A New Detection Statistics for Removal of Impulsive Noise

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ISSUE2, VOLUME 1 (FEBRUARY 2012) ISSN: 2250-1797 ISSN: 2250 -1797 A New Detection Statistics for Removal of

ISSUE2, VOLUME 1 (FEBRUARY 2012)Impulsive Noise ISSN: 2250-1797

P.Neelavathi Assistant Professor, Department of CSE, Sengunthar College of Engineering, Tiruchengode. ISSUE2, VOLUME 1 (FEBRUARY 2012) [email protected] ISSN: 2250-1797

Abstract In digital Image Processing, removal of noise is a highly demanded area of research. ImpulsiveISSUE 2, noise VOLUME is 1 common(FEBRUARY 2012) in images which arise at the time of image ISSN: 22 acquisition50-1797 or transmission of images. Impulsive noise can be classified into two categories, namely Salt & Pepper Noise (SPN) and Random Valued Impulsive Noise (RVIN). Removal of SPN is easier as compared to RVIN due to its characteristics. A new method for removal of RVIN from images is presented. The originality of this method is that no a priori threshold is needed as in the case of classical switching median filters. Instead, threshold is computed locally from image pixels intensity values in a sliding window. Results show that this method provides better performance in terms of PSNR and MSE than many other median filter variants for random valued impluse noise. In addition it can preserve more image details in high noise environment. Index Terms—Detail-preserving, image restoration, impulse noise detection, switching median filter.

I. INTRODUCTION

IMAGES are frequently corrupted by impulse noise due to camera sensors or transmission in noisy channels . It occurs in bioluminescence imaging for which the image acquisition is disturbed by the presence of cosmic noise [2]. Cosmic noise can be considered to be an impulse noise. For this application in bioluminescence imaging, a step of filtering is needed a pre-processing step before the deconvolution task [2]. The main approach for removing impulse noise is to use median-based filters (see, e.g., [1], [3]). However, since filters are usually implemented identically across the images, they tend to modify both noise and noise-free pixels. Consequently, some desirable details can be removed [1]. To overcome this problem, many modified forms of median filters were proposed among which is the weighted median filter [4] and the center weighted median (CWM) filter [5].Those filters tend to work well with low noise level, but poorly for highly corrupted images. To solve this problem, the switching median (SWM) filter [6] was introduced. The main idea of the SWM is to use an impulse detector before filtering. This detector is based on an a priori Threshold value to decide if a median filter is to be applied or not. Next, many other approaches were proposed such as tri-state median (TSM) filter [7] and more recently, alpha-trimmed mean-based approach (ATMA) [8], directional weighted median (DWM) filter [9], and modified switching median (MSWM) filter [10]. All of these filters usually perform well in terms of Peak Signal-to-Noise Ratio (PSNR) and Mean Absolute Error (MAE). However, the principle drawback is that they have limited performance in terms of false and missed detections Hence, they cannot preserve the image details and especially when the noise is high.

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ISSUE2, VOLUME 1 (FEBRUARY 2012) ISSN: 2250-1797 ISSN: 2250 -1797 To reduce these effects and improve impulse noise detection, we propose a new Adaptive thresholding scheme. The originality of scheme is that no a priori Threshold is to be given as in the case of a classical SWM filter. Instead, the threshold is computed locally from imageISSUE2, VpixelsOLUME 1intensity (FEBRUARY values 2012) in a sliding window using weighted ISSN:statistics. 2250- 1797Thus, it is expected that this new strategy will lead to better performances.

This letter is organized as follows. In Section II, we review the impulse noise removal principle using SWM.The ASWM algorithm is described in Section III. Section IV,V and VI gives simulation results using different test images to demonstrate the performances of the new approach.ISSUE2, VOLUME Finally, 1 (FEBRUARY conclusions 2012) are no ted in Section VII. ISSN: 2250-1797

II.SWITCHING MEDIAN FILTER (SWM) ISSUE2, VOLUME 1 (FEBRUARY 2012) ISSN: 2250-1797 The principle of the SWM filter is reminded. A SWM filter is a two steps procedure. First, a test decides whether or not a given pixel is contaminated by impulse noise: a pixel is contaminated if the absolute difference between the median value in its neighbourhood and the value of the current pixel itself is greater than a given Threshold [6]. If contaminated, a classical median fitter is applied; if not, the current pixel is noise free and will be not modified. More precisely, consider an image corrupted by an impulse noise and the grey level value at position , Let be a square window surrounding this pixel. This window is of size (2L+1)X(2L+1) where L is an integer greater than zero. The output of the switching median filter is given by

(1)

Where m(i,j) is the median value in the window W and Threshold is a fixed parameter. The numerical Threshold value is defined a priori or chosen after many data dependant tests. The literature shows that an optimal threshold in the sense of the mean square error can be obtained for most real data [6], [7]. However, Threshold suitable for particular image is not necessarily adapted to another one. In addition, an image is often non stationary and statistics in a region may be different in other part of the image. The next section presents the new method that does not require a priori knowledge and automatically defines a local Threshold.

III.ADAPTIVE THRESHOLDING SCHEME The proposed method has the same general structure as the SWM filter. The difference between the new method and SWM relies on the fact that Threshold is not an a priori choice but is computed locally from image pixels. For that reason, we called it the Adaptive scheme. More precisely, the weighted mean value and the weighted standard deviation are estimated in the current window. The weights are the inverse of the distance between the weighted mean value of pixels in a given window and the considered pixel. A result is that impulse noise does not corrupt the determination of these statistics from which the Threshold is derived. In each window, the weighted mean are first iteratively estimated. Then the weighted standard deviation is calculated and the Threshold is determined. This procedure is explained in the following.

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ISSUE2, VOLUME 1 (FEBRUARY 2012) ISSN: 2250-1797 ISSN: 2250 -1797

Step.1: Initialize a window (w) size, k=3.with all of its weights equal to 1 at initialization.

Step.2:ISSUE Compute2, VOLUME 1 weighted (FEBRUARY 2012) mean of gray - level values in ISSN: the 2250 window-1797 under consideration using equation

(2) ISSUE2, VOLUME 1 (FEBRUARY 2012) ISSN: 2250-1797 Step.3: Compute the updated weight of the window using the equation (5.2) given below

ISSUE2, VOLUME 1 (FEBRUARY 2012) ISSN: 2250-1797 (3)

Where Step.4: the above calculations are iteratively done at t= 3 to 9 , where t is the no of iteration Step.5: continue iteration where ε=0.01

Step.6: when above condition satisfied calculate the weighted standard deviation

(4)

Step.7: from above compute a local threshold T= (5) Finally, Method can be summarized as follows:

a) Compute the weighted mean and the weighted standard deviation in the window surrounding the current pixel as described above. b) Use the following rule:

(6)

Where m(i,j) is the trimmed mean in the window , is a given parameter and represents the local threshold. This method is applied recursively and iteratively. During the iterations, in each window, the threshold is decreased as proposed by Dong [9]. This is done by varying the α value. From simulation conducted on a broad variety of images, the following strategy for α yields satisfactory results, that is and

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ISSUE2, VOLUME 1 (FEBRUARY 2012) ISSN: 2250-1797 ISSN: 2250 -1797 Where α0 is the initial parameter and the αn parameter in the nth step. In Fig. 1, we show the values of across the “Lena” image degraded by (a) 30% and (b) 60% of random-value impulsive noise respectively. Here, we have chosen a 3x 3 square windowISSUE ,δ=0.1,ε=0.012, VOLUME 1 (FEBRUARY and 6 2012) iterations were used (that mean s, , ISSN:and 2250, -1797 [see (6)].

ISSUE2, VOLUME 1 (FEBRUARY 2012) ISSN: 2250-1797

Fig. 1. Values of for Lena image degraded respectively by (a) 30% of Random-value impulsive noise and (b) 60% of random-value impulsive noise. From the images of Fig. 1, we observed that is quite different depending on its location in the image: while is low in uniform regions, it shows high values in textured regions or in presence of edges. This adaptability is an important point to preserve image details. Besides, the noise percentage does not change this general behavior.

IV.RESULTS In this section, the restoration property, the noise detection capability of ASWM (adaptive SWM) and the visual performances are evaluated and compared to a number of existing median-based filters used to remove random-valued impulse noise. Commonly, most authors use the peak signal-to-noise ratio (PSNR) and mean absolute error (MAE) to quantify the restoration results .To complete comparisons, authors of [9] compute the number of missed noisy pixels and the number of noise-free pixels that are identified as noise to show the efficiency of their method. In the same aim, authors of [10] defined a noise detection rate.

V.RESTORATION PERFORMANCE MEASUREMENTS Restoration performances are evaluated quantitatively by using PSNR and MAE, which are defined as in [3]. We compare presented method to other well known methods like adaptive center weighted median filter(ACWM),pixel wise median of absolute deviation based filter(PWMAD),pinar civi method of detection(PCM) As test images we adopted the well known images Lena,Boat,Peppers, Goldhill,” We have applied Monte Carlo simulations. Each image is 100 times degraded by a random impulse noise. The obtained mean PSNR and mean MAE are reported in TableI.Results show that ASWM performs better than other considered methods and achieved the image quality with best PSNR and MAE for random valued impulse noise.

TABLE I Page 52

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ISSUE2, VOLUME 1 (FEBRUARY 2012) ISSN: 2250-1797 ISSN: 2250 -1797 PSNR (DB) VALUES FOR 30% RANDOM-VALUED IMPULSIVE NOISE BEST RESULTS ARE BOLD PRINTED.

ISSUE2, VOLUME 1 (FEBRUARY 2012) ISSN: 2250-1797 Filters Lena Boat Peppers Goldhill

ACWM 27.69 24.32 28.5 23.55

PWMAD 28.52 25.68 30.55 24.80 ISSUE2, VOLUME 1 (FEBRUARY 2012) ISSN: 2250-1797 PCM 31.66 27.71 32.66 27.71 DWM 32.41 28.53 34.13 30.22

SSUE OLUME EBRUARY I 2,proposedV 1 (F 2012)33.09 29.33 35.66 ISSN:30.812250 -1797

Fig. 2. Performance comparison of different methods for filtering the degraded by various levels of random-value impulsive noise

The performances of ASWM and other considered median based filters for “Peppers” image in term of PSNR for random valued impulse noise with different noise densities are reported in Fig. 2.Results show that proposed scheme performs well for the tested range data corrupted with various noise percentages up to 60%. Results are similar in term of MAE. This confirms that proposed method achieves good restoration in all range of noise percentage. this method outperforms well when the noise percentage is low and noise is increased above 60% the detection mechanism used here has a limitation with more no of false and miss detections

VI. RESTORATION PERFORMANCES

Restoration results are quantitatively measured by peak signal-to-noise ratio (PSNR), which is defined as in [12].Table I shows the PSNR values of the best results obtained by different filters, where the 512x 512 image “Lena” corrupted with different noise ratios are used. In the proposed method, Page 53

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ISSUE2, VOLUME 1 (FEBRUARY 2012) ISSN: 2250-1797 ISSN: 2250 -1797 It can be seen that our proposed filter provides the best results in PSNR. In particular, when the noise ratio is larger than 30%, the proposed scheme produces PSNR values that are a full decibel higher than the closest competing filter. In order to compare the results ISSUEsubjectively,2, VOLUME 1 (FweEBRUARY give 2012)some restored images in Fig. 3. It is obvious ISSN: that 2250 the-1797 proposed method performs better. There are still a lot of noise patches in the images restored by the others, but by the scheme, noticeable noise is much less

ISSUE2, VOLUME 1 (FEBRUARY 2012) ISSN: 2250-1797

Original image Noisy image

ACWM PWMAD

PCM PROPOSED

Fig. 3. Restoration performance comparison on the “Lena” image degraded by 30% random-value impulsive noise

VII. APPLICATION TO COLOUR IMAGES The proposed filter also can be extended to remove random-valued impulse noise for color images. In the impulse detector, we will treat each color component as an independent entity. Fig. 4 shows the results in restoring 30% corrupted image “Lena” by the vector median filter and proposed filter. It is easy to see that the image restored by the vector median

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ISSUE2, VOLUME 1 (FEBRUARY 2012) ISSN: 2250-1797 ISSN: 2250 -1797 filter is very blurry and still includes many noticeable noise patches, especially for the result in restoring the image with30% noise level. In contrast, the proposed filter performs much better, which can suppress the noise successfully while preserving more details. ISSUE2, VOLUME 1 (FEBRUARY 2012) ISSN: 2250-1797

ISSUE2, VOLUME 1 (FEBRUARY 2012) ISSN: 2250-1797

Fig: Results in restoring 30% corrupted color image “Lena”: (a) vector median filter; (b) proposed filter

VIII. CONCLUSION This paper proposes a new method ,called adaptive thresholding technique which does not need an a priori Threshold as in the case in classical Switching Median filter to detect noisy pixels. Instead, threshold is computed locally from image pixels grey values in a sliding window .the proposed method have shown high noise detection ability. Extensive simulations results indicate that this method performs significantly better than many other existing techniques. In addition, psychovisual results are of high quality. Finally, this method will be used as pre processing to remove cosmic noise for an application in bioluminescence imaging.

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