Digital Image Processing

Digital Image Processing is a fundamental textbook designed to cater to the needs of undergraduate engineering students of computer science, information technology, electronics and electrical engineering. The book aims to provide an understanding of the principles and various processing techniques of digital images to further the utility of images. Spread over twelve chapters, this book starts with a discussion on fundamentals followed by a brief chapter on digital imaging system, and then broadly addresses the core topics of interest such as image transforms, image enhancement, image compression, image segmentation, colour image processing. The book also extends the discussion to popular research domains such as biometrics, steganography, image mining and content based retrieval systems providing a brief overview on these topics. The book strikes a perfect balance between theoretical and mathematical exposition with lots of numerical examples, review questions, numerical exercises, and MATLAB programs.

Table of Contents:
1.1 OVERVIEW OF IMAGE PROCESSING; 1.2 NATURE OF DIGITAL IMAGE PROCESSING; 1.3 IMAGE PROCESSING AND RELATED FIELDS; 1.3.1 IMAGE PROCESSING AND COMPUTER GRAPHICS; 1.3.2 IMAGE PROCESSING AND SIGNAL PROCESSING; 1.3.3 IMAGE PROCESSING AND MACHINE VISION; 1.3.4 IMAGE PROCESSING AND VIDEO PROCESSING; 1.3.5 IMAGE PROCESSING AND OPTICS; 1.3.6 IMAGE PROCESSING AND STATISTICS; 1.4 DIGITAL IMAGE REPRESENTATION; 1.5 TYPES OF IMAGES; 1.5.1 TYPES OF IMAGES BASED ON ATTRIBUTES; 1.5.2 TYPES OF IMAGES BASED ON COLOUR; 1.5.2.1 GREY SCALE IMAGES; 1.5.2.2 BINARY IMAGES; 1.5.2.3 COLOUR IMAGES; 1.5.2.4 PSEUDOCOLOUR IMAGES; 1.5.3 TYPES OF IMAGES BASED ON DIMENSIONS; 1.5.4 TYPES OF IMAGES BASED ON DATA TYPES; 1.6 DIGITAL IMAGE PROCESSING OPERATIONS; 1.7 FUNDAMENTAL STEPS IN IMAGE PROCESSING; 1.7.1 IMAGE ENHANCEMENT; 1.7.2 IMAGE RESTORATION; 1.7.3 IMAGE COMPRESSION; 1.7.4 IMAGE ANALYSIS; 1.7.5 IMAGE SYNTHESIS; 1.8 IMAGE PROCESSING APPLICATIONS; 1.8.1 BIOMETRICS; 1.8.2 MEDICAL IMAGING; 1.8.3 FACTORY AUTOMATION; 1.8.4 REMOTE SENSING; 1.8.5 DOCUMENT IMAGE PROCESSING; 1.8.6 DEFENSE/MILITARY APPLICATIONS; 1.8.7 PHOTOGRAPHY; 1.8.8 ENTERTAINMENT; 1.9 DIGITAL IMAGING SYSTEM; 1.9.1 IMAGE SENSORS; 1.9.2 IMAGE STORAGE; 1.9.3 IMAGE PROCESSOR; 1.9.4 OUTPUT DEVICES; 1.9.5 NETWORKING COMPONENTS; 1.9.6 IMAGE PROCESSING SOFTWARE; 2.1 PHYSICAL ASPECTS OF IMAGE ACQUISITION; 2.1.1 NATURE OF LIGHT; 2.1.2 LIGHTING SYSTEM DESIGN; 2.1.3 SIMPLE IMAGE FORMATION PROCESS; 2.2 BIOLOGICAL ASPECTS OF IMAGE ACQUISITION; 2.2.1 HUMAN VISUAL SYSTEM; 2.2.2 PROPERTIES OF HUMAN VISUAL SYSTEM; 2.2.2.1 BRIGHTNESS ADAPTATION; 2.2.2.2 INTENSITY AND BRIGHTNESS; 2.2.2.3 SIMULTANEOUS CONTRAST; 2.2.2.4 MACH BANDS; 2.2.2.5 FREQUENCY RESPONSE; 2.3 REVIEW OF DIGITAL CAMERA; 2.4 SAMPLING AND QUANTIZATION; 2.5 IMAGE QUALITY; 2.5.1 OPTICAL RESOLUTION; 2.5.2 IMAGE DISPLAY DEVICES AND DEVICE RESOLUTION; 2.5.3 DIGITAL HALFTONE PROCESS; 2.5.3.1 RANDOM DITHERING; 2.5.3.2 ORDERED DITHERING; 2.5.3.3 NON-PERIODIC DITHERING; 2.6 IMAGE STORAGE AND FILE FORMATS; 2.6.1 TYPES OF FILE FORMATS; 2.6.2 STRUCTURE OF FILE FORMAT; 3.1 BASIC RELATIONSHIPS AND DISTANCE METRICS; 3.1.1 IMAGE COORDINATE SYSTEM; 3.1.2 IMAGE TOPOLOGY; 3.1.3 CONNECTIVITY; 3.1.4 RELATIONS; 3.1.5 DISTANCE MEASURES; 3.1.6 SOME IMPORTANT IMAGE CHARACTERISTICS; 3.2 IMAGE PROCESSING OPERATIONS; 3.2.1 ARITHMETIC OPERATIONS; 3.2.1.1 IMAGE ADDITION; 3.2.1.2 IMAGE SUBTRACTION; 3.2.1.3 IMAGE MULTIPLICATION; 3.2.1.4 IMAGE DIVISION; 3.2.1.5 APPLICATIONS OF ARITHMETIC OPERATIONS; 3.2.2 LOGICAL OPERATIONS; 3.2.2.1 AND/NAND; 3.2.2.2 OR/NOR; 3.2.2.3 XOR/XNOR T; 3.2.2.4 INVERT/LOGICAL NOT; 3.2.3 GEOMETRICAL OPERATIONS; 3.2.3.1 TRANSLATION; 3.2.3.2 SCALING; 3.2.3.3 ZOOMING ION; 3.2.3.4 LINEAR INTERPOLATION; 3.2.3.5 MIRROR OR REFLECTION OPERATION; 3.2.3.6 SHEARING; 3.2.3.7 ROTATION; 3.2.3.8 AFFINE TRANSFORM; 3.2.3.9 INVERSE TRANSFORMATION; 3.2.3.10 3D TRANSFORMS HNIQUES; 3.2.4 IMAGE INTERPOLATION TECHNIQUES; 3.2.4.1 DOWNSAMPLING; 3.2.4.2 UPSAMPLING; 3.2.5 SET OPERATIONS S; 3.2.6 STATISTICAL OPERATIONS PERATIONS; 3.2.7 CONVOLUTION AND CORRELATION OPERATIONS IONS 3.3 DATA STRUCTURES AND IMAGE PROCESSING APPLICATIONS DEVELOPMENT; 3.3.1 MATRIX; 3.3.2 CHAIN CODE; 3.3.3 REGION ADJACENCY GRAPH; 3.3.4 RELATIONAL STRUCTURES; 3.3.5 HIERARCHICAL DATA STRUCTURES; 3.3.5.1 PYRAMIDS; 3.3.5.2 QUADTREES; 3.3.6 APPLICATION DEVELOPMENT; 4.1 NEED FOR IMAGE TRANSFORMS; 4.1.1 INTRODUCTION TO FOURIER TRANSFORM; 4.1.2 DISCRETE FOURIER TRANSFORM; 4.1.3 FAST FOURIER TRANSFORM; 4.2 PROPERTIES OF FOURIER TRANSFORM; 4.2.1 SAMPLING THEOREM; 4.2.2 PARSEVAL'S THEOREM; 4.3 DISCRETE COSINE TRANSFORM; 4.4 DISCRETE SINE TRANSFORM; 4.5 WALSH TRANSFORM; 4.6 HADAMARD TRANSFORM; 4.7 HAAR TRANSFORM; 4.8 SLANT TRANSFORM; 4.9 SVD AND KL TRANSFORMS; 4.9.1 SINGULAR-VALUE DECOMPOSITION TRANSFORM; 4.9.2 KARHUNEN-LOEVE TRANSFORM OR HOTELLING TRANSFORM; 5.1 IMAGE QUALITY AND NEED FOR IMAGE ENHANCEMENT; 5.1.1 IMAGE QUALITY FACTORS; 5.1.2 IMAGE QUALITY ASSESSMENT TOOL; 5.1.3 IMAGE QUALITY METRICS; 5.2 IMAGE ENHANCEMENT POINT OPERATIONS; 5.2.1 LINEAR AND NON-LINEAR FUNCTIONS; 5.2.1.1 INVERSION (DIGITAL NEGATIVE OPERATION); 5.2.1.2 WHAT IS A NON-LINEAR OPERATOR?; 5.2.2 PIECEWISE LINEAR FUNCTIONS; 5.2.2.2 INTENSITY SLICING; 5.2.2.3 BIT-PLANE SLICING; 5.2.3 HISTOGRAM-BASED TECHNIQUES; 5.2.3.1 HISTOGRAM STRETCHING; 5.2.3.2 HISTOGRAM SLIDING; 5.2.3.3 HISTOGRAM EQUALIZATION; 5.2.3.4 HISTOGRAM SPECIFICATION; 5.2.3.5 LOCAL AND ADAPTIVE CONTRAST ENHANCEMENT; 5.3 SPATIAL FILTERING CONCEPTS; 5.3.1 IMAGE SMOOTHING SPATIAL FILTERS; 5.3.1.1 HOW TO DESIGN A DISCRETE GAUSSIAN MASK?; 5.3.1.2 NON-LINEAR FILTERS; 5.3.1.3 DIRECTIONAL SMOOTHING; 5.3.2 IMAGE SHARPENING SPATIAL FILTERS; 5.3.2.1 HIGH-BOOST FILTER; 5.4 FREQUENCY DOMAIN FILTERING; 5.4.1 IMAGE SMOOTHING IN FREQUENCY DOMAIN; 5.4.2 IMAGE SHARPENING IN FREQUENCY DOMAIN; 5.4.2.1 BAND-PASS FILTERING; 5.5 IMAGE DEGRADATION (RESTORATION) MODEL; 5.6 CATEGORIES OF IMAGE DEGRADATIONS; 5.6.1 NOISE MODELLING; 5.6.1.1 NOISE CATEGORIES BASED ON DISTRIBUTION; 5.6.1.2 NOISE CATEGORIES BASED ON CORRELATION; 5.6.1.3 NOISE CATEGORIES BASED ON NATURE; 5.6.1.4 NOISE CATEGORIES BASED ON SOURCE; 5.6.2 BLUR AND DISTORTIONS; 5.7 IMAGE RESTORATION IN THE PRESENCE OF NOISE ONLY; 5.7.1 MEAN FILTERS; 5.7.1.1 ARITHMETIC MEAN FILTER; 5.7.1.2 CONTRA-HARMONIC MEAN FILTER; 5.7.1.3 GEOMETRIC MEAN FILTER; 5.7.1.4 HARMONIC MEAN FILTER; 5.7.1.5 YP MEAN FILTER; 5.7.2 ORDER-STATISTICS FILTERS; 5.7.2.1 MEDIAN FILTER; 5.7.2.2 MAXIMUM FILTER; 5.7.2.3 MINIMUM FILTER; 5.7.2.4 MIDPOINT FILTER; 5.7.2.5 ALPHA-TRIMMED MEAN FILTER; 5.8 IMAGE RESTORATION TECHNIQUES; 5.8.1 CONSTRAINED METHOD; 5.8.2 UNCONSTRAINED METHOD; 5.8.2.1 WIENER FILTER; 5.8.2.2 CONSTRAINED LEAST SQUARE FILTER; 5.8.2.3 PSEUDO-INVERSE FILTER; 5.8.3 INTERACTIVE IMAGE RESTORATION; 5.8.4 BLIND IMAGE RESTORATION; 5.9 GEOMETRICAL TRANSFORMS FOR IMAGE RESTORATION; 6.1 IMAGE COMPRESSION MODEL; 6.2 COMPRESSION ALGORITHM AND ITS TYPES; 6.2.1 ENTROPY CODING; 6.2.2 PREDICTIVE CODING; 6.2.3 TRANSFORM CODING; 6.2.4 LAYERED CODING; 6.3 TYPES OF REDUNDANCY; 6.3.1 CODING REDUNDANCY; 6.3.2 INTER-PIXEL REDUNDANCY; 6.3.3 PSYCHOVISUAL REDUNDANCY; 6.3.4 CHROMATIC REDUNDANCY; 6.4 LOSSLESS COMPRESSION ALGORITHMS; 6.4.1 RUN-LENGTH CODING; 6.4.2 HUFFMAN CODING; 6.4.2.1 CANONICAL HUFFMAN CODE; 6.4.2.2 HUFFMAN DECODER; 6.4.2.3 CHARACTERISTICS OF HUFFMAN CODING; 6.4.4 BIT-PLANE CODING; 6.4.5 ARITHMETIC CODING; 6.4.6 DICTIONARY-BASED CODING; 6.4.6.1 ENCODING; 6.4.6.2 DECODING; 6.4.7 LOSSLESS PREDICTIVE CODING; 6.5 LOSSY COMPRESSION ALGORITHMS; 6.5.1. LOSSY PREDICTIVE CODING; 6.5.2 VECTOR QUANTIZATION; 6.5.2.1 CODEBOOK DESIGN; 6.5.2.2 GENERALIZED LLOYD ALGORITHM; 6.5.3 BLOCK TRANSFORM CODING; 6.5.3.1 SUB-IMAGE SELECTION; 6.5.3.2 TRANSFORM SELECTION; 6.5.3.3 BIT ALLOCATION; 6.5.3.4 ZONAL CODING; 6.5.3.5 THRESHOLD MARK; 6.6 IMAGE AND VIDEO COMPRESSION STANDARDS; 6.6.1 JPEG; 6.6.1.1 SEQUENTIAL DCT-BASED MODE (BASELINE ALGORITHM); 6.6.1.2 LOSSLESS MODE; 6.6.1.3 PROGRESSIVE ENCODING; 6.6.1.4 HIERARCHICAL MODE; 6.6.2 VIDEO COMPRESSION-MPEG; 6.6.2.1 MACROBLOCK FORMATION; 6.6.2.2 FRAME FORMATION; 6.6.2.3 GROUP OF PICTURES; 6.6.2.4 MOTION ESTIMATION; 6.6.2.5 AUDIO COMPRESSION; 6.6.3 MPEG VARIATIONS; 7.1 INTRODUCTION; 7.2 CLASSIFICATION OF IMAGE SEGMENTATION ALGORITHMS; 7.3 DETECTION OF DISCONTINUITIES AND LINE-DETECTION APPROACHES; 7.4 EDGE DETECTION; 7.4.1 STAGES IN EDGE DETECTION; 7.4.1.1 FILTERING; 7.4.1.2 DIFFERENTIATION; 7.4.1.3 LOCALIZATION; 7.4.2 TYPES OF EDGE DETECTORS; 7.4.3 FIRST-ORDER EDGE DETECTION OPERATORS; 7.4.3.1 ROBERTS OPERATOR; 7.4.3.2 PREWITT OPERATOR; 7.4.3.3 SOBEL OPERATOR; 7.4.3.4 TEMPLATE MATCHING MASKS; 7.4.4 SECOND-ORDER DERIVATIVE FILTERS; 7.4.4.1 LAPLACIAN OF GAUSSIAN (MARR-HILDRITH) OPERATOR; 7.4.4.2 COMBINED DETECTION; 7.4.4.3 DIFFERENCE OF GAUSSIAN FILTER; 7.4.4.4 CANNY EDGE DETECTION; 7.4.4.5 PATTERN FIT ALGORITHM; 7.4.5 EDGE OPERATOR PERFORMANCE; 7.4.6 EDGE-LINKING ALGORITHMS; 7.4.6.1 EDGE RELAXATION; 7.4.6.2 GRAPH THEORETIC ALGORITHMS; 7.5 HOUGH TRANSFORMS AND SHAPE DETECTION; 7.6 CORNER DETECTION; 7.7 PRINCIPLE OF THRESHOLDING; 7.7.1 GLOBAL THRESHOLDING ALGORITHMS; 7.7.2 MULTIPLE THRESHOLDING GORITHMS; 7.7.3 ADAPTIVE THRESHOLDING ALGORITHM; 7.7.4 OPTIMAL THRESHOLDING ALGORITHMS; 7.7.4.1 PARAMETRIC METHODS ALGORITHMS; 7.7.4.2 NON-PARAMETRIC METHODS; 7.8 PRINCIPLE OF REGION GROWING; 7.8.1 REGION-GROWING ALGORITHMG; 7.8.2 SPLIT-AND-MERGE ALGORITHM; 7.8.3 SPLIT-AND-MERGE ALGORITHM USING PYRAMID QUADTREE; 7.9 DYNAMIC SEGMENTATION APPROACHES G PYRAMID QUADTREE; 7.9.1 USE OF MOTION IN SEGMENTATION; 7.9.2 HYBRID EDGE/REGION APPROACHES; 7.10 VALIDATION OF SEGMENTATION ALGORITHMS; 8.1 COLOUR FUNDAMENTALS; 8.2 DEVICES FOR COLOUR IMAGING; 8.2.1 TYPES OF CAMERAS; 8.2.2 COLOUR MONITORS; 8.3 COLOUR IMAGE STORAGE AND PROCESSING; 8.4 COLOUR MODELS; 8.4.1 RGB COLOUR MODEL; 8.4.2 HSI COLOUR MODEL; 8.4.3 HSV COLOUR MODEL; 8.4.4 HLS COLOUR MODEL; 8.4.5 TV COLOUR MODELS; 8.4.6 PRINTING COLOUR MODELS; 8.5 COLOUR QUANTIZATION; 8.5.1 POPULARITY (OR POPULOSITY) ALGORITHM; 8.5.2 MEDIAN-CUT ALGORITHM; 8.5.3 OCTREE-BASED ALGORITHM; 8.6 PSEUDOCOLOUR IMAGE PROCESSING; 8.7 FULL COLOUR PROCESSING; 8.7.1 COLOUR TRANSFORMATIONS; 8.7.1.1 INTENSITY MODIFICATIONS; 8.7.1.2 COLOUR NEGATIVES; 8.7.1.3 COLOUR SLICING; 8.7.1.4 TONAL AND COLOUR CORRECTION; 8.7.1.5 HISTOGRAM PROCESSING; 8.7.2 IMAGE FILTERS FOR COLOUR IMAGES; 8.7.3 COLOUR IMAGE SEGMENTATION; 8.7.3.1 THRESHOLDING; 8.7.3.2 K-MEANS CLUSTERING TECHNIQUE; 8.7.3.3 RGB COLOUR SPACE SEGMENTATION; 8.7.4 COLOUR FEATURES; 9.1 NEED FOR MORPHOLOGICAL PROCESSING; 9.2 MORPHOLOGICAL OPERATORS; 9.2.1 ALGORITHM FOR DILATION AND EROSION; 9.2.2 OPENING AND CLOSING OPERATIONOSION; 9.3 HIT OR MISS TRANSFORM; 9.4 BASIC MORPHOLOGICAL ALGORITHMSN; 9.4.1 BOUNDARY EXTRACTION; 9.4.2 NOISE REMOVAL; 9.4.3 THINNING; 9.4.4 THICKENING; 9.4.5 CONVEX HULL; 9.4.6 SKELETONIZATION; 9.4.7 MEDIAL AXIS TRANSFORM AND DISTANCE TRANSFORM; 9.4.8 REGION FILLING; 9.4.9 EXTRACTION OF CONNECTED COMPONENT; 9.4.10 PRUNING; 9.5 GRAY SCALE MORPHOLOGY; 9.5.1 MORPHOLOGICAL GRADIENT; 9.5.2 TOP-HAT AND WELL TRANSFORMATIONS; 9.5.3 MORPHOLOGICAL RECONSTRUCTION; 9.5.4 WATERSHED ALGORITHM; 10.1 BOUNDARY AND REGION REPRESENTATION; 10.1.1 CHAIN CODE; 10.1.2 POLYGONAL APPROXIMATIONS; 10.1.3 SIGNATURES; 10.1.4 BENDING ENERGY; 10.1.5 STATISTICAL MOMENTS; 10.1.6 REGION REPRESENTATION; 10.2 BOUNDARY DESCRIPTIONS; 10.2.1 SIMPLE DESCRIPTORS; 10.2.2 SHAPE NUMBER; 10.2.3 FOURIER DESCRIPTORS; 10.2.4 RUN CODE; 10.2.5 PROJECTIONS; 10.2.6 CONCAVITY TREE; 10.3 COMPONENT LABELING; 10.3.1 RECURSIVE ALGORITHM; 10.3.2 SEQUENTIAL ALGORITHM; 10.4 BASICS OF REGIONAL DESCRIPTIONS; 10.4.1 HISTOGRAM (BRIGHTNESS) FEATURES; 10.4.2 SHAPE FEATURES; 10.4.3 SPATIAL MOMENTS; 10.4.4 CENTRAL AND INVARIANT MOMENTS; 10.4.4 CENTRAL AND INVARIANT MOMENTS; 10.4.5 TOPOLOGICAL FEATURES; 10.4.6 TRANSFORM FEATURES; 10.4.7 TEXTURE FEATURES; 10.4.8 SYNTACTIC AND STRUCTURAL FEATURES; 10.5 FEATURE SELECTION TECHNIQUES; 11.1 PATTERNS AND PATTERN CLASSES; 11.2 TEMPLATE MATCHING; 11.3 INTRODUCTION TO CLASSIFICATION; 11.4 DECISION- THEORETIC METHODS; 11.4.1 LINEAR DISCRIMINANT ANALYSIS; 11.4.2 BAYESIAN CLASSIFIERS; 11.4.3 NON PARAMETRIC STATISTICAL METHODS; 11.4.4 REGRESSION METHODS; 11.5 STRUCTURAL AND SYNTACTIC CLASSIFIER ALGORITHMS; 11.5.1 GRAMMAR ORIENTED RECOGNITION; 11.5.2 SHAPE MATCHING ALGORITHMS; 11.5.3 STRING MATCHING ALGORITHMS; 11.5.4 RULE BASED ALGORITHMS; 11.5.5 GRAPH ORIENTED APPROACHES; 11.6 EVALUATION OF CLASSIFIER ALGORITHMS; 11.7 BIOMETRICS CASE STUDIES; 11.7.1 FACE RECOGNITION; 11.7.2 IRIS RECOGNITION; 11.7.3 FINGERPRINT RECOGNITION; 11.7.4 SIGNATURE VERIFICATION; 11.8 CLUSTERING TECHNIQUES; 11.8.1 SIMILARITY MEASURES; 11.8.2 HIERARCHICAL METHODS; 11.8.3 K-MEANS ALGORITHM; 11.8.4 CLUSTER EVALUATION METHODS; 12.1 SOFT COMPUTING AND IMAGE PROCESSING; 12.1.1 FUZZY LOGIC; 12.1.2 GENETIC ALGORITHMS; 12.1.3 ARTIFICIAL NEURAL NETWORKS; 12.2 MULTIRESOLUTION ANALYSIS AND WAVELET TRANSFORMS; 12.2.1 WAVELET TRANSFORMS; 12.3 IMAGE SYNTHESIS; 12.3.1 IMAGE REGISTRATION TECHNIQUES; 12.3.2 IMAGE FUSION ALGORITHMS; 12.3.3 IMAGE VISUALIZATION; 12.3.4 IMAGE UNDERSTANDING AND STEREO IMAGING; 12.4 DIGITAL WATERMARKING; 12.5 IMAGE MINING AND CONTENT BASED RETRIEVAL SYSTEMS; 12.5.1 DATA MINING FOR IMAGE DATA; 12.5.2 CONTENT - BASED IMAGE RETRIEVAL SYSTEMS; APPENDIX A - A BRIEF INTRODUCTION TO MATLAB PROGRAMMING; APPENDIX B - IMAGEJ AND OTHER OPEN SOURCE ALTERNATIVES; APPENDIX C - LABORATORY EXERCISES

About the Author :
Dr S Sridhar is Associate Professor, Department of Information Science& Technology, College of Engineering Guindy, Anna University, Chennai. He has more than 20 years of active teaching and research experience. He has served as a project trainee at Council for Scientific and Industrial Research (CSIR), Chennai and Indian Space Research Organisation (ISRO) for a short stint. A PhD from Anna University, his doctoral specialisation was in medical imaging. He has published a lot of research articles related to medical imaging in leading national and international journals.