Signal Processing and Performance Analysis for Imaging Systems

Name: Signal Processing and Performance Analysis for Imaging Systems
Brand: Artech House
Price: 139.99 EUR
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S. Susan Young(Author)

Artech House (Publisher)

1st Edition

Published on 1. January 2008

270 pages

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978-1-59693-288-3 (ISBN)

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Signal Processing and Performance Analysis for Imaging Systems
Contents
Preface
Part I: Basic Principles of Imaging Systemsand Performance
Chapter 1 Introduction
1.1 "Combined" Imaging System Performance
1.2 Imaging Performance
1.3 Signal Processing: Basic Principles and Advanced Applications
1.4 Image Resampling
1.5 Super-Resolution Image Reconstruction
1.6 Image Restoration-Deblurring
1.7 Image Contrast Enhancement
1.8 Nonuniformity Correction (NUC)
1.9 Tone Scale
1.10 Image Fusion
References
Chapter 2 Imaging Systems
2.1 Basic Imaging Systems
2.2 Resolution and Sensitivity
2.3 Linear Shift-Invariant (LSI) Imaging Systems
2.4 Imaging System Point Spread Function and Modulation Transfer Function
2.4.1 Optical Filtering
2.4.2 Detector Spatial Filters
2.4.3 Electronics Filtering
2.4.4 Display Filtering
2.4.5 Human Eye
2.4.6 Overall Image Transfer
2.5 Sampled Imaging Systems
2.6 Signal-to-Noise Ratio
2.7 Electro-Optical and Infrared Imaging Systems
2.8 Summary
References
Chapter 3 Target Acquisition and Image Quality
3.1 Introduction
3.2 A Brief History of Target Acquisition Theory
3.3 Threshold Vision
3.3.1 Threshold Vision of the Unaided Eye
3.3.2 Threshold Vision of the Aided Eye
3.4 Image Quality Metric
3.5 Example
3.6 Summary
References
Part II: Basic Principles of Signal Processing
Chapter 4 Basic Principles of Signal and ImageProcessing
4.1 Introduction
4.2 The Fourier Transform
4.2.1 One-Dimensional Fourier Transform
4.2.1.1 Fourier Integral
4.2.1.2 Properties of Fourier Transform
4.2.2 Two-Dimensional Fourier Transform
4.2.2.1 Two-Dimensional Continuous Fourier Transform
4.2.2.3 Polar Representation of Fourier Transform
4.2.2.4 Two-Dimensional Discrete Fourier Transform and Sampling
4.3 Finite Impulse Response Filters
4.3.1 Definition of Nonrecursive and Recursive Filters
4.3.2 Implementation of FIR Filters
4.3.3 Shortcomings of FIR Filters
4.4 Fourier-Based Filters
4.4.1 Radially Symmetric Filter with a Gaussian Window
4.4.2 Radially Symmetric Filter with a Hamming Window at a Transition Point
4.4.3 Radially Symmetric Filter with a Butterworth Window at a Transition Point
4.4.4 Radially Symmetric Filter with a Power Window
4.4.5 Performance Comparison of Fourier-Based Filters
4.5 The Wavelet Transform
4.5.1 Time-Frequency Wavelet Analysis
4.5.1.1 Window Fourier Transform
4.5.1.2 Wavelet Transform
4.5.2 Dyadic and Discrete Wavelet Transform
4.5.3 Condition of Constructing a Wavelet Transform
4.5.4 Forward and Inverse Wavelet Transform
4.5.5 Two-Dimensional Wavelet Transform
4.5.6 Multiscale Edge Detection
4.6 Summary
References
Part III: Advanced Applications
Chapter 5 Image Resampling
5.1 Introduction
5.2 Image Display, Reconstruction, and Resampling
5.3 Sampling Theory and Sampling Artifacts
5.3.1 Sampling Theory
5.3.2 Sampling Artifacts
5.4 Image Resampling Using Spatial Domain Methods
5.4.1 Image Resampling Model
5.4.2 Image Rescale Implementation
5.4.3 Resampling Filters
5.5 Antialias Image Resampling Using Fourier-Based Methods
5.5.1 Image Resampling Model
5.5.2 Image Rescale Implementation
5.5.2.1 Output Requirements
5.5.2.2 Computational Efficiency
5.5.3 Resampling System Design
5.5.4 Resampling Filters
5.5.5 Resampling Filters Performance Analysis
5.5.5.1 Resampling 2-D Delta Test Pattern
5.5.5.2 Resampling 2-D Chirp Test Pattern
5.5.5.3 Ripple Property
5.6 Image Resampling Performance Measurements
5.7 Summary
References
Chapter 6 Super-Resolution
6.1 Introduction
6.1.1 The Meaning of Super-Resolution
6.1.2 Super-Resolution for Diffraction and Sampling
6.1.3 Proposed Nomenclature by IEEE
6.2 Super-Resolution Image Restoration
6.3 Super-Resolution Image Reconstruction
6.3.1 Background
6.3.2 Overview of the Super-Resolution Reconstruction Algorithm
6.3.3 Image Acquisition-Microdither Scanner Versus Natural Jitter
6.3.4 Subpixel Shift Estimation
6.3.4.1 Signal Registration
6.3.4.2 Correlation Interpolation
6.3.4.3 Resampling Via Intensity Domain Interpolation
6.3.4.4 Resampling Via Frequency Domain Interpolation
6.3.5 Motion Estimation
6.3.5.1 Gradient-Based Method
6.3.5.2 Optical Flow Method
6.3.5.3 Correlation Method
6.3.5.4 Correlation Method Within Subpixel Accuracy
6.3.6 High-Resolution Output Image Reconstruction
6.3.6.1 Number of Input Images Required
6.3.6.2 Factors Limiting the Resolution Recovery
6.3.6.3 Nonuniform Interpolation Method
6.3.6.4 Regularized Inverse Method
6.3.6.5 Error-Energy Reduction Method
6.3.6.6 Examples
6.3.6.7 Practical Considerations
6.4 Super-Resolution Imager Performance Measurements
6.4.1 Background
6.4.2 Experimental Approach
6.4.2.1 Target-Triangle Orientation Discrimination (TOD)
6.4.2.2 Field Data Collection
6.4.2.3 Sensor Description
6.4.2.4 Experiment Design
6.4.3 Measurement Results
6.5 Sensors That Benefit from Super-Resolution Reconstruction
6.5.1 Example and Performance Estimates
6.6 Performance Modeling and Prediction of Super-ResolutionReconstruction
6.7 Summary
References
Chapter 7 Image Deblurring
7.1 Introduction
7.2 Regularization Methods
7.3 Wiener Filter
7.4 Van Cittert Filter
7.5 CLEAN Algorithm
7.6 P-Deblurring Filter
7.6.2.1 The Peak Point
7.6.2.2 The Noise Separation Frequency Point
7.6.2.3 The Cutoff Frequency Point
7.6.3 P-Deblurring Filter Design
7.6.3.1 Direct Design
7.6.3.2 Adaptive Design
7.6.3.3 Estimating Noise Energy and Noise Separation Frequency Point
7.6.3.4 The Procedure of the Adaptive Design
7.6.1 Definition of the P-Deblurring Filter
7.6.2 Properties of the P-Deblurring Filter
7.7 Image Deblurring Performance Measurements
7.7.1 Experimental Approach
7.7.1.1 Infrared Imagery Target Set
7.7.1.2 Experiment Design
7.7.1.3 Observer Training
7.7.1.4 Display Setting
7.7.2 Perception Experiment Result Analysis
7.8 Summary
References
Chapter 8 Image Contrast Enhancement
8.1 Introduction
8.2 Single-Scale Process
8.2.1 Contrast Stretching
8.2.2 Histogram Modification
8.2.3 Region-Growing Method
8.3 Multiscale Process
8.3.1 Multiresolution Analysis
8.3.2 Contrast Enhancement Based on Unsharp Masking
8.3.3 Contrast Enhancement Based on Wavelet Edges
8.3.3.1 Multiscale Edges
8.3.3.2 Multiscale Edge Detection
8.3.3.3 Wavelet Edge Modification
8.3.3.4 Output Contrast Enhancement Presentation
8.4 Contrast Enhancement Image Performance Measurements
8.4.4 Results
8.4.4.1 Results-Night Images
8.4.4.2 Results-Day Images
8.4.5 Analysis
8.4.5.1 Analysis-Night Images
8.4.5.2 Analysis-Day Images
8.4.6 Discussion
8.4.1 Background
8.4.2 Time Limited Search Model
8.4.3 Experimental Approach
8.4.3.1 Field Data Collection
8.4.3.2 Experiment Design
8.5 Summary
References
Chapter 9 Nonuniformity Correction
9.1 Detector Nonuniformity
9.2 Linear Correction and the Effects of Nonlinearity
9.2.1 Linear Correction Model
9.2.2 Effects of Nonlinearity
9.2.2.1 Residual Error
9.2.2.2 Error Due to Second Order Nonlinearity
9.2.2.3 Calibration Using the Second-Order Assumption
9.2.2.4 Other Sources of Calibration Error
9.3 Adaptive NUC
9.3.1 Temporal Processing
9.3.2 Spatio-Temporal Processing
9.4 Imaging System Performance with Fixed-Pattern Noise
9.5 Summary
References
Chapter 10 Tone Scale
10.1 Introduction
10.2 Piece-Wise Linear Tone Scale
10.3 Nonlinear Tone Scale
10.3.1 Gamma Correction
10.3.2 Look-Up Tables
10.4 Perceptual Linearization Tone Scale
10.5 Application of Tone Scale to Enhanced Visualization in Radiation Treatment
10.5.1 Portal Image in Radiation Treatment
10.5.2 Locating and Labeling the Radiation and Collimation Fields
10.5.3 Design of the Tone Scale Curves
10.5.3.1 Scaling Selectively the Input Tone Scale Curve
10.5.3.2 Adjusting the Tone Scale Curve Contrast
10.5.3.3 Determining the Speed Point
10.5.4 Contrast Enhancement
10.5.5 Producing the Output Image
10.6 Tone Scale Performance Example
10.7 Summary
References
Chapter 11 Image Fusion
11.1 Introduction
11.2 Objectives for Image Fusion
11.3 Image Fusion Algorithms
11.3.1 Superposition
11.3.2 Laplacian Pyramid
11.3.3 Ratio of a Lowpass Pyramid
11.3.4 Perceptual-Based Multiscale Decomposition
11.3.5 Discrete Wavelet Transform
11.4 Benefits of Multiple Image Modes
11.5 Image Fusion Quality Metrics
11.5.1 Mean Squared Error
11.5.2 Peak Signal-to-Noise Ratio
11.5.3 Mutual Information
11.5.4 Image Quality Index by Wang and Bovik
11.5.5 Image Fusion Quality Index by Piella and Heijmans
11.5.6 Xydeas and Petrovic Metric
11.6 Imaging System Performance with Image Fusion
11.7 Summary
References
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Signal Processing and Performance Analysis for Imaging Systems

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