Salt Pepper Noise

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A NEW EFFICIENT ALGORITHM FOR REMOVING OF HIGH DENSITY SALT AND PEPPER NOISE THROUGH MODIFIED DECISION BASED UNSYMMETRIC TRIMMED MEDIAN FILTER FOR VEDIO RESTORATION

A Project Report Submitted in partial fulfillment of the Requirement for the award of the Degree ofMaster of Technology INCommunication Engineering & Signal ProcessingBySIDDABATHUNI RAMATULASI(Y11MTEC813)Under the guidance ofP.SIVA PRASAD, M.Tech Assistant professor

Department of Electronics & Communication EngineeringR.V.R. & J.C.COLLEGE OF ENGINEERINGAUTONOMOUS(Approved by A.I.C.T.E)(Affiliated to Acharya Nagarjuna University) Chandramoulipuram GUNTUR 522 019, Andhra Pradesh, INDIA[2011-2013]Department of Electronics & Communication Engineering

CERTIFICATE

This is to certify that the project report entitled THE NEW EFFICIENT ALGORITHM FOR REMOVING OF HIGH DENSITY SALT AND PEPPER NOISE THROUGH MODIFIED DECISION BASED UNSYMMETRIC TRIMMED MEDIAN FILTER FOR VIDEO RESTORATION that is being submitted by Siddabathuni Ramatulasi (Y11MTEC813) in partial fulfillment for the award of the Degree of Master of Technology in Communication Engineering & Signal Processing to the Acharya Nagarjuna University is a record of bonafide work carried out by her under my guidance and supervision. The results embodied in this project report have not been submitted to any other University or Institute for the award of any degree or diploma.

Date:

Signature of GuideSignature of HODP.SIVA PRASAD, M.TechDr.V.V.K.D.V.PRASAD, Ph.DAssistant ProfessorProfessor & Head

ACKNOWLEDGEMENTI would like to express my sincere gratitude to my project guide, P.SIVA PRASAD, for his advice, encouragement and his help to solve many practical problems and his patience to answer my many questions. His deep understanding and immense knowledge helped me solve many difficult problems. He guided me throughout this project, even during his extremely busy schedule. I am also grateful to my Head of Department Dr.V.V.K.D.V.PRASAD, for his support and supervision. And also I sincerely thank to Dr.A.SUDHAKAR, Principal for providing all facilities to done the project successfully.This master thesis project has been a very valuable learning experience. It has given me the chance to learn better ways of achieving goals from more experienced personnel. Above all, the most important asset I have taken from this experience is the willingness to learn. The working atmosphere and especially the nice persons of this department have encouraged me in my work. I would like to thank them all for their hospitality. I would like to thank all the lecturers and technical staff in Electronics and Communications Department for their warm hearted support during difficult times. Last but not least to my parents, I extend my deepest love. They have always motivated me to continue my higher studies.

TABLE OF CONTENTSChapter NoDescriptionPage NoAbstractiList of FiguresiiList of SymbolsvList of AbbreviationsviChapter 1INTRODUCTION11.1 Introduction21.2 Literature Survey3Chapter 2DIGITAL IMAGE PROCESSING72.1 Introduction 82.2 Digital image8 2.2.1 Types of digital images102.2.1.1 Black and white images10 2.2.1.2 Color images102.3 Image file sizes112.4 Image file formats112.5 Digital image processing122.6 Advantages of Digital Image Processing132.7 Disadvantages of Digital Image Processing132.8 Fundamental steps in digital image processing142.8.1 Image acquisition14 2.8.2 Image enhancement152.8.3 Image restoration162.9 Color image processing172.9.1 Segmentation172.9.2 Image compression182.10 Project description 19Chapter 3DIGITAL VIDEO213.1 Introduction223.2 History253.3 Advantages of Digital video273.4 Frame separation293.4.1 Number of Frames per Second293.4.2 Interlaced Vs Progressive303.5 Video quality31Chapter 4NOISE324.1 Introduction334.2 Noise334.3 Types of Noise344.3.1 Short Noise354.3.2 Gaussian Noise354.3.3 White Noise364.3.4 Additive White Gaussian Noise364.3.5 Poison Noise374.3.6 Speckle Noise374.3.7 Salt and Pepper Noise384.4 Noise Generation384.4.1 Signal to Noise Ratio394.4.2 Peak Signal to Noise Ratio404.5 Detector Noise414.6 Crimmins Speckle Removal42 Chapter 5IMAGE RESTORATION445.1 Introduction455.2 Restoration455.3 Requirements for Restoration465.4 Degradation465.4.1 Image Degradation475.4.2 Image Degradation for Blur Parameters475.5 Thresholding49Chapter 6REMOVING OF HIGH DENSITY SALT AND PEPPER NOISE USING DIFFERENT FILTERS506.1 Introduction516.2 Salt and Pepper Noise516.3 Disadvantages of Salt and Pepper Noise526.4. Mean Filter526.5 Median Filter536.5.1 Advantages of Median Filter556.5.2 Disadvantage of the Median Filter566.5.3 Comparison between the Median Filter and the Average filter586.6 Removing of Salt and Pepper Noise using Different Filters596.6.1 Standard Median Filter596.6.2 Adaptive Median Filter606.6.3 Tolerance Based Switched Median Filter616.6.4 Decision Based Algorithm626.6.5 Unsymmetric Trimmed Median Filter626.6.6 Decision Based Unsymmetric Trimmed Median Filter636.6.7 Modified Decision based Unsymmetric Trimmed Median Filter 64 Chapter 7APPLICATIONS 667.1 Photoshop 677.2 Satellites687.3 Medical line process69Chapter 8SIMULATION RESULTS70Chapter 9CONCLUSIONS81BIBLIOGRAPHY 83

ABSTRACT

It is important to remove or minimize the degradations, noises in valuable ancient blurred color images. The traditional available filtering methodologies are applicable for fixed widow dimensions only these are not applicable for varying scale images. In this project we propose a new technique for digital image restoration, in this the noise free and noisy pixels are classified based on empirical multiple threshold values. Then the median filtering technique is applied. So that noise free pixels are getting preserved and only noisy pixels get restored. In this project, a novel decision-based filter, called the multiple thresholds switching (MTS) filter, is proposed to restore images corrupted by salt-pepper impulse noise. The filter is based on a detection-estimation strategy. The impulse detection algorithm is used before the filtering process, and therefore only the noise-corrupted pixels are replaced with the estimated central noise-free ordered mean value in the current filter window. The new impulse detector, which uses multiple thresholds with multiple neighborhood information of the signal in the filter window, is very precise, while avoiding an undue increase in computational complexity. For impulse noise suppression without smearing fine details and edges in the image, extensive experimental results demonstrate that our scheme performs significantly better than many existing, well-accepted decision-based methods. The performance of our proposed algorithm will be analyzed based PSNR and MSE values.

LIST OF FIGURES

Figure NoFigure TitlePage NoFigure 2.1 Normal and Pesedo-photo graph Image8Figure 2.2 Pixel value of a Image9Figure 2.3Color values form 32-bit table10Figure 2.4Metafile format Images12Figure 2.5Digital Image Processing block diagram14Figure 2.6Digital Camera 15Figure 2.7Scanners15Figure 2.8Enhancement Image16Figure 2.9Image Restoration16Figure 2.10Black and white to color image17Figure 2.11Image Segmentation17Figure 2.12Block diagram of proposed scheme20Figure 3.1Example of frame separation29Figure 4.11D Gaussian distributions with mean 0 and Standard deviation 1 41Figure 5.1Blur Length48Figure 5.2Blur Angle49Figure 6.133 averaging kernel often used in mean filtering53Figure 6.2Illustrates an example of median filtering54Figure 6.3Calculating the median value of a pixel neighborhood 55Figure 6.4The original image and the same image of median filter56Figure 6.5Comparison of the nonlinear median filter and the linear mean filter. 57Figure 6.6Standard median filter59Figure 6.7Adaptive median filter61Figure 6.8Tolerance based switched median filter62Figure 6.9Decision based algorithm 62Figure 6.10Modified decisions based un-symmetric trimmed median Filter65Figure 8.1Project execution first step71Figure 8.2Select the video sample72Figure 8.3 Browsing the video sample72Figure 8.4Frame separation73Figure 8.5Frame selection73Figure 8.6Selecting the frame74Figure 8.7Generating the noise in first level74Figure 8.8Generating the noise in second level75Figure 8.9Generating the noise in third level75Figure 8.10 Generating the noise in fourth level76Figure 8.11Generating the noise in fifth level76Figure 8.12The output of the standard median filter77Figure 8.13The output of the adaptive median filter77Figure 8.14The output of the tolerance based switched median filter78Figure 8.15The output of the decision based algorithm78Figure 8.16The output values of the existing methods79Figure 8.17The output of the decision based unsymmetric trimmed Median filter79Figure 8.18The output of the decision based unsymmetric trimmed Median filter for video Restoration80Figure 8.19The output value of the modified method80

LIST OF SYMBOLS

Noise Variance

Alpha

KNoisy Approximation

IImage

LIST OF ABBREVIATIONS

CWM: Center Weighted Median FilterPSM:Progressive Switching Median FilterPNG:Portable Network GraphicsJPEG:Joint Photographic Experts GroupGIF:Graphics Interchange FormatTV:TelevisionCT:Computed TomographyMRI:Magnetic Resonance ImagingPSNR:Peak Signal-to-Noise RatioIEF:Image Enhancement FactorMSE:Mean Square ErrorFPS:Frames per SecondCD:Compact DiscNLE:Non-linear Editing WorkstationHDTV:High Definition TelevisionMPEG:Moving Picture Experts GroupDVD:Digital Video DiscCPU:Central Processing UnitTBC: Time Base CorrectorsNEC:Nippon Electric CorporationDVE:Digital Video EffectsADO:AmpexDigital OpticsVTR:Video Tape RecordersEFP:Electronic Field ProductionHDV:High Definition VideoPAL:Phase Alternating LineSECAM:Sequential Couleur Avec MemoireNTSC:National Television Standards CommitteeCRT:Cat