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.