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ISSN (ONLINE) : 2045 -8711 ISSN (PRINT) : 2045 -869X

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING NOVEMBER 2014 VOL- 4 NO - 11

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.11 NOVEMBER 2014

UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: editor@ijitce.co.uk Phone: +44-773-043-0249 USA: Editor International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA. Phone: 001-706-206-0812 Fax:001-706-542-2626 India: Editor International Journal of Innovative Technology & Creative Engineering Dr. Arthanariee. A. M Finance Tracking Center India 66/2 East mada st, Thiruvanmiyur, Chennai -600041 Mobile: 91-7598208700

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.11 NOVEMBER 2014

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING Vol.4 No.11 November 2014

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.11 NOVEMBER 2014

From Editor's Desk Dear Researcher, Greetings! Research article in this issue discusses about motivational factor analysis. Let us review research around the world this month. Everyday life throws at us an endless number of pattern recognition problems smells, images, voices, faces, situations and so on. Most of these problems we solve at a sensory level or intuitively, without an explicit method or algorithm. he techniques we considered the manipulation of the dynamic range of a given digital image to improve visualization of its contents. In this consider more general image enhancement. We introduce the concept of image filtering based on localized image sub regions (pixel neighbourhoods), outline a range of noise removal filters and explain how filtering can achieve edge detection and edge sharpening effects for image enhancement. The basic goal of image enhancement is to process the image so that we can view and assess the visual information it contains with greater clarity. Image enhancement, therefore, is rather subjective because it depends strongly on the specific information the user is hoping to extract from the image. The primary condition for image enhancement is that the information that you want to extract, emphasize or restore must exist in the image. Fundamentally, ‘you cannot make something out of nothing’ and the desired information must not be totally swamped by noise within the image. Perhaps the most accurate and general statement we can make about the goal of image enhancement is simply that the processed image should be more suitable than the original one for the required task or purpose. This makes the evaluation of image enhancement, by its nature, rather subjective and, hence, it is difficult to quantify its performance apart from its specific domain of application. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue.

Thanks, Editorial Team IJITCE

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Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at ShangaiJiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin,Faculty of Agriculture and Horticulture,Asternplatz 2a, D-12203 Berlin,Germany Dr. Marco L. BianchiniPh.D Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbaraPh.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh,Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University,No. 303, University Road,Puli Town, Nantou County 54561,Taiwan Dr. S.Pannirselvam M.Sc., M.Phil., Ph.D Associate Professor & Head, Department of Computer Science, Erode Arts & Science College (Autonomous),Erode, Tamil Nadu, India. Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources,Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D. Project Manager - Software,Applied Materials,1a park lane,cranford,UK Dr. S.Prasath M.Sc., M.Phil., Ph.D Assistant Professor, Department of Computer Science, Erode Arts & Science College (Autonomous),Erode, Tamil Nadu, India. Dr. Bulent AcmaPh.D Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. SelvanathanArumugamPh.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA.

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Review Board Members Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. MutamedTurkiNayefKhatib Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University (Kadoorie), TulKarm, PALESTINE. Dr.P.UmaMaheswari Prof &Head,Depaartment of CSE/IT, INFO Institute of Engineering,Coimbatore. Dr. T. Christopher, Ph.D., Assistant Professor &Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India. Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India. Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 JavadRobati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran VineshSukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE HOD & Associate Professor, IT Dept, Medi-Caps Inst. of Science & Tech.(MIST),Indore, India

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.11 NOVEMBER 2014 Dr. S. B. Warkad Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India Dr. doc. Ing. RostislavChoteborský, Ph.D. Katedramateriálu a strojírenskétechnologieTechnickáfakulta,Ceskázemedelskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 DR.ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg.,HamptonUniversity,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A.,M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India). Dr. Ing. RostislavChotěborský,ph.d, Katedramateriálu a strojírenskétechnologie, Technickáfakulta,Českázemědělskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21

Dr. AmalaVijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE

Naik Nitin AshokraoB.sc,M.Sc Lecturer in YeshwantMahavidyalayaNanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed. Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-BangloreWesternly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech& PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Jeremiah Neubert Assistant Professor,MechanicalEngineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ. Dr. Xiangfa Wu, Ph.D. Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY SeraphinChallyAbou Professor,Mechanical& Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 OrdeanCourt,Duluth, Minnesota 55812-3042

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Dr. Qiang Cheng, Ph.D. Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive, Carbondale, IL 62901 Dr. Carlos Barrios, PhD Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials CSIRO Process Science & Engineering Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688

Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,S찾o Paulo Business School,RuaItapeva, 474 (8째 andar)01332-000, S찾o Paulo (SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India. Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India. Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India. Mr. Md. Musfique Anwar B.Sc(Engg.) Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh. Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom. Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India.

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Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India PremaSelvarajBsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India. Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),UniversitiSainsMalaysia,School of Mathematical Sciences, Pulau Pinang-11800,[PENANG],MALAYSIA.

Manoj Sharma Associate Professor Deptt. of ECE, PrannathParnami Institute of Management & Technology, Hissar, Haryana, India RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India Dr. S. B. Warkad Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India Dr. Saurabh Pal Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India Manimala Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post, Kottayam Dt. Kerala -686579 Dr. Qazi S. M. Zia-ul-Haque Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan Dr. A. Subramani, M.C.A.,M.Phil.,Ph.D. Professor,Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode - 637215 Dr. SeraphinChallyAbou Professor, Mechanical & Industrial Engineering Depart. MEHS Program, 235 Voss-Kovach Hall, 1305 Ordean Court Duluth, Minnesota 55812-3042 Dr. K. Kousalya Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052 Dr. (Mrs.) R. Uma Rani Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India. MOHAMMAD YAZDANI-ASRAMI Electrical and Computer Engineering Department, Babol"Noshirvani" University of Technology, Iran.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.11 NOVEMBER 2014 Dr. Kulasekharan, N, Ph.D Technical Lead - CFD,GE Appliances and Lighting, GE India,John F Welch Technology Center,Plot # 122, EPIP, Phase 2,Whitefield Road,Bangalore – 560066, India. Dr. Manjeet Bansal Dean (Post Graduate),Department of Civil Engineering,Punjab Technical University,GianiZail Singh Campus,Bathinda -151001 (Punjab),INDIA Dr. Oliver Jukić Vice Dean for education,Virovitica College,MatijeGupca 78,33000 Virovitica, Croatia Dr. Lori A. Wolff, Ph.D., J.D. Professor of Leadership and Counselor Education,The University of Mississippi,Department of Leadership and Counselor Education, 139 Guyton University, MS 38677

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.11 NOVEMBER 2014

Contents Face Image Analysis under Various Noisy Backgrounds Using Gaussian Median Filtering FFFFFFFF S.Prasath & Dr.S.PannirselvamFFFFFFFFFFFFFFFFFFFFFFFFF.FFFFFFFFF.[243]

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.11 NOVEMBER 2014

Face Image Analysis under Various Noisy Backgrounds Using Gaussian Median Filtering [GMF] S.Prasath Ph.D (Research Scholar),Department of Computer Science, Erode Arts & Science College (Autonomous),Erode, Tamil Nadu, India. Email: softprasaths@gmail.com Dr.S.Pannirselvam Research Supervisor & Head Department of Computer Science, Erode Arts & Science College (Autonomous),Erode, Tamil Nadu, India. Email: pannirselvam08@gmail.com Abstract--- Today, image processing penetrates into various fields, but till it is struggling in quality issues. Hence, image enhancement came into existence as an essential task for all kinds of image processings. Various methods are been presented for gray scale image enhancement, especially for face image. In this paper various filters are used for face image enhancement. However, the authenticity of noises that might insert into an image document will affect the performance of face recognition algorithms. Hence, different filtering algorithms are presented for noise elimination using various filtering algorithm. In order to improve of the image quality Gaussian Median filtering has been applied. The experimental result shows that this method provides better enhancement in term of quality when compared with the existing methods such as Mean filter, Wiener Filter and laplacian filter. The peak Signal Noise Ratio (PSNR) and Mean Square Error (MSE) are been used for similarity measures. Keywords— Mean Filter (MF), Median Filter, Wiener Filter, Gaussian Median Filter (GMF),PSNR, MSE.

I . INTRODUCTION In the computer era there is a rapid growth in the field of information technology and the security system was suffering from various issues. Today, criminals have been entered into the field of information technology called cyber crime. Lot of security systems has emerged to solve the various security issues such as password, username, secret codes, but failed due to cyber attacks. In order to overcome such security issues the biometric system has been emerged with various features such as face recognition, fingerprints recognition, gait, palm print, voice, signatures etc. Every human being can identify a faces in a scene with no effort, with an automated system such objectives are the very challenging one due to various factors which affects the quality of the image. Hence, face recognition system has been used to verify the identity of an individual. It can be accomplished by matching process using various methods and features such as geometric, statistical, low-level features which are derived from face images.

Since last decades, researchers are involved on face recognition in image processing and they achieved so many mile stone for this. Because face recognition is the critical stage to identify the face in images due to pose, presence or absence of structural components, facial expression, occlusion, image orientation. Above all, Noise is prime factor of reducing face recognition rate. Several methods have been evolved to increase recognition rate. Filters are widely accepted to remove impulsive and high frequency noise for signal and image processing. The concept of filtering has its roots in the use of the Fourier transform for signal processing in the so-called frequency domain. Spatial filtering term is the filtering operations that are performed directly on the pixels of an image. The process consists simply of moving the filter mask from point to point in an image at each point (x, y) and the response of the filter at that point is calculated using a predefined relationship. II. RELATED WORK In recent years, considerable progress has been made in the area of face recognition with the development of many techniques. Even these techniques perform extremely well under various constrain, the problem of face recognition in uncontrolled by noisy environment remains unsolved. Image noise can originate in film grain or in electronic noise in the input device such as scanner digital camera, sensor and circuitry or in the unavoidable shot noise of an ideal photon detector. Noise affects the identification of images in authentication and also in pattern recognition process. The identification of the nature of the noise [1] is an important part in determining the type of filtering that is needed for rectifying the noisy image. Noise in imaging systems is usually either additive or multiplicative [2]. The basic types of noise can be further classified into various forms [3] such as amplifier noise or Gaussian noise, Impulsive noise or salt and pepper noise, quantization noise, shot noise, film grain noise and nonisotropic noise. A model [4] proposed with noise removal filtering algorithms. Most of them follows certain statistical parameters and know the noise types. Applying various a filtering algorithms that is sensitive to additive noise to an image that has been degraded by a multiplicative noise which does not provide best results. Many algorithms have been developed to remove salt & pepper noise in document images with different

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.11 NOVEMBER 2014 performance in removing noise and retaining fine details of the image. Various filtering techniques exist to perform the inverse of the imperfections in the degraded image [5], [6]. These filtering techniques are application oriented. Some filtering techniques perform better than the others techniques based on the noise category. These filters are used in a variety of applications [7] efficiently in preprocessing module. III. NOISE TYPES The noise is characterized by its pattern and its probabilistic characteristics. There is a wide variety of noise types while this paper focus on the most important types they are Gaussian noise, salt and pepper noise, poison noise, impulse noise, speckle noise. A) Gaussian Noise Gaussian noise is statistical noise that has its probability density function equal to that of the normal distribution, which is also known as the Gaussian distribution. In applications, Gaussian noise is most commonly used as additive white noise to yield additive white Gaussian noise. B) Salt and Pepper Noise Salt and pepper noise is a form of noise typically seen on images. It represents itself as randomly occurring white and black pixels. Salt and pepper noise creep into images in situations where quick transients, such as faulty switching, take place. C) Speckle Noise Speckle is a complex phenomenon, which degrades an image quality. Speckle noise is a multiplicative noise. The speckle noise follows a gamma distribution [8]. Thus, denoising or reducing the noise from a noisy image has become the predominant step in image processing. For the quality and edge preservation of images we have taken different denoising techniques into consideration. IV. EXISTING METHODOLOGY 4.1 Filters Generally filters are used to filter unwanted things or object in a spatial domain or surface. In digital image processing, mostly the images are affected by various noises. The main objectives of the filters are to improve the quality of image by enhancing is to improve interoperability of the information present in the images for human visual. A general structure of a filter mask is as follows. -1

-1

-1

-1

N

-1

-1

-1

-1

Fig.1.1 Filtering Mask Image filtering can be used for many aspects which includes, smoothing, sharpening, noise eliminating and edge detection etc. A filter is defined by a kernel, which represented is a small array and applied to each pixel and its neighbours within an image.

4.2 Frequency and Spatial Filters The frequency domain technique is based on the convolution theorem. It decomposes an image from its spatial domain form of brightness into frequency domain components and is represented as the following equation ݃(‫ݔ‬,‫= )ݕ‬h( ‫ݔ‬,‫ݔ( ݂∗) ݕ‬,‫ ) ݕ‬...... (1)

Where ݂(‫ݔ‬,‫ )ݕ‬is the input image, h(‫ݔ‬,‫ )ݕ‬is a position invariant operator and ݃(‫ݔ‬,‫ )ݕ‬is the resultant image from the convolution theorem. ‫ݑ(ܩ‬,‫ݑ( ܪ= )ݒ‬,‫ݑ(ܨ )ݒ‬,‫)ݒ‬

........ (2)

Where G, H, F is the fourier transform of ݃, h, ݂ respectively. The transform H (u, v) is called transfer function of the process. Here the edge in f(x,y) can be boosted by using H(u,v) to emphasis the high frequency component of F(u,v). In case of spatial filter works on pixels in the neighbourhood of the pixel. The operation on sub image pixels is defined using mask or filter with the same dimension. 4.3 Mean Filter (MF) The Mean Filter is a linear filter which uses a mask over each pixel in the signal. Each of the components of the pixels which fall under the mask are averaged together to form a single pixel. This filter is also called as average filter. The Mean Filter is poor in edge preserving. The Mean filter is defined by:

Where M is the total number of pixels in the neighborhood N. Mean filtering is a simple, intuitive and easy to implement method of smoothing images, i.e. reducing the amount of intensity variation between one pixel and the next. It is often used to reduce noise in images. The idea of mean filtering is simply to replace each pixel value in an image with the mean value of its neighbours, including itself. This has the effect of eliminating pixel values which are unrepresentative of their surroundings. Mean filtering is usually thought of as a convolution filter. Like other convolutions it is based around a kernel, which represents the shape and size of the neighbourhood to be sampled when calculating the mean. 4.4 Wiener Filter 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 [9].

..... (4) Where H(u, v) = Degradation function H*(u, v) = Complex conjugate of degradation function Pn (u, v) = Power Spectral Density of Noise Ps (u, v) = Power Spectral Density of un-degraded image

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.11 NOVEMBER 2014 Wiener filtering is able to achieve significant noise removal when the variance of noise is low they cause blurring and smoothening of the sharp edges of the image. Detection of emotions in highly corrupted noisy environment this approach involves removal of noise from the image by the Wiener Filter for an automatic system for the recognition of facial expressions is based on a representation of the expression [10]. 4.5 Laplacian Filter Laplacian is a 2-D isotropic measure of the second spatial derivative of an image. The Laplacian of an image highlights regions of rapid intensity change and is therefore often used for edge detection. The laplacian is often applied to an image that has first been smoothed with something approximating a Gaussian smoothing filter in order to reduce its sensitivity to noise. The operator normally takes a single gray level image as input and produces another gray level image as output.

median value. The median filter does not require convolution. The best known order-statistics filter is the median filter, which replaces the value of a pixel by the median of the gray levels in the neighborhood of that pixel, The original value of the pixel is included in the computation of the median. Median filters are quite popular because, for certain types of random noise they provide excellent noise reduction capabilities, with considerably less blurring than linear smoothing filters of similar size. Input Image

Gaussian Filter

Add Noise

V. PROPOSED METHODOLOGY By considering the inefficiency of the existing image enhancement methods there is a need to propose a new methodology for face image enhancement which leads to improve the quality of the image. A novel filtering technique is proposed with the hybridization of Gaussian filter with Median filter. 5.1 Gaussian filtering Gaussian filters are a class of linear smoothing filter with the weights chosen according to the Gaussian functions. Mainly these kind filters are used to smooth the image and to eliminate the Gaussian noises. 2 2 1 1 n 2 .... (5) h(m ,n)= e m2 X 2πσ e2 σ 2σ 2πσ From the above equation 5 shows the Gaussian filter is separable. The Gaussian smoothing filter is very good in noise removal in normal distribution function. This filter is rotationally symmetric the amount of smoothening is all direction. The degree of smoothening is governed by variance T. 5.2 Median Filtering Then Median filter, the most prominently used impulse noise removing filter, provides better removal of impulse noise from corrupted images by replacing the individual pixels of the image as the name suggests by the median value of the gray level The median of a set of values is such that half of its values in the set are below the median value and half of them are above it and so is the most acceptable value than any other image statistics value for replacing the impulse corrupted pixel of a noisy image for if there is an impulse in the set chosen to determine the median it will strictly lie at the ends of the set and the chance of identifying an impulse as a median to replace the image pixel is very less. A commonly used non-linear operator is the median, a special type of low-pass filter. The median filter takes an area of an image (3x3, 5x5, 7x7, etc.), sorts out all the pixel values in that area and replaces the center pixel with the

Median Filter

Preprocessed Image

Fig.1.2 Process flow of GMF Model The above figure 1.2 shows the process flow of the proposed preprocessing technique. Initially, the input image is selected from the standard fingerprint database. Then the Gaussian Median filter is applied on the input image for image enhancement of Gaussian filter and Median filter to eliminate the noise presents in the image. In order to enhance the clarity of the image the median filter is applied with the amplification factor A. Finally the preprocessed image has been obtained with better quality. VI. SIMILARITY MEASURES 6.1 Mean Squared Error (MSE) Mean square error is given by

MSE =

2 1 M N g ( i , j ) − f ( i , j ) [ ] ∑ ∑ MN i=1 i=1 .... (7)

Where M and N are the total number of pixels in the horizontal and the vertical dimensions of image, g denotes the Noise image and f denotes the filtered image. 6.2 Peak Signal to Noise Ratio (PSNR) The peak Signal to Noise ratio is calculated by:  2552  P S N R = 1 0 lo g 1 0    M S E  .... (8) For the image quality measures, if the value of the PSNR is very high for an image of a particular noise type then is best quality image.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.11 NOVEMBER 2014 VII. ALGORITHM Input : Input image from IDB Output : Preprocessed Image Step 1: Read an image from the image database (IDB) Step 2: Add Noise to an input image. Step3: Apply Gaussian filter on the input image. Step 4: Then apply Median filter on the input image.

I. values. PAGE SIZE Step 5: Calculate PSNR Step 6: Repeat step 2 and step 4 for all images in database (IDB). Step 7: Stop

VIII. EXPERIMENTATION & RESULTS The performance of the existing filters is measured by conducting the following procedure. The performance of the filters is measured by applying noise on the face images. The sample face images downloaded are used to analysis this work. For our experiments, the sample facial images from the standard ORL face image database are used. It contains a total of 4000 images containing 40 subjects each with 10 images that differ in poses, expressions and lighting conditions. Figure 1.3 shows the sample images used in our experiments used noise. From the figure 1.4 shows noise removed using mean filter, wiener filter and proposed Gaussian Median filter. Original Image

Fig.1.3 Sample and Noisy image Mean Filter

Noisy Image

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Wiener Filter


INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.11 NOVEMBER 2014

Fig.1.4 Output image

Laplacian Filter

Gaussian Median Filter

Image Id

Mean Filter

Wiener Filter

Laplacian Filter

Gaussian Median Filter

1

43.71

8.53

55.14

49.31

2

60.73

12.71

50.48

62.40

3

33.73

9.48

36.60

42.02

4

61.09

14.0

39.03

77.64

5

37.63

8.02

29.87

41.93

6

64.72

13.44

44.35

75.56

7

55.14

13.75

40.26

57.37

8

31.96

7.34

29.81

32.66

9

42.94

10.84

31.37

36.29

10

45.24

9.56

25.89

58.13

Table 1. MSE Comparison Values From the table 1 shows the experimented values obtained from different preprocessing methods. It shows the selected face image from the database. The performance was evaluated using the Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR) in order evaluates the quality of the image. By the analysis of the values in the table the Gaussian Median filter is better with less MSE and high PSNR values. In order to evaluate the performance of the Gaussian Median filter considered the obtained results with the existing Mean filter, Wiener filter and laplacian filter are shown in the following table 2. Image Id

Mean Filter

Wiener Filter

High Boost Filter

Gaussian Median Filter

1

31.75

28.85

30.78

35.31

2

30.33

27.12

29.26

33.06

3

32.88

28.39

27.55

37.37

4

30.30

26.69

28.47

33.75

5

32.40

29.12

30.10

37.40

6

30.05

26.87

29.79

34.09

7

30.74

26.78

28.05

33.79

8

33.11

29.50

27.69

37.50

9

31.83

27.81

28.12

36.05

10

31.60

28.35

29.33

35.58

Table 2. PSNR Comparison Values

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.11 NOVEMBER 2014 From the below figure 4 shows the pictorial representation of the performance evaluated. By analysing the obtained results the proposed model produced the best results. Hence the Gaussian Median filter is an efficient one.

IX. CONCLUSION In this paper, the noise removal of images based on Gaussian Median filtering has been presented. The experimental result proves the effectiveness of this approach, providing good PSNR values when compared to existing methods. The performances of PSNR values of proposed Gaussian Median filtering when compared to existing methods Mean filter, Wiener Filter and Laplacian Filter are investigated independently. The proposed Gaussian Median filtering produces better results with 35.39% accuracy compared with existing methods gives 30.05 % accuracy for Mean filter, Wiener Filter with 27.94% accuracy and laplacian Filter with 29.26% accuracy. Moreover, the computational cost of the algorithm is very low. Therefore, the proposed algorithm candidates itself for implementation is in simple low-cost cameras or in video capture devices with high values of resolution and frame rate. The proposed scheme is capable of achieving at least comparable and often better performance than existing iterative filtering techniques.

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IJITCE Nov2014  

International Journal of Innovative Technology and Creative Engineering (ISSN:2045-8711) November 2014 Issue Vol.4 No.11

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