
Signal Processing and Performance Analysis for Imaging Systems
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Content
- 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
- About the Authors
- Index
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