Study of Linear Filtering Techniques for Shoeprint Recognition System

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1 Study of Linear Filtering Techniques for Shoeprint Recognition System J. V. Mashalkar Department of Computer Science and IT, Rajarshi Shahu Mahavidyalaya(Autonomous), Latur(MS),INDIA Id: Abstract-The term Digital Image Processing denotes the processing of digital images with the use of digital computer. Digital image processing is used in various types of application areas. Digital images contains various types of noises which reduces the quality of images.the noises are Salt and Pepper noise, Gaussian noise, Poisson noise and Speckle noise. Noises can be removed by various noise removal or filtering techniques. Filtering is the process of modifying the values of the pixels using a function that is typically applied on a local neighborhood of the image. In this paper, an experimental study on impulse noise removal techniques is presented. This paper describes the study of two types of noise such as Salt and Pepper and Guassian Noise in Shoeprint Recognition System. At present there are a variety of methods to remove noise from digital images, such as linear filtering and non-linear filtering. These methods include Mean Filter, Median Filter, Wiener Filter, Minimum Filter, and Maximum Filter. The certain image denoising filters are based on the median filters.this comparative study is conducted with the help of Peak Signal to Noise Ratio(PSNR) and it also include various advantages and disadvantages of particular filtering technique. In this paper the results shows that mean filter is suitable for Shoeprint Recognition System both for Salt and Pepper and Guassian noise. Index Terms- denoising, Gaussian noise, Image Filtering, Median Filters, Salt & Pepper noise and Speckle noise, noise. I. INTRODUCTION The importance of image sequence processing is constantly growing with the ever increasing use of digital television and commercial, medical and communicational e applications. Digital Image Processing has many advantages over analog image processing: it allows a much wider range of algorithms to be applied to the input data and can avoid problems such as build-up of noise and signal distortion during processing, so noise filtering or noise removal is an important task in image processing. Digital images get noised when acquired by a defective sensor or when transmitted through a faulty channel. The noise removal image processing is an important pre-processing step which involves the removal of noise from digital images so that restored images can be applied to subsequent phases of segmentation. Noise disturbances may also be caused by electronic imaging sensors, film granularity, and channel noise. High levels of noise are always undesirable; hence noise removal has to be employed before the image could be used for further analysis.salt and pepper noise is an impulse type of noise, which is also referred to as intensity spikes. This is caused generally due to dead pixels, analog-to-digital converter errors, errors in data transmission, malfunctioning of pixel elements in the camera sensors, faulty memory locations, or timing errors in the digitization process. It has only two possible values, a and b. The probability of each is typically less than 1. The corrupted pixels are set alternatively to the minimum restore the images at considerably higher noise densities. These filters can be either linear or nonlinear. II. IMAGE NOISE Image noise represents unwanted information that deteriorates image quality and can occur during the image capture, transmission, processing or acquisition, and may be dependent or independent of the imag content. Noise deteriorates image quality. A Types of noise:- Image noise can be classified as Impulse noise (Saltand-pepper noise), Amplifier noise (Gaussian noise), Shot noise, Quantization noise (uniform noise), Film grain, onisotropic noise, Multiplicative noise (Speckle noise) and Periodic noise. A.1. Salt and pepper or impulse noise) The salt-and-pepper noise are also called shot noise, impulse noise or spike noise that is usually caused by faulty memory locations, malfunctioning pixel elements in the camera sensors, or there can be timing errors in the process of digitization.in the salt and pepper noise there are only two possible values exists that is a and b and the probability of each is less than 0.2.If the numbers greater than this numbers the noise will swamp out image. For 8- bit image the typical value for 255 for salt-noise and pepper noise is 0 Reasons for Salt and Pepper Noise: a. By memory cell failure. Or dead pixels b. By malfunctioning of camera s sensor cells. Analog to digital converter errors and bit errors in transmission c. By synchronization errors in image digitizing or transmission. A.2.Guassian Noise (Amplifier Noise) Guassian noise is a set of values which are Gaussian distribution which are added to each pixel value. 366

2 Impulsive noise values with random ones. Gaussian noise removal algorithms ideally should smooth the distinct parts of the image. A.3. Poisson Noise (Photon Noise) Poisson or shot photon noise is the noise that can cause, when number of photons sensed by the sensor is not sufficient to provide detectable statistical information [4]. This noise has root mean square value proportional to square root intensity of the image. Different pixels are suffered by independent noise values. At practical grounds the photon noise and other sensor based noise corrupt the signal at different proportions. A.4. Speckle Noise This noise can be modeled by random value multiplications with pixel values of the image and can be expressed as J = I + n*i Where, J is the speckle noise distribution image, I is the input image and n is the uniform noise image by mean o and variance v. This noise deteriorates the quality of active radar and Synthetic aperture radar (SAR) images. This noise is originated because of coherent processing of back scattered signals from multiple distributed points. III. IMAGE DENOISING TECHNIQUES or FILTERING TECHNIQUES A. Filters:- Various techniques are employed for the removal of four types of noise based on the properties and their respective noise models. Image filtering is not used to improve image quality but also is used as a preprocessing stage in many applications. Noise reduction is a two step process: 1. Noise detection 2. Noise replacement In first step location of noise is identified and in second step detected noisy pixels are replaced by estimated value. Efficiency of noise reduction algorithm depends on both Noise detection and Noise replacement. IV. RESEARCH WORK:- This paper describes only two types of noise that is salt and pepper and Gaussian noise. This paper presents Linear filtering methods-mean filtering and wiener filtering. B. Filtering Techniques These filters can be either linear or nonlinear. Filtering Techniques Linear Filters Non-Linear Filters Figure (1) Filtering techniques Linear Filters: Linear filters are used to remove certain type of noise. In linear filter each pixel value in the output image is a weighted sum of the pixel in the neighborhood of the corresponding pixel in the input image. These filters also tend to blur the sharp edges, destroy the lines and other fine details of image, and perform badly in the presence of signal dependent noise. Ex :- mean or average filter, Weiner A mean filter is the optimal linear filter for Gaussian noise in the sense of mean square error. The wiener filtering method requires the information about the spectra of the noise and the original signal and it works well only if the underlying signal is smooth. Wiener method implements spatial smoothing and its model complexity control correspond to choosing the window size. Non-Linear Filters: Nonlinear filtering operation is based conditionally on the values of the pixels in the neighborhood, and they do not explicitly use coefficients in the sum-of-products manner. Noise reduction can be achieved effectively with a nonlinear filter. In recent years, a variety of non-linear median type filters have been developed to overcome the drawbacks of linear filter. Ex: - median filter. Salt And Pepper Noise Removal:- Noise Detection:- If the intensity value of pixel is less than or equal to zero then there is pepper noise and if the intensity value of pixel is greater than or equal to 255 then there is a salt noise. Figure (2) (a) Original image (b) Histogram of original image (c) Image corrupted by salt and pepper noise of salt and pepper image (d) Histogram A. Gaussian Noise Removal: Noise detection:- We compare and take absolute difference of each pixel from original image and corrupted image. If there is any difference then that pixel is noisy pixel and being process for the noise removal. 367

3 Figure (3) (a) Original image (b) Histogram of original image (c) Adding Gaussian noise (d) Histogram of Gaussian noise image V. DIFFERENT TYPES OF LINEAR AND NONLINEAR FILTERS A. Types Of Linear Filters 1. Mean Filter/Average Filtering Mean filtering is simple, intuitive and most commonly used method for reducing noise in an image. It is a sliding-window filter in which, we replace the desired pixel intensity with the arithmetic mean of its surrounding pixel s intensity value. It replaces with the average mean of all the pixel values in the kernel or window. The window is usually square but it can be of any shape. Steps: i. Take the arithmetic mean of surrounding (of noisy pixel) pixel values. ii. The noisy pixel value replays by the resulted arithmetic mean of its surrounding pixels Figure (4) Mean Filtering for Salt and Pepper noise (a) Original image (b)original Image corrupted by salt and pepper noise (c) after noise removal (d) histogram shows the effect of applying a 3 3 mean filter The image figure(c) shows the effect of smoothing the noisy image with a 3 3 mean filter. Note that the noise is less apparent, but the image has been `softened'. If we increase the size of the mean filter to 5 5, we obtain an image with less noise and less high frequency detail. The two main problems with mean filtering, which are: i) A single pixel with a very unrepresentative value can significantly affect the mean value of all the pixels in its neighborhood. ii) When the filter neighborhood straddles an edge, the filter will interpolate new values for pixels on the edge and so will blur that edge. This may be a problem if sharp edges are required in the output. Both of these problems are tackled by the median filter, which is often a better filter for reducing noise than the mean filter, but it takes longer to compute Disadvantage: It does not preserve details of image. Some details a of image re removes with using the mean filter. Figure (5) Mean Filtering for Gaussian noise (a) Original image (b)original Image corrupted by salt and pepper noise (c) shows the effect of applying a 3 3 mean filter (d) histogram shows the effect of applying a 3 3 mean filter 2. Weiner Filter The purpose of the Wiener filter is to filter out the noise that has corrupted a signal. This filter is based on a statistical approach. Mostly all the filters are designed for a desired frequency response. The goal of wiener filter is reduced the mean square error as much as possible. This filter is capable of reducing the noise and degrading function. One method that we assume we have knowledge of the spectral property of the noise and original signal. 368

4 Figure (6) Weiner Filtering (a) Original image ( b) Original Image corrupted by salt and pepper noise (c) Shows the effect of applying Weiner filter (d) histogram after Weiner filtering Figure(7) Weiner filter for Guassian noise (a) Original image (b) Original Image corrupted by salt and pepper noise (c) shows the effect of applying Weiner mean filter (d) histogram after Weiner filtering VI MSE (Mean Square Error) AND PSNR (Peak Signal-To-Noise Ratio) CALCULATION The PSNR block computes the peak signal-to-noise ratio, in decibels, between two images. This ratio is often used as a quality measurement between the original and compressed image. The higher the PSNR, the better the quality of the compressed or reconstructed. The higher the PSNR, the better the quality of the compressed or reconstructed image. Mean Square Error (MSE) represents the cumulative squared error between the compressed and the original image, where as PSNR represents a measure of the peak error. The lower the value of MSE, lower the error. PSNR is most easily defined via the mean squared error.. 1 m-1 n-1 MSE= [I(I,j)-K] m n i=0 j=0 Mean Square Error PSNR=10.log 10(MAX 2 I/MSE) PSNR Here, MAX I is the maximum possible pixel value of the image. When the pixels are represented using 8 bits per sample, this is 255. More generally, when samples are represented using linear PCM with B bits per sample, MAX I is 2 B 1. For color images with three RGB values per pixel, the definition of PSNR is the same except the MSE is the sum over all squared value differences divided by image size and by three. VII. EXPERIMENTAL RESULTS Salt and Pepper Noise Guassian Noise MSE PSNR MSE PSNR Table(1) : - Comparison between Original image and noised image Filter Type Noise Type Salt and Pepper Noise Guassian Noise MSE PSNR MSE PSNR Mean or Average Median Table(2): - Comparison between Original image and Denoised image 369

5 VIII. FUTURE WORK There are a couple of areas which we would like to improve on. In this paper we have analyzed only linear filters for denoising images. The future work of research is to study on non linear filters like Median, comparison between linear and non linear filters and compare Speckle and Poisson noises using above mentioned filtering techniques. Publications, Volume 3, Issue 1, January ISSN Digital Image Processing Rafael C. Gonzalez & Richard E. Woods IX. CONCLUSION Enhancement of a noisy image is necessary task in digital image processing. Filters are used best for removing noise from the images. In this paper we describe various types of noise and filter techniques-linear and non-linear. After studying linear and non-linear filter each of have limitations and advantages. This paper highlighted the noise removal algorithms for gray scale images corrupted by Salt and Pepper and Guassian noise only. This work primarily focuses on comparing the efficiency of noise removal algorithms. The comparative study is explained by with the help of Peak Signal to Noise ratio (PSNR). For removing the Salt and Pepper noise as well as Guassian noise we applied various noise linear filtering algorithms such as mean and weiner. The mean filter produces the correct image as compare to weiner filtering algorithms. We used the Shoeprint Image(Figure 1 and 2) in.tif format,adding two noise (Salt & Pepper and Guassian ) in original image with standard deviation(0.025),de-noised all noisy images by all above mentioned filters and conclude from the results that: (a)the performance of the Mean filter after de-noising for all Salt & Pepper noise is better than Weiner filter and Wiener filter. (b)the performance of the Mean Filter after de-noising for Gaussian noise is better than Weiner filter. From MSE and PSNR calculation it is observed that for enhancing noised image, Guassian noise is efficient than Salt and Pepper noise. The denoised images still contained significant amount of noise. It was able to recover much more detail of the original image from the noisy image REFERENCES 1. Digital Image Processing by Rafael C. Gonzalez 2. Fundamentals of Digital Image Processing A. K. Jain 3. Prachi Khanzode,Dr.S.A.Ladhake Impulse Noise Removal Technique Based on Neural Network and Fuzzy Decisions Volume 2, Issue 2, February 2012 ISSN: X International Journal of Advanced Research in Computer Science and Software Engineering 4. Ms. Rohini R. Varade, Prof. M. R. Dhotre, Ms. Archana B. Pahurkar A Survey on Various Median Filtering Techniques for Removal of Impulse Noise from Digital Images ISSN: International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2, Issue 2, February Prabhdeep Singh, Aman arora Analytical Analysis of Image Filtering Techniques ISSN: ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 4, October Garima Goyal, Ajay Kumar Bansal, Manish Singhal Review Paper on Various Filtering Techniques and Future Scope to Apply These on TEM Images International Journal of Scientific and Research 370

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