A Fast and Robust Hybridized Filter For Image De-Noising

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1 International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue, December A Fast and Robust Hybridized Filter For Image De-Noising Ramandeep Kaur Student of M.Tech IT, Guru Kahi University, Talwandi Sabo, India Er.Rachna Rajput Assistant Professor, CSE, Guru Kashi University, Talwandi Sabo, India Abstract - Salt & pepper noise degrades the quality of the image by hiding the details of objects in the image and also causes damages to the colour quality of an image. Noise removal is an important task in image processing. The noise has to be removed to obtain the good quality image after removing the salt and pepper noise. But the existing salt & pepper noise removal filter like median, wiener and order static filters are not capable of reproducing object details in image with higher accuracy. In this Dissertation work, I have developed a new hybridized filter for the removal of salt & pepper noise. This proposed filter will remove the noise with minimum image quality degradation. I propose the development of an advanced salt & pepper noise removal filter using effective statistic and image processing methods to remove the noise along with support vector machine (SVMs) that is effectively do the job by reproducing the deep image details after removing the noise, which enhance the quality of image than the existing filters. The results presented show that this filter slightly outperforms previous salt and pepper filters, both in quality and in edge preservation. To compare results of all existing filters with new hybridized filter, I use comparison parameters like PSNR, and MAE. KEYWORDS Salt and Pepper Noise, Median Filter, Order Static Filter, SVM, PSNR, MAE. I. INTRODUCTION Images taken with both digital cameras and conventional film cameras will pick up noise from a variety of sources. Many further uses of these images require that the noise will be (partially) removed for aesthetic purposes as in artistic work or marketing, or for practical purposes such as computer vision. There are various types of noise which can affect an image such as Salt and Pepper noise, Gaussian noise, Shot noise etc. In salt and pepper noise (sparse light and dark disturbances), pixels in the image are very different in color or intensity from their surrounding pixels; the defining characteristic is that the value of a noisy pixel bears no relation to the color of surrounding pixels []. Generally this type of noise will only affect a small number of image pixels. When viewed, the image contains dark and white dots, hence the term salt and pepper noise. Typical sources include flecks of dust inside the camera and overheated or faulty CCD(charge-coupled device) []. Image processing is the study of any algorithm that takes an image as input and returns an image as output. Image processing is any form of signal processing for which the input is an image, such as a photograph or video frame the output of image processing may be either an image or a set of characteristics or parameters related to the image. The digital image is processed by a computer to achieve the desired result. Image enhancement improves the quality (clarity) of images for human viewing. Removing blurring and noise, increasing contrast, and revealing details are examples of enhancement operations. For example, an image might be taken of an endothelial cell, which might be of low contrast and somewhat blurred. Reducing the noise and blurring and increasing the contrast range could enhance the image. The original image might have areas of very high and very low intensity, which mask details. An adaptive enhancement algorithm reveals these details. Adaptive algorithms adjust their operation based on the image information (pixels) being processed. In this case the mean intensity, contrast, and sharpness (amount of blur removal) could be adjusted based on the pixel-intensity statistics in various areas of the image. An image may be described as a two-dimensional function I=f(x, y) Where x and y are spatial coordinates. Amplitude of f at any pair of coordinates (x, y) is called ISSN: All Rights Reserved IJSETR 33

2 International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue, December intensity I or gray value of the image. When spatial coordinates and amplitude values are all finite, discrete quantities, the image is called digital image. Digital image processing may be classified into various sub branches based on methods whose: Inputs and outputs are images. Inputs may be images where as outputs are attributes extracted from those images. Digital images are form of visual information captured or transmitted using camera or other imaging system. The received image might be corrupted due to the presence of noise. Image noise reduction without structure degradation is perhaps the most important low-level image processing task. Faulty sensors, optic imperfectness, electronics interference, and data transmission errors may introduce noise to digital images. In considering the signal-to-noise ratio over practical communication media, such as microwave or satellite links, there can be degradation in quality due to low received signal power. Based on trichromatic color theory, color pixels are encoded as three scalar values, namely, red, green and blue (RGB color space). Since each individual channel of a color image can be considered as a monochrome image, traditional nonlinear image filtering techniques often involved the application of scalar filters on each channel separately. However, this disrupts the correlation that exists between the color components of natural images. As such the color noise model should be considered as a 3-channel perturbation vector in color space. Image noise is the random variation of brightness or colours information in images produced by the sensor and circuitry of a scanner or digital camera. Image noise can also originate in film grain and in the unavoidable shot noise of an ideal photon detector. Although these unwanted Fluctuations became known as "noise" by analogy with unwanted sound they are inaudible and such as dithering. The types of Noise are following:- Amplifier noise (Gaussian noise) Salt-and-pepper noise Speckle noise etc. Salt and pepper noise also called as an impulse noise. It is also referred to as intensity spikes. Mainly while transmitting data we will get this salt and pepper noise. It has only two possible values, and. The probability of each value is typically less than.. The corrupted pixel values are set alternatively to the maximum or to the minimum value, giving the image a salt and pepper like appearance as salt looks like [3]white(one) and pepper looks as black(zero) for binary ones. Pixels which are not affected by noise remain unchanged. For an 8-bit image, the typical value for pepper noise is (minimum) and for salt noise (maximum). This noise is generally caused in digitization process during timing errors, malfunctioning of pixel elements in the camera sensors, faulty memory locations. SUPPORT VECTOR MACHINE Support Vector Machine (SVM) was first heard in 99, introduced by Boser, Guyon, and Vapnik in COLT-9. Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression []. They belong to a family of generalized linear classifiers. In another terms, Support Vector Machine (SVM) is a classification and regression prediction tool that uses machine learning theory to maximize predictive accuracy while automatically avoiding over-fit to the data. Support Vector machines can be defined as systems which use hypothesis space of a linear functions in a high dimensional feature space, trained with a learning algorithm from optimization theory that implements a learning bias derived from statistical learning theory. Support vector machine was initially popular with the NIPS community and now is an active part of the machine learning research around the world. SVM becomes famous when, using pixel maps as input; it gives accuracy comparable to sophisticated neural networks with elaborated features in a handwriting recognition task [9]. It is also being used for many applications, such as hand writing analysis, face analysis and so forth, especially for pattern classification and regression based applications. The foundations of Support Vector Machines (SVM) have been developed by Vapnik [7] and gained popularity due to many promising features such as better empirical performance. The formulation uses the Structural Risk Minimization (SRM) principle, which has been shown to be superior, to traditional Empirical Risk Minimization (ERM) principle, used by conventional neural networks. SRM minimizes an upper bound on the expected risk, where as ERM minimizes the error on the training data. It is this difference which equips SVM with a greater ability ISSN: All Rights Reserved IJSETR 33

3 International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue, December to generalize, which is the goal in statistical learning. SVMs were developed to solve the classification problem, but recently they have been extended to solve regression problems []. II. Median filter A digital filter is a system that performs mathematical operations on a sampled. There are two types of filters that are used to remove different type of noises from digital images. Linear filters are used to remove certain type of noise. Gaussian or Averaging filters are suitable for this purpose. 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. Non-linear filters have quite different behaviour compare to linear filters. For non-linear filters, the filter output or response of the filter does not obey the principals of scaling and shift invariance. In this project I use Average filter, and Median filter. Average filter is linear filters and a median filter is a non-linear filter. Image de-noising is a vital image processing task i.e. as a process itself as well as a component in other processes. There are many ways to de-noise an image or a set of data and A method exists. The important property of a good image de-noising model is that it should completely remove noise as far as possible as well as preserve edges. Traditionally, there are two types of models i.e. linear model and non-liner model. The benefits of linear noise removing models is the speed and the limitations of the linear Models are the models are not able to preserve edges of the images in a efficient manner i.e the edges, which are recognized as discontinuities in the image, are smeared out. On the other Hand, Non-linear models can handle edges in a much better way than linear models. The median filter follows the moving window principle for filtering. A 3 3, or 7 7 kernel of pixels is scanned over pixel matrix of the complete image. The median of the pixel values within the window is computed, and therefore the center pixel of the window is replaced with the computed median. Median filtering is completed by, initial sorting all the pixel values from the surrounding neighbourhood into numerical order so substitution the pixel being considered with the centre pixel value. Note that the median value must be written to a separate array or buffer in order that the results are not corrupted because the method is performed. The below process illustrates the methodology of median filtering. x mask and the pixel values of image in the neighbourhood of considered noisy pixel are Table: Median Values in the Neighbourhood Of Algorithm for Median Filter Figure: Types of Filter A median filter comes under the class of nonlinear filter. The best known order statistics filter is the median filter that replaces the value of a pixel by the median of their neighbourhood pixels.median Filters are very effective in removing impulse noise at low density levels.. Step. Select a two dimensional window W of size 3*3. Assume hat the pixel being processed is Cx,y.. Step. Compute Wmed the median of the pixel values in window W. 3. Step 3. Replace Cx,y by Wmed.. Step. Repeat steps to 3 until all the pixels in the entire image are processed. ISSN: All Rights Reserved IJSETR 333

4 International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue, December Advantage: a. It is easy to implement. b. Used for de-noising different types of noises. [] Disadvantage: a.median Filter tends to remove image details while reducing noise such as thin lines and corners. b. Median filtering performance is not satisfactory in case of signal dependant noise. To remove these difficulties different variations of median filters have been developed for the better results. III. PARAMETERS ANALYZED In order to determine the performance of various noise removal algorithms the following parameters are analyzed: A.Peak Signal-to-Noise Ratio B.Mean Absolute Error A. Peak Signal-To-Noise Ratio Peak signal-to-noise ratio, often abbreviated PSNR, is an engineering term for the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Because many signals have a very wide dynamic range, PSNR is usually expressed in terms of the logarithmic decibel scale. The PSNR is most commonly used as a measure of quality of reconstruction of lossy compression codecs (e.g., for image compression). The signal in this case is the original data, and the noise is the error introduced by compression. When comparing compression codecs it is used as an approximation to human perception of reconstruction quality, therefore in some cases one reconstruction may appear to be closer to the original than another, even though it has a lower PSNR (a higher PSNR would normally indicate that the reconstruction is of higher quality) [6]. where MAX is the maximum possible pixel value of the image. b. Mean Absolute Error It is a quantity used to measure how close forecasts or predictions are to the eventual outcomes. The mean absolute error is given by As the name suggests, the mean absolute error is an average of the absolute error Where are the prediction and the true value. The mean absolute error is a common measure of forecast error in time series analysis, where the terms "mean absolute deviation" is sometimes used in confusion with the more standard definition of mean absolute deviation IV. PROPOSED MODEL In this research, we will work on the development of a new method for the removal of salt & pepper noise by creating a new hybridized filter using existing and/or new noise removal filters. The proposed filter will remove the noise with no or minimum image quality degradation. Salt & pepper noise degrades the quality of the image by hiding the details of objects in the image and also causes damages to the colour quality of an image. The noise has to be removed to obtain the good quality image after removing the salt and pepper noise. But the existing salt & pepper noise removal filter like median filter are not capable of reproducing object details in image with higher accuracy. We propose the development of an advanced salt & pepper noise removal filter using effective statistic and image processing methods to remove the noise along with support vector machine (SVMs) that will effectively do the job by reproducing the deep image details after removing the noise, which will enhance the quality of image than the existing filters. Algorithm for Hybridized Filter. Img Load Image (). FiltSvm Load SVM Filter 3. ImageRestored FiltSvm(Img). Allocate outputpixels () a. If not lastsegment(feof(blocksize)) b. clrarr ColorArray() c. abridx abnormindex(clrarr) d. nidx neuralize(clrarr,abridx). sortclrarr sort(nidx) 6. outputpixel level(sortclrarr) This proposed method will help pathologists identify the characteristic of pyloric stenos is. For this reason, the paper is separately as follows. First, as mention in introduction, the brief review about medical image is guided and summary about filters technique. Second is methodology of doing the tasks. Third, the three example tests based on these, ISSN: All Rights Reserved IJSETR 33

5 International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue, December techniques are tried. Meanwhile, we also made the comparisons with median filter, wiener filter, and order-static filter. Finally, some conclusions are made and discussed. Hybridized De-Noising filter Flow Chart Image Acquisition Load SVM Image Filter Apply SVM Image Filter Allocate output pixels and return image Figure : Showing Original Image and its planes In figure we are taking an original Colour image. Image name is tomato. Then we show its three RGB planes these planes are red, green and blue.rgb means Red, Green and Blue planes. Extract color array Extract Abnormal Index Neuralize the color array on the basis of abnormal index Sort and level the image matrix and return de-noised : Flow chart for Hybridized De-Noising filter In results we are taking an original coloured image and then convert it into grey scale image that is black and white image. Then we are adding salt and pepper noise into the original image. Figure : Images with salt and pepper noise on different planes Now we are adding salt and pepper noise into an original coloured image. Then we display results on the image RGB (Red, Green, Blue) planes. salt and pepper noise is present in the image in the form of black and white dots. Which corrupt the image and hide the details of an image. ISSN: All Rights Reserved IJSETR 33

6 International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue, December linear high pass filter which cannot discriminate signal from noise. Moreover it perceptually enhances image more in dark areas than in lighter ones. Figure 3: Results of Median filtering In this figure we are showing results of median which is used for noise reduction on different planes of an image. Median filter is a spatial filtering operation, so it uses a -D mask that is applied to each pixel in the input image. It is used to remove defects and noise from pictures. Median filter is much less sensitive than the mean to extreme values (called outliers). Figure : Result of Hybridized Filter This is new filter named as hybridized filter used to de-noising the image and its different planes. Figure 6: Results SVM Figure : Results of Order-static filtering In this figure we are showing results of order static filter which is used for noise reduction on different planes of an image. The classical order-static masking is the method that using nonlinear Filter. In figure we are using SVM (Support Vector Machine) for noise estimation into the image. The order-static masking method operates by adding a fraction of the high pass filtrated version of the input image to the original one. This operator is sensitive to noise due to the presence of the ISSN: All Rights Reserved IJSETR 336

7 International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue, December Filter Name MAE PSNR Median Filter: Order Static Filter hybridized Filter For image Figure 7: Comparisons of all filters In figure 7 Results of the various image de-noising filters have been shown. The images are added up with noise during the acquisition or transmission processes. In order to remove the noise from the images, the image matrix has to be normalized. The normalization of the images is also called as image de-noising. The filters used in this research are order static, median and the Hybridized filter. The results have proved the effectiveness of the Hybridized filter when compared to the other image de-noising filters. VII. COMPARISONS OF ALL FILTERS We are comparing the results of filters with MAE AND PSNR Comparison Parameters. For image : Table : Comparison of Filters for image Filter Name MAE PSNR Median Filter: Order Static Filter hybridized Filter These tables are shows the comparison of all filters with comparison parameters PSNR and MAE values for image. By these values we get better results with Hybridized filter then Median and Order static filter. Hybridized filter is smoother and shows the details of image than the other ones. VIII. CONCLUSION AND FUTURE SCOPE Table : Comparison of Filters for image CONCLUSION Salt & pepper noise degrades the quality of the image by hiding the details of objects in the image and also causes damages to the colour quality of an image. The noise has to be removed to obtain the good quality image after removing the salt and pepper noise. But the existing salt & pepper noise removal filter like median filter, order static are not capable of reproducing object details in image with higher accuracy. In this dissertation, I create a new filter that is hybridized filter using existing for the removal of salt & pepper noise. The proposed filter will remove the noise with no or minimum image quality degradation. We propose the development of an advanced salt & pepper noise removal filter using effective statistic and image processing ISSN: All Rights Reserved IJSETR 337

8 International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue, December methods to remove the noise with hybridized filter which will enhance the quality of image than the existing filters. Quality of image is also be improved and values of PSNR and MAE are also give better results as compare to the values of existing filters. FUTURE SCOPE In the future, various techniques can be considered to incorporate in this scheme to further improve the performance and preserve more edges in both highly and lowly corrupted images. We also can develop a filter that can completely remove high density noise from an image and work on the details of an image. In future any one can improve the performance of de-noising filter and also show improvement in Comparison parameters values like PSNR, MSE, and MAE. To improve de-noising along the edges as the method we used did not perform so well along the edges. In future salt and pepper noise is also removing from audio and video signals. REFERENCES [] Amandeep Kaur, Karamjeet Singh (), Speckle Noise Reduction By Using Wavelets, NCCI -National Conference On Computational Instrumentation Chandigarh, India. [] Abhishek Kesharwani Sumit Agrawal Mukesh Kumar Dhariwal (3), An Improved Decision based Asymmetric Trimmed Median Filter for Removal of High Density Salt and Pepper Noise, International Journal of Computer Applications ( ) Volume 8 No 8. [3] Chandra Sekhar Panda, and Prof. (Dr.) SrikantaPatnaik (), Filtering Corrupted Image and Edge Detection in Restored Grayscale Image Using Derivative Filters, International Journal of Information Processing, Volume (3): Issue (3). [] Chanchal Srivastava, Saurabh Kumar And O.P. Singh (3), performance comparison of various filters and wavelet transform for image de-noising IOSR Journal of computer Engineering (IOSR- JCE), p-issn: Vol.,Issue.. [] Er. Nancy, Er. Sumandeep Kaur (3), Comparative Analysis and Implementation of Image Enhancement Techniques Using MATLAB International Journal of Computer Science and Mobile Computing, Vol., Issue.. [6] Irphan Ali Shaik, Mirza shafi shahsavar, 3K.J.Silva Lorraine, Ajesh kumar vishwanadham (), Impulse Noise Detection and Filtering Based on Adaptive Weighted Median Filter ISSN: 78-7, Vol., Issue 8. [7] J.Harikiran, B.Saichandana, and B.Divakar (), Impulse Noise Removal in Digital Images, International Journal of Communication Systems, Volume No.8, 39-. [8] Mrs. C. Mythili and Dr. V. Kavitha (), Efficient Technique for Color Image Noise Reduction, TRBJ ACM - ISWSA; ISSN: (print); (online). [9] Mr. Salem Saleh Al-amri, Dr. N.V. Kalyankarand Dr.Khamitkar S.D (), A Comparative Study of Removal Noise from Remote Sensing Image IJCSI International Journal of Computer Science Issues, Vol. 7, Issue., No.. [] Om Prakash Verma and Shweta Singh (3), A Fuzzy Impulse Noise Filter Based on Boundary Discriminative Noise Detection, Journal of Information Process System, Vol.9, No.. [] Pawan Patidar and Manoj Gupta (), Image De-noising by Various Filters for Different Noise, International Journal of Computer Applications ( ) Volume 9 No., November. [] P.E.Ng and K.K.Ma(6), Aswitching median filter with boundary discriminative noise detection for exteremely corrupted image, IEEE Trans. Image process, vol.,no.6,pp.6-6. [3] Priyanka Kamboj and Versha Rani (3), A Brief Study Of Various Noise Model And Filtering Techniques, Journal of Global Research in Computer Science, Volume, No.. [] Ravinder Singh Balwinder Singh and Harmandeep Singh (3), Removal of High Density Salt & Pepper Noise in Noisy color Images using Proposed Median Filter International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE) Volume, Issue. [] Sanjeev Kumar, Asutosh Kar and Mahesh Chandra (), SVM Based Adaptive Median Filter Design for Face Detection in Noisy Images, International Conference on Signal Processing and Integrated Networks (SPIN). [6] S.Gopi Krishna, T. Sreenivasulu Reddy andg.k.rajini, Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetric Trimmed Median Filter International Journal of Engineering Research and ISSN: All Rights Reserved IJSETR 338

9 International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue, December Applications (IJERA) Vol., Issue, Jan-Feb. [7] Srinivasan, K.S., Ebenezer, D. (7), A new fast and efficient decision based algorithm for removal of high-density impulse noises, IEEE Signal Process.Lett.(3), Pp [8] Sucharita Padhi Debananda Padhi, and Bratat imohanty (), Order Statistic Filters for removing Salt-and-Pepper Noise of Images: A Comparative Study International Journal of Advanced Research in Computer Science and Software Engineering Volume, Issue. [9] Sukomal Mehta, Sanjeev Dhull (), fuzzy based median filter for grey-scale images, International journal of engineering science & advanced technology, ISSN:-3676,Volume-,Issue-..Author Profile Ramandeep Kaur received the M.SC(IT) Master Degrees in Information Technology degree from T.P.D punjabi university nebhourhood campus rampura-phul in.currently she is pursuing m.tech degree in information technology from Guru Kashi University, Talwandi Sabo, Bathinda (Punjab). Her research interests include digital image processing and Image Enhancement. ISSN: All Rights Reserved IJSETR 339