Preface

Acknowledgements

About the Authors

1 Introduction

  Preview

  1.1 Background

  1.2 What Is Digital Image Processing?

  1.3 Background on MATLAB and the Image Processing Toolbox

  1.4 Areas of Image Processing Covered in the Book

  1.5 The Book Web Site

  1.6 Notation

  1.7 Fundamentals

    1.7.1 The MATLAB Desktop

    1.7.2 Using the MATLAB Editor/Debugger

    1.7.3 Getting Help

    1.7.4 Saving and Retrieving Work Session Data

    1.7.5 Digital Image Representation

    1.7.6 Image I/O and Display

    1.7.7 Classes and Image Types

    1.7.8 M-Function Programming

  1.8 How References Are Organized in the Book

  Summary

2 Intensity Transformations and Spatial Filtering

  Preview

  2.1 Background

  2.2 Intensity Transformation Functions

    2.2.1 Functions imadjust and stretchlim

    2.2.2 Logarithmic and Contrast- Stretching Transformations

    2.2.3 Specifying Arbitrary Intensity Transformations

    2.2.4 Some Utility M-functions for Intensity Transformations

  2.3 Histogram Processing and Function Plotting

    2.3.1 Generating and Plotting Image Histograms

    2.3.2 Histogram Equalization

    2.3.3 Histogram Matching (Specification)

    2.3.4 Function adapthisteq

  2.4 Spatial Filtering

    2.4.1 Linear Spatial Filtering

    2.4.2 Nonlinear Spatial Filtering

  2.5 Image Processing Toolbox Standard Spatial Filters

    2.5.1 Linear Spatial Filters

    2.5.2 Nonlinear Spatial Filters

  2.6 Using Fuzzy Techniques for Intensity Transformations and Spatial Filtering

    2.6.1 Background

    2.6.2 Introduction to Fuzzy Sets

    2.6.3 Using Fuzzy Sets

    2.6.4 A Set of Custom Fuzzy M-functions

    2.6.5 Using Fuzzy Sets for Intensity Transformations

    2.6.6 Using Fuzzy Sets for Spatial Filtering

  Summary

3 Filtering in the Frequency Domain

  Preview

  3.1 The 2-D Discrete Fourier Transform

  3.2 Computing and Visualizing the 2-D DFT in MATLAB

  3.3 Filtering in the Frequency Domain

    3.3.1 Fundamentals

    3.3.2 Basic Steps in DFT Filtering

    3.3.3 An M-function for Filtering in the Frequency Domain

  3.4 Obtaining Frequency Domain Filters from Spatial Filters

  3.5 Generating Filters Directly in the Frequency Domain

    3.5.1 Creating Meshgrid Arrays for Use in Implementing Filters in the Frequency Domain

    3.5.2 Lowpass (Smoothing) Frequency Domain Filters

    3.5.3 Wireframe and Surface Plotting

  3.6 Highpass (Sharpening) Frequency Domain Filters

    3.6.1 A Function for Highpass Filtering

    3.6.2 High-Frequency Emphasis Filtering

  3.7 Selective Filtering

    3.7.1 Bandreject and Bandpass Filters

    3.7.2 Notchreject and Notchpass Filters

  Summary

4 Image Restoration and Reconstruction

  Preview

  4.1 A Model of the Image Degradation/Restoration Process

  4.2 Noise Models

    4.2.1 Adding Noise to Images with Function imnoise

    4.2.2 Generating Spatial Random Noise with a Specified Distribution

    4.2.3 Periodic Noise

    4.2.4 Estimating Noise Parameters

  4.3 Restoration in the Presence of Noise Only—Spatial Filtering

    4.3.1 Spatial Noise Filters

    4.3.2 Adaptive Spatial Filters

  4.4 Periodic Noise Reduction Using Frequency Domain Filtering

  4.5 Modeling the Degradation Function

  4.6 Direct Inverse Filtering

  4.7 Wiener Filtering

  4.8 Constrained Least Squares (Regularized) Filtering

  4.9 Iterative Nonlinear Restoration Using the Lucy-Richardson Algorithm

  4.10 Blind Deconvolution

  4.11 Image Reconstruction from Projections

    4.11.1 Background

    4.11.2 Parallel-Beam Projections and the Radon Transform

    4.11.3 The Fourier Slice Theorem and Filtered Backprojections

    4.11.4 Filter Implementation

    4.11.5 Reconstruction Using Fan-Beam Filtered Backprojections

    4.11.6 Function radon

    4.11.7 Function iradon

    4.11.8 Working with Fan-Beam Data

  Summary

5 Geometric Transformations and Image Registration

  Preview

  5.1 Transforming Points

  5.2 Affine Transformations

  5.3 Proj