Digital Image Processing 3 rd Edition Rafael C.Gonzalez, Richard E.Woods Prentice Hall, 2008
Chapter 1 Table of Content 1.1 Introduction 1.2 The Origins of Digital Image processing 1.2 Examples of fields that use Digital Image Processing: - 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
Table of Content Chapter 1 1.4 Fundamental Steps in Digital Image Processing 1.5 Components of an Image Processing System
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
Table of Content Chapter 2 Digital Image Fundamentals 2.6 An introduction to mathematical tools used in digital image processing Array operations Linear verses nonlinear operations Arithmetic operations Set and Logical operation Vectors and matrix operations Image transforms Probabilistic methods
Chapter 3 Table of Content 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
An example of image enhancement in spatial domain: Local histogram equalization
Table of Content Chapter 4 Filtering in Frequency Domain 4.1 Background 4.2 Preliminary Concepts (Introduction to Fourier Transform and Frequency Domain) 4.3 Sampling and Fourier transform of Sampled Functions 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
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 - Band-reject and Band-pass filters - Notch Filtering 4.11 Implementation
An example of image enhancement High Frequency Emphasis in frequency domain. Input image 11
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
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
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
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
Motion blur image restoration A motion blur image given as input The restored image
Motion blur image restoration A motion blue image given as input The restored image
Table of Content Chapter 5 Image Restoration How to restore images highly corrupted by impulse (salt and pepper) noise. Example: For noise over 80%
80% 95% 85% Restored image
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 - Color Distance: a measure to compare how similar two colors are.
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
Tone and Color Corrections. All three R,G,B components of the three images are corrected according to the transform function. Note that the tone corrections (The bellow functions) are applied only on L* component. A Flat color image. The S shape transform Function ideal for boosting contrast. A light (high key) color image. A dark (low key) color image. Note how all three RGB colors are mapped to a wider range. 22
Table of Content Chapter 7 Waelets and Multiresolution Processing
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
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
Chapter 8 Image Compression Digital Image watermarking The Art of Secret Communication using Digital Media Introduction to image hiding / Steganography and Steganoanalysis
Some illustrating examples: Hiding a binary data in an image Data hiding in an image. Any data, has a binary representation. So generally, for data hiding we think of adding a bulk of binary data to a given image. 101101011010101010 100101000101101101 101010100101011010 101011110000101010 100101110101101011 010100101001000010 011101010011110110 111101110111010001 + = 27
Digital Image Watermarking A watermarked document Areas in color do not conform to coding rules that was used for coding the authenticating data in the embedding process.
Steganography is the art of hiding information in ways that prevent the detection of hidden messages. Steganography, derived from Greek, literally means covered writing.
Image quality assessment Image quality is a characteristic of an image that measures the perceived image degradation. It plays an important role in various image processing applications. How to evaluate the quality of an image.
Image quality assessment
Chapter-9 Binary Image Analysis Binary Image Morphology Structuring element Basic morphological operations Dilation and Erosion Opening and Closing The Hit-or-Miss transformation
Table of Content Chapter 10 Image Segmentation Chapter 11 Representation and Description Chapter 12 Object Recognition
نحوه ارزیابی این درس امتحان میان ترم اول 2.5 امتحان میان ترم دوم نمره 2.5 نمره امتحان پایان ترم 7 سمینار نمره 2 نمره تمرین های برنامه نویسی MathLab )حد اقل 6 سری ) 6 نمره
کالس آموزش برنامه نویسی Matlab توسط یکی از دانشجویان دکترا )دستیار آموزشی(
جلسات ارایه سمینار در ساعت خارج از کالس درس برگذار میشود. در هر جلسه 1.5 ساعته 2 ارایه خواهیم داشت.
زمان ارایه تمرینات برنامه نویسی بطور حضوری به دستیاران آموزشی از قبل برنامه ریزی شده و اعالم میگردد. زمان ارایه هر دانشجو مشخص خواهد بود و دانشجویان موظف به ارایه تمرین در زمان تعیین شده میباشند.