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Fundamentals of Digital Image Processing: Techniques and Transformations

Explore the foundational concepts and advanced methods in processing digital images for various applications like gamma-ray imaging, spatial filtering, and wavelets. Understand color models, compression methods, and binary image analysis using mathematical tools and spatial filters.

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Fundamentals of Digital Image Processing: Techniques and Transformations

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  1. Digital Image Processing3rd Edition Rafael C.Gonzalez, Richard E.Woods Prentice Hall, 2008

  2. Table of Content • Chapter 1 • 1.1 Introduction 1.2 The Origins of Digital Image processing • 1.2 Examples of fields that use Digital ImageProcessing: - Gamma ray Imaging - Imaging in Ultra Violet Band - Imaging in Visible and Infrared bands - Imaging in Microwave Band - Imaging in radio Band - Some other examples

  3. Table of Content • Chapter 1 1.4 Fundamental Steps in Digital ImageProcessing 1.5 Components of an Image Processing System

  4. Table of Content • Chapter 2 Digital Image Fundamentals 2.1 Elements of Visual perception 2.2 Light and the Electromagnetic Spectrum 2.3 Image Sensing and Acquisition 2.4 Image Sampling and Quantization 2.5 Some Basic relationship between Pixels 2.6 An introduction to mathematical tools used in digital image processing

  5. Table of Content • Chapter 2 Digital Image Fundamentals 2.6 An introduction to mathematical tools used in digital image processing • Array verses matrix operations • Linear verses nonlinear operations • Arithmetic operations • Set and Logical operation • Vectors and matrix operations • Image transforms • Probabilistic methods

  6. Table of Content • Chapter 3 Intensity Transformations and Spatial Filtering 3.1 Background 3.2 Some Basic Intensity Transformation Functions 3.3 Histogram Processing 3.4 Fundamentals of Spatial Filtering 3.5 Smoothing Spatial Filters 3.6 Sharpening Spatial Filters 3.7 Combining Spatial Enhancement Methods 3.8 Using Fuzzy Techniques for Intensity Transformations and Spatial Filtering

  7. Table of Content • Chapter 4 Filtering in Frequency Domain 4.1 Background 4.2 Preliminary Concepts (Introduction to FourierTransform and Frequency Domain) 4.3 Sampling and Fourier transform of SampledFunctions 4.4 Discrete Fourier Transform (DFT) of one Variable 4.5 Extension of functions of Two Variables 4.6 Some Properties of 2-D Discrete Fourier Transform 4.7 Basic of Filtering in Frequency Domain

  8. Table of Content • Chapter 4 Filtering in Frequency Domain 4.8 Image Smoothing using Frequency Domain Filters 4.9 Image Sharpening using Frequency Domain Filters 4.10 Selective Filtering - Bandreject and Bandpass filters - Notch Filtering 4.11 Implementation

  9. Table of Content • Chapter 4 Some other useful transforms • Walsh Transform • Hadamard Transform • Discrete Cosine Transform (DCT) • Principal Component Analysis (PCA) Karhunen Loeve Transform (KLT) Hotling Transform

  10. Table of Content • Chapter 5 Image Restoration and Reconstruction 5.1 A Model of the Image Degradation/Restoration Process 5.2 Noise Models 5.3 Restoration in the Presence of Noise Only-Spatial Filtering 5.4 Periodic Noise Reduction by Frequency Domain Filtering 5.5 Linear, Position-Invariant Degradations 5.6 Estimating the Degradation Function

  11. Table of Content • Chapter 5 Image Restoration 5.7 Inverse Filtering 5.8 Minimum Mean Square (Winner) Filtering 5.9 Constrained Least Squares Filtering 5.10 Geometric Mean Filter 5.11 Image Reconstruction from Projections

  12. Table of Content • Chapter 5 Image Restoration How to find linear motion blur and out of focus blur parameters and then restore such degraded images

  13. Table of Content • Chapter 6 Color Image processing 6.1 Color Fundamentals 6.2 Color Models 6.3 Pseudo-color Image processing 6.4 Basics of Full-Color Image Processing 6.5 Color Transformation

  14. Table of Content • Chapter 6 Color Image processing 6.6 Smoothing and Sharpening 6.7 Image Segmentation based on Color 6.8 Noise in Color Images 6.9 Color Image Compression

  15. Table of Content • Chapter 7 Waelets and Multiresolution Processing

  16. Chapter 8 Image Compression - Fundamentals - Coding redundancy - Spatial and temporal redundancy - Irrelevant information - Measuring image information - Fidelity criteria - Image compression methods - Image formats, Containers and compression standards

  17. Chapter 8 Image Compression • - Some basic Compression methods - Huffman coding - Arithmetic Coding - LZW coding - Run length coding - Symbol-based coding - Bit-plane coding - Block transform coding - Predictive coding - Wavelet coding - Digital Image watermarking

  18. Chapter 8 Image Compression • - Digital Image watermarking

  19. Chapter-9Binary Image Analysis • Binary Image Morphology • Structuring element • Basic morphological operations • Dilation and Erosion • Opening and Closing • The Hit-or-Miss transformation

  20. Chapter-9 Binary Image Analysis • Some basic morphological algorithms • Boundary extraction • Hole filling • Extraction of connected components • Convex Hull • Thinning • Thickening • Skeleton • Pruning

  21. Table of Content • Chapter 10 Image Segmentation • Chapter 11 Representation and Description • Chapter 12 Object Recognition

  22. نحوه ارزیابی این درس امتحان میان ترم اول 2 نمره امتحان میان ترم دوم 2 نمره امتحان پایان ترم 7 نمره سمینار 2 نمره 5 نمره MathLabتمرين های برنامه نويسی پروژه برنامه نویسی و گزارش فنی 2 نمره

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