Digital Image Processing 3 rd Edition. Rafael C.Gonzalez, Richard E.Woods Prentice Hall, 2008

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2 Digital Image Processing 3 rd Edition Rafael C.Gonzalez, Richard E.Woods Prentice Hall, 2008

3 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

4 Table of Content Chapter Fundamental Steps in Digital Image Processing 1.5 Components of an Image Processing System

5 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

6 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

7 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

8 An example of image enhancement in spatial domain: Local histogram equalization

9 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

10 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

11 An example of image enhancement High Frequency Emphasis in frequency domain. Input image 11

12 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

13 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

14 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

15 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

16 Motion blur image restoration A motion blur image given as input The restored image

17 Motion blur image restoration A motion blue image given as input The restored image

18 Table of Content Chapter 5 Image Restoration How to restore images highly corrupted by impulse (salt and pepper) noise. Example: For noise over 80%

19 80% 95% 85% Restored image

20 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.

21 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

22 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

23 Table of Content Chapter 7 Waelets and Multiresolution Processing

24 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

25 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

26 Chapter 8 Image Compression Digital Image watermarking The Art of Secret Communication using Digital Media Introduction to image hiding / Steganography and Steganoanalysis

27 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 = 27

28 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.

29 Steganography is the art of hiding information in ways that prevent the detection of hidden messages. Steganography, derived from Greek, literally means covered writing.

30 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.

31 Image quality assessment

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

33 Table of Content Chapter 10 Image Segmentation Chapter 11 Representation and Description Chapter 12 Object Recognition

34 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

35 کالس آموزش برنامه نویسی Matlab توسط یکی از دانشجویان دکترا )دستیار آموزشی(

36 جلسات ارایه سمینار در ساعت خارج از کالس درس برگذار میشود. در هر جلسه 1.5 ساعته 2 ارایه خواهیم داشت.

37 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.