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LITERATURE REVIEW ON EFFICIENT DETECTION AND FILTERING OF HIGH DENSITY IMPULSE NOISE - A NOVEL ADAPTIVE WEIGHT ALGORITHM 1
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3 N.Pushpalatha B. Neelima Associate Professor Assistant professor Department of ECE, AITS Department of ECE, AITS Annamacharya Institute of Technology and Sciences,Tirupati,India-517520
Ch.v.Nagendra Babu M.Tech(DECS), Student
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1 nagendra.cherukupalli@gmail.com pushpalatha_nainaru@rediffmail.com 3 neeli405@gmail.com
Abstract – This paper presents a literature review on image filters, which are used to remove salt and pepper noise. Image filtering, is a fundamental and crucial for vision perception of human bare eye, can remove noise from the noisy image. There are various image filtering techniques to de-noise the noisy image. Each filtering techniques has its own features to filter an image. The overall goal of this paper is to explore the benefits and limits of existing techniques. A few among the existing common salt and pepper noise filtering algorithms includes: Traditional Median (TM) filter algorithm; Switching Median (SM) filter algorithm, Decision based median algorithm etc. It is found that Adaptive weight algorithm has some advantages over existing techniques when to reduce salt and pepper noise.
Key Terms: Impulse noise, Salt and pepper noise, Traditional Median filter(TMF), Switching Median filter(SMF), Decision based median (DBM)algorithm.
I. INTRODUCTION Images captured by cameras may produce noise due to malfunction of camera pixels. And these captured images often were polluted by various noises during the course in which they are generated or transmitted. There are different types of noises, such as White noise, salt and pepper noise, which affect the vision of an image. Among all the types of noises salt and pepper noise is the most frequent one. Salt and pepper noise is a type of noise normally seen on images. Salt and "impulsive" noise is sometimes called salt-and-pepper noise or spike noise. It represents itself as randomly occurring white and black pixels. An image containing saltand-pepper noise will have dark pixels in bright regions and bright pixels in dark regions. This type of noise can be getting by analog-to-digital converter errors, bit errors in transmission, etc. Here, the noise is get by errors in the data transmission. The corrupted pixels will be set to the either maximum
value (Which looks like snow in the image) or have single bits flipped over. In some cases, only single pixels are set alternatively to zero or to the maximum value, giving the image a `salt and pepper' like appearance. De-noised pixels always remain unchanged. The noise is generally quantified by the percentage of pixels which are corrupted. Image noise is a random variation of brightness or color information in images, and is generally an aspect of electronic noise. Digital Image processing is an Electronic Domain in which image is divided into small unit called pixel and subsequently various operation has been carried out on the pixels. Noise can be usually originated in the sensor or transmission channel during the acquisition and transfer procedure for the digital signal images. In the Digital Image Processing field, removing the noise from the image is the critical issue. In past years, linear filters became the most popular filters in image signal processing. The reason of their popularity is caused by the existence of robust mathematical models which can be used for their design and analysis. However, there exist many areas in which the nonlinear filters provide significantly better results. The benefit of nonlinear filters lies in their ability to preserve edges and suppress the noise without details loss. The success of nonlinear filters is due to the fact that image signals as well as existing noise types are usually nonlinear. As salt and pepper noise is a random shot noise, it is very hard to remove this type of noise using linear filters. Median filters are non-linear. Median based filters have attracted very much attention due to their simplicity and information preservation capabilities [1][2], during last one decade. A few of the median image filters to denoise salt and pepper noise includes: Traditional Median (TM) filter algorithm, Switching Median (SM) filter algorithm, Decision based filtering
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algorithm etc. Traditional Median filter, Switching median filter (SM) algorithms are good at the lower noise density due to less numbers of the noisy pixels which are replaced with the median values which are replaced with the median values[3][4]. II. RELATED WORK 2.1 EXISTING IMAGE FILTERING TECHNIQUES A. TRADITIONAL MEDIAN FILTER Median filtering is a non-linear filtering technique that is well known for the ability to remove impulsive type noise, while preserving sharp edges. The median filter is an order statistics filter.
details. Some pixel details will be removed with the mean filter algorithm. But with the median filter, we do not replace the pixel value with the mean of neighboring pixel values, instead it replaces with the median of those values. The median is calculated by first sorting all the pixel values in ascending order from the surrounding neighborhood into numerical order and then replacing the pixel being considered with the middle pixel value. In median filter, the pixel value of a point p is replaced by the median of pixel value of 8-neighbourhood of a point ‘p’. Fig illustrates an example calculation. .
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Fig 2: Pixels of an image Neighborhood values: 115,117,120,121,124,125,126,127,150. Median values: 124. The median filter gives best result when the impulse noise percentage is less than 0.1%. When the quantity of impulse noise is increased the median filter not gives best result.
Fig.3. Experimental results of the Traditional median filter.
Fig.1. Flow chart of traditional median filter
The procedural steps for traditional median filtering: step1: Consider the Image pixel matrix of [m x n] size
Mean filter is also used to remove the impulse noise. Mean filter replaces pixel with the mean of the pixels values but it does not preserve image
step2: Now pre-allocate another matrix of size [m+2 x n+2]. i.e., Pad the matrix with zeros on all sides of the original image pixel matrix.
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Step3: Consider a window of size 3 x 3. The window of can be any size. Step4: The value to be changed is the middle value of the considered window matrix. Step5: Sort the window matrix. After sorting the output value is found using median values of the neighborhood pixels. The mean value is the average of the all values, we can calculate it by adding all the values together and then dividing that number by the number of values you have. But the median is the middle number of all values. We can calculate it by placing all values in ascending order, and that final middle value is the median value. The calculation of median value is explained in fig.2. The traditional median filtering is best known for its simplicity, but it has its own limitations that it is not suitable in case of noise higher than 25%. The main drawback of the median filter is that it also modifies non noisy pixels thus removing some fine details of the image. Therefore it is only suitable for very low level noise density[5]. At high noise density it shows the blurring.
ݔௗ = ܦܧܯ൛ݔି,ି , … … … , ݔ, … … . . , ݔା,ା ൟ Ti is a threshold and yi,j is the filtered pixel locating at position (i,j) . ߂x ≥ Ti means that the current pixel is much more different from its neighbors and can be treated as a noise. ߂x < Ti denotes the current pixel to be regarded as a noisefree pixel. In fact, the impulse noise value is distributed uniformly, once its value is rather close to its neighbors such that ߂x < Ti happens, the noise pixel cannot be detected by SWM. Hence, this noise pixel cannot be filtered unless the threshold is lowered down. The lower the threshold is used, the more are noise pixels detected, but less detail pixels are preserved. In other words, there is a trade-off between noise detection and detail preservation on tuning the threshold[7].
B. SWITCHING MEDIAN FILTER Switching median filters[6] are well known image filtering algorithm. Detecting noisy pixels and processing only noisy pixels is the main principle in switching-based median filters. There are 3 stages in switching-based median filtering, namely, detection of noise, noise-free pixels estimation and replacement. The principle of detecting noisy pixels and processing only noisy pixels has been effective in reducing processing time as well as image degradation. The mathematical operation of switching median filter is explained below. ା,ା ൟrepreseLet൛ݔି,ି , … … … , ݔ, … … . . , ݔ nts the input sample in the (2L+1) x (2L+1) sliding window where xi,j is the current pixel locating at position (i,j) in the image. The output of SWM is defined as
ݔௗ ݕ, = ൜ ݔ , where ∆ = ݔหݔ, − ݔௗ ห
∆ܶ ≥ ݔ ∆ܶ < ݔ Fig.4. Flow chart of Switching Median Filter
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The limitation of switching median filter is that defining a robust decision measure is difficult because the decision is usually based on a predefined threshold value[8][9]. In addition the noisy pixels are replaced by some median value in their vicinity without taking into account of local features such as presence of edges. So that, edges and fine details are not recovered satisfactorily, particularly when the noise level is very high. In order to overcome these drawbacks a two-phase algorithm has proposed. In the first phase an adaptive median filter is used to classify noise affected and unaffected pixels. In the second phase, specialized regularization method is applied to the noisy pixels to preserve the edges besides noise suppression. The main limitation of the switching median filter is due to this some details and edges are also removed particularly in case of high noise density. Noise density higher than 50%, switching median filter is not suitable. C. DECISION BASED ALGORITHM To overcome the drawbacks of the switching median filter the decision based algorithm is proposed[10]. The decision based algorithm first detects the impulse noise in the image. The noise affected and unaffected pixels in the image are detected by checking the pixel element value against the maximum and minimum values in the window selected. The maximum and minimum values that the impulse noise takes will be in the dynamic range (0, 255). If the pixel being currently processed has a value within the minimum and maximum values in the window of processing, then it is an uncorrupted pixel and no modification is made to that pixel. If the value doesn’t lie within the range, then it is a corrupted pixel and will be replaced by either the median pixel value or by the mean of the neighborhood processed pixels (if the median itself is noisy), which will ensure a smooth transition among the pixels. In the case of high noise density, the median value itself can be noisy. It is in this case, the pixel value is replaced by the mean of the neighborhood processed pixels.
In the 3×3 window above, P1, P2, P3 and P4 indicates already processed pixel values, C indicates
the current pixel being processed, and Q1, Q2, Q3 and Q4 indicates the pixels yet to be processed. If the median value of the above window itself is noisy, then, the current pixel value C will be replaced by the mean of the neighborhood processed pixels, that is, the mean of P1, P2, P3 and P4. And the values of the pixels Q1, Q2, Q3 and Q4 will not be taken into account since they represent unprocessed pixels. The steps of the algorithm are elucidated as follows: Step 1: Select a two dimensional window W of size 3×3. Assume that the pixel being processed isܥ௫,௬ . Step 2: Compute - ܹ , ܹௗ and ܹ௫ - the minimum, median and maximum of the pixel values in the window W respectively. Step 3: Case (i) If ܹ < ܥ௫,௬ < ܹ௫ , then ܥ௫,௬ is an uncorrupted pixel and its value is left unchanged. Otherwise ܥ௫,௬ is a noisy pixel. Case (ii) If ܥ௫,௬ is a noisy pixel, it will be replaced by ܹௗ , the median value, only if ܹ < ܹௗ <ܹ௫ . Case (iii) If ܹ < ܹௗ < ܹ௫ is not satisfied, Wmed itself is a noisy pixel value. In this case, ܥ௫,௬ will be replaced by the mean of the neighborhood processed pixels. Step 4: Repeat Steps 1 to 3 until all the image pixels are processed. In the decision based algorithm, the nature of the pixel being processed, that it is corrupted or not, will be checked. Then the value of the pixel being processed is replaced with the corresponding value as in the Cases (i), (ii), and (iii) of Step 3. The window is then subsequently moved to form a new set of values, on the next pixel to be processed at the window centre. This process is repeated till the last image pixel is processed. The limitation of the Decision based filter is that it is based on the predefined threshold. In this algorithm, image is de-noised by using a 3X3 window. The image is de-noised for pixel value ‘0’ or ‘255’ else it is left unaltered. At very high noise density the median value will be ‘0’ or ‘255’ which is noisy. So in such case, neighboring pixel is used
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Processing, vol. 4, no. 4, 1995, pp. 499– 502.
for replacement. Such repeated replacement of neighboring pixel produces streaking effect. III.CONCLUSION In this paper we have discussed different existing image filtering methods (Traditional median filtering algorithm, Switching filtering algorithm and Decision based algorithm) used to remove salt and pepper noise. A new adaptive weight algorithm is proposed which gives better performance in terms of PSNR[11]. The proposed method consists of two major blocks, detection and filtering. The detection block uses neighborhood pixels correlations to divide the pixels into signal pixels and noise pixels. Only noise pixels are processed and signal pixels are kept the same. For filtering block, different approaches were considered according to the noise density. In the low noise density case, neighborhood signal pixels mean method is adopted. While in the high noise density case, a new adaptive weight algorithm is used. The performance of the proposed algorithm has been tested at low noise, medium and high noise densities on grayscale images. Even at high noise density levels the algorithm gives better results in comparison with other existing image filtering techniques. Both visual and quantitative results are demonstrated. Adaptive weight algorithm, the proposed algorithm, is effective for salt and pepper noise removal in images at high noise densities.
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