Abstract-
Noise Suppression from images is one of the most important concerns in digital image processing. Impulsive noise may occur during image acquisition, transmission or storage
Noise should be removed in such a way that important information of image should be preserved. We can use so many algorithms for getting the original image, by removing salt and pepper noise from the corrupted images. In this paper an algorithm is proposed for the restoration of gray scale images that are highly corrupted by impulse noise (salt and pepper noise). There are two phases in the proposed algorithm. First phase detects whether the processing pixel is corrupted or not. In the Second phase it recreates the corrupted pixel by means of the proposed algorithm. This algorithm shows better results than the
Standard Median Filter (MF), Center Weighed Median
Filter(CWM), A daptive Median Filter(AMF), Adaptive Center
Weighed Median Filter (ACWM ), Decision Based Aigorithm
(DBA ) and Modified Decision Based Unsymmetrical Trimmed
Median Filter A 1gorithm(MDBUTMF). Obtained results with different grayscale images shows that proposed algorithm gives better Peak Signal-to-Noise Ratio (PSNR) and less
Computational time and works weil in removing salt and pepper noise at low, medium and high noise densities.
Keywords:
I. INTRODUCTION
N
OISE is an unwanted information that corrupts an image.
In digital images various types of noises are there. Different noise models which can corrupt images are Gaussian,
Impulse, Rayleigh or an erlang and speckle noise. An image can be corrupted by means of Impulse noise, because of faulty camera sensors, errors in data acquisition systems, and transmission through noisy channel. In Impulse noise, intensity of the corrupted pixels will be either relatively high or low [1]- [3]. There are two types of impulse noise, they are salt and pepper noise and random valued noise. In Salt-and­ pepper noise, the gray level value of some of the pixels of an image will be either 255, maximum or 0, minimum. The appearance of noise is as white and black dots superimposed on the image and hence the name salt and pepper noise. In the presence of salt and pepper noise information in the image may be get corrupted. Therefore, removal of this type of noise is critical for the extraction of reliable and accurate information from a digital image [2].
11. BACKGROUND WORK
Several nonlinear filters have been proposed for restoration of images corrupted by salt and pepper noise. Among these standard median filter is the simple method to remove the salt and pepper noise without damaging the edge details. But for this, when the noise level is above 50% the edge details of the original image will not be preserved, works only at low noise densities [3]. In Weighed median (WM) filter and Center
Weighed Median filter (CWM) weights are assigned to selected pixels in the filtering window in order to control the filtering behavior. These filters not checking whether the processing pixel is corrupted or not and process all pixel elements. So at high noise density level these filters fails to reproduce the original image with edge details. In order to avoid the drawback of CWM filter we can go for Adaptive centre weighted median (ACWM) filter. But in this filter we need some threshold values [1], [4].
In adaptive median filter (AMF) window size increases and is effective only at low noise densities [2].
Decision based filter checks for noise in each processing pixel. If the processing pixel is 0 or 255 it is considered as noise and is processed. At high noise density level the DBA filter replaces the noisy pixel by means of the neighborhood pixel [5]. At high noise density level DBA filter produces streaks in the output image because of the continuous replacement and an improved DBA is proposed to avoid this drawback [7]. In DBUTMF instead of replacing with neighborhood pixel unsymmetrical trimmed median value is used. But at high noise densities, in selected window all the pixel elements may be either or both 0 or 255. In this case unsymmetrical trimmed median will be either 0 or 255, which is again noise. To avoid this drawback we go for Modified decision based un-symmetric trimmed median filter
(MDBUTMF). In this when the above mentioned case occurs, mean of the selected window will be found and replaced
[8][9]. All these algorithms fail at high noise density. The proposed work is the enhancement done on removal of salt and pepper noise at high noise density [10] from digital tmages.
The rest of the paper is structured as folIows. Section
III describes about the proposed algorithm and different cases of proposed algorithm. The detailed explanation of the proposed algorithm with an example is presented in Section
IV. Simulation results with different images are presented in
Section V. Finally in Section VI conclusions are given.
111. PROPOSED ALGORITHM
In a trimmed filter a 3 x3 window is selected and the corrupted pixels are rejected. Alpha Trimmed Mean Filtering (ATMF) is a symmetrical filter where the trimming is done symmetrically at both ends. Trimming of uncorrupted pixels takes place in this method and so loss of image detail and image blurring will occur.
An
Unsymmetric Trimmed Median Filter (UTMF) is proposed to overcome the above mentioned drawback. In

2 this UTMF, a 3x 3 window is selected and the elements are arranged in either ascending or descending order. From this the noisy pixel elements Os and 255s are eliminated, which are responsible for saIt and pepper noise and the median value of the remaining pixels were taken. The corrupted pixel is replaced by this median value. Since the pixel values Os and
255s are eliminated from the selected window it is called as trimmed median filter. This method is better than ATMF, because it identifies the noise and is removed [9].
The proposed algorithm processes the corrupted images by first detecting the impulse noise. The pixel value is processed to check whether it is corrupted or not. If the gray level value lies in-between 0 and 255 it is not corrupted and is left unchanged. Else it is a corrupted pixel and is processed by the proposed filter. The steps of the algorithm are as folIows.
ALGORITHM
Step 1: Select 2-D window of size 3 x 3. Let P (i,j) be the processing pixel .
Step 2: Check whether processing pixel P (i,j) is corrupted or not.
Step 3: If P (i,j) is an uncorrupted pixel then its value is left unchanged. This is illustrated in Case iii) of Section IV.
Step 4: If P (i,j) is a corrupted pixel then two cases are possible as given in Case i) and ii).
Case i): If the selected window contains all the elements as O's and 255's. Then replace with the mean of the preprocessed neighborhood pixels by means of a midpoint filter.
Case ii): If the selected window contains not all elements as
O's and 255's. Then eliminate 255's and O's and find the median value of the remaining elements. Replace with the median value.
Step 5: Repeat steps 1 to 4 until all the pixels in the entire
Replace tbe processing pixel witb mea.D oe the preprocessed elements in tbe selected wiodow
Denoi
� ed image
Fig 1: Proposed algorithm
Where "255" is processing pixel, i.e., P (i,j). Since all the elements surrounding are O's and 255's. If one takes the median value it will be either 0 or 255 which is again noisy.
To solve this problem, the mean of the previously processed neighborhood pixels from the selected window is found and the processing pixel is replaced by the mean value. And for finding this mean value we go for a midpoint filter. Let P is the processing matrix and P' is the processed matrix. Consider a 3x3 window in the processing matrix as shown below. image are processed.
The pictorial representation of each case of the proposed algorithm is shown in Fig.l. The detailed description of each case of the flow chart shown in Fig.l is illustrated through an example in Section IV.
IV. ILLUSTRATION OF PROPOSED ALGORITHM
The proposed algorithm consists of two phases. First phases detects whether the processing pixel is corrupted or not. In the second phase the corrupted pixels are reconstructed using the proposed algorithm. Each and every pixel of the image is checked for the presence of salt and pepper noise. Different cases are illustrated in this Section. If the processing pixel is
P ( i-l,j-l )
P ( i,j-l )
P ( i-l,j )
<
P ( i,j )
>
P ( i+l,j-l ) P ( i+l,j )
P ( i-l,j+l )
P ( i,j+1 )
P ( i+l,j+l )
Fig 2: 3x3 wmdow m the processmg matrix
When processed till P (i,j) the processed matrix will be as shown below.
P'
P'
P'(
noisy and all other pixel values are either O's or 255's is illustrated in Case i). If the processing pixel is noisy pixel that is 0 or 255 is illustrated in Case ii). If the processing pixel is not noisy pixel and its value lies between 0 and 255 is illustrated in Case iii).
If the selected window contains salt and pepper noise as processing pixel (i.e., 255/0 pixel value) and neighboring pixel values contains all pixels that adds salt and pepper noise to the image: An example is illustrated
255
P'
<
P ( i,j )
>
P ( i,j+1 )
P ( i+l,j-l ) P ( i+l,j ) P ( i+l,j+l )
Fig 3: 3x3 wmdow m the processmg matrix
In cases where the processing pixel is corrupted and all the surrounding pixels are noisy, we replace that processing pixel by finding the mean of the previously processed neighborhood pixels. Here we find the mean of [ P'
( i-l,j-l ) , P' ( i-l,j ), P
'
( i-l,j+l ), P' ( i,j-l ) ], previously processed neighborhood elements, with the help of a midpoint filter and will replace the processing pixel by that value.
<255> o �
2 5
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