original (b). Noisy (c). Restored Fig 12. Simulation result of VLSI architecture of proposed algorithm

for 60% noise corrupted Lena image.

(a) (b) (c) (d)

Fig 8. Restoration results of (a). Darkhair, (b). Livingroom, (c). Satellite, (d). Mandril image using various filters namely SMF, TMF, ACWMF,

PWMAD, ARWMF, REBF,MDBUTMF, NAWMF and proposed algorithm for 30% salt and pepper noise density.



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(a) (b) (c) (d)


PWMAD, ARWMF, REBF,MDBUTMF, NAWMF and proposed algorithm for 60% salt and pepper noise density.

(a) (b) (c) (d)


PWMAD, ARWMF, REBF,MDBUTMF, NAWMF and proposed algorithm for 90% salt and pepper noise density



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VI. CONCLUSION A new switching based trimmed median filter using 3 x 3

filtering window for effective removal of high density salt and pepper noise and its VLSI architecture is proposed in this work. The image degradation caused from undetected noisy pixels is prevented due to the better noise detection capability of the proposed algorithm. The experimental results in terms of qualitative and quantitative metrics of proposed algorithm and other state of art technique are compared and better performance of proposed filter is demonstrated. In addition to that, VLSI architecture of the proposed filtering algorithm is also implemented and performance in terms of logic element, delay and power delay product is compared with other existing algorithms which clearly show the simplicity of the porposed architecture.

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VLSI Architecture of Switching Median Filter for Salt and Pepper … · 2016-02-28 · Abstract— In this paper, VLSI architecture of new switching based median filter to remove