Signal Processing and Performance Analysis for Imaging Systems

Signal Processing and Performance Analysis for Imaging Systems......Page 2
Contents......Page 8
Preface......Page 14
Part I: Basic Principles of Imaging Systemsand Performance......Page 18
1.2 Imaging Performance......Page 20
1.4 Image Resampling......Page 21
1.5 Super-Resolution Image Reconstruction......Page 22
1.6 Image Restoration—Deblurring......Page 23
1.8 Nonuniformity Correction (NUC)......Page 24
1.10 Image Fusion......Page 25
References......Page 27
2.1 Basic Imaging Systems......Page 28
2.2 Resolution and Sensitivity......Page 32
2.3 Linear Shift-Invariant (LSI) Imaging Systems......Page 33
2.4 Imaging System Point Spread Function and Modulation Transfer Function......Page 37
2.4.1 Optical Filtering......Page 38
2.4.2 Detector Spatial Filters......Page 39
2.4.3 Electronics Filtering......Page 41
2.4.4 Display Filtering......Page 42
2.4.5 Human Eye......Page 43
2.4.6 Overall Image Transfer......Page 44
2.5 Sampled Imaging Systems......Page 45
2.6 Signal-to-Noise Ratio......Page 51
2.7 Electro-Optical and Infrared Imaging Systems......Page 55
References......Page 56
3.2 A Brief History of Target Acquisition Theory......Page 58
3.3.1 Threshold Vision of the Unaided Eye......Page 60
3.3.2 Threshold Vision of the Aided Eye......Page 64
3.4 Image Quality Metric......Page 67
3.5 Example......Page 70
References......Page 78
Part II: Basic Principles of Signal Processing......Page 80
4.2.1.1 Fourier Integral......Page 82
4.2.1.2 Properties of Fourier Transform......Page 83
4.2.2.1 Two-Dimensional Continuous Fourier Transform......Page 95
4.2.2.3 Polar Representation of Fourier Transform......Page 97
4.2.2.4 Two-Dimensional Discrete Fourier Transform and Sampling......Page 99
4.3.1 Definition of Nonrecursive and Recursive Filters......Page 100
4.3.2 Implementation of FIR Filters......Page 101
4.3.3 Shortcomings of FIR Filters......Page 102
4.4 Fourier-Based Filters......Page 103
4.4.2 Radially Symmetric Filter with a Hamming Window at a Transition Point......Page 104
4.4.3 Radially Symmetric Filter with a Butterworth Window at a Transition Point......Page 105
4.4.4 Radially Symmetric Filter with a Power Window......Page 106
4.5 The Wavelet Transform......Page 107
4.5.1.1 Window Fourier Transform......Page 108
4.5.1.2 Wavelet Transform......Page 110
4.5.2 Dyadic and Discrete Wavelet Transform......Page 113
4.5.4 Forward and Inverse Wavelet Transform......Page 114
4.5.6 Multiscale Edge Detection......Page 115
References......Page 119
Part III: Advanced Applications......Page 122
5.2 Image Display, Reconstruction, and Resampling......Page 124
5.3.1 Sampling Theory......Page 126
5.3.2 Sampling Artifacts......Page 127
5.4.1 Image Resampling Model......Page 128
5.4.3 Resampling Filters......Page 129
5.5.1 Image Resampling Model......Page 131
5.5.2.1 Output Requirements......Page 132
5.5.2.2 Computational Efficiency......Page 133
5.5.3 Resampling System Design......Page 134
5.5.4 Resampling Filters......Page 135
5.5.5.1 Resampling 2-D Delta Test Pattern......Page 136
5.5.5.2 Resampling 2-D Chirp Test Pattern......Page 137
5.5.5.3 Ripple Property......Page 139
5.6 Image Resampling Performance Measurements......Page 142
References......Page 144
6.1.2 Super-Resolution for Diffraction and Sampling......Page 146
6.2 Super-Resolution Image Restoration......Page 147
6.3.1 Background......Page 148
6.3.3 Image Acquisition—Microdither Scanner Versus Natural Jitter......Page 149
6.3.4.1 Signal Registration......Page 150
6.3.4.3 Resampling Via Intensity Domain Interpolation......Page 151
6.3.5 Motion Estimation......Page 152
6.3.5.1 Gradient-Based Method......Page 153
6.3.5.2 Optical Flow Method......Page 155
6.3.5.3 Correlation Method......Page 159
6.3.6 High-Resolution Output Image Reconstruction......Page 160
6.3.6.2 Factors Limiting the Resolution Recovery......Page 162
6.3.6.3 Nonuniform Interpolation Method......Page 164
6.3.6.4 Regularized Inverse Method......Page 165
6.3.6.5 Error-Energy Reduction Method......Page 166
6.3.6.6 Examples......Page 169
6.3.6.7 Practical Considerations......Page 174
6.4.1 Background......Page 175
6.4.2.1 Target—Triangle Orientation Discrimination (TOD)......Page 176
6.4.2.2 Field Data Collection......Page 177
6.4.2.3 Sensor Description......Page 178
6.4.2.4 Experiment Design......Page 181
6.4.3 Measurement Results......Page 183
6.5 Sensors That Benefit from Super-Resolution Reconstruction......Page 184
6.5.1 Example and Performance Estimates......Page 185
6.6 Performance Modeling and Prediction of Super-ResolutionReconstruction......Page 189
6.7 Summary......Page 190
References......Page 191
7.1 Introduction......Page 196
7.3 Wiener Filter......Page 198
7.4 Van Cittert Filter......Page 199
7.5 CLEAN Algorithm......Page 200
7.6 P-Deblurring Filter......Page 201
7.6.2 Properties of the P-Deblurring Filter......Page 203
7.6.3.1 Direct Design......Page 205
7.6.3.2 Adaptive Design......Page 209
7.6.3.3 Estimating Noise Energy and Noise Separation Frequency Point......Page 210
7.6.3.4 The Procedure of the Adaptive Design......Page 212
7.6.1 Definition of the P-Deblurring Filter......Page 202
7.7 Image Deblurring Performance Measurements......Page 216
7.7.1.2 Experiment Design......Page 217
7.7.1.4 Display Setting......Page 219
7.7.2 Perception Experiment Result Analysis......Page 220
References......Page 221
8.1 Introduction......Page 224
8.2.1 Contrast Stretching......Page 225
8.3 Multiscale Process......Page 226
8.3.2 Contrast Enhancement Based on Unsharp Masking......Page 227
8.3.3.1 Multiscale Edges......Page 228
8.3.3.3 Wavelet Edge Modification......Page 230
8.3.3.4 Output Contrast Enhancement Presentation......Page 233
8.4.1 Background......Page 234
8.4.4.1 Results—Night Images......Page 239
8.4.5 Analysis......Page 240
8.4.5.2 Analysis—Day Images......Page 242
8.4.6 Discussion......Page 243
8.4.2 Time Limited Search Model......Page 235
8.4.3.1 Field Data Collection......Page 236
8.4.3.2 Experiment Design......Page 237
8.5 Summary......Page 244
References......Page 245
9.1 Detector Nonuniformity......Page 248
9.2 Linear Correction and the Effects of Nonlinearity......Page 249
9.2.2.1 Residual Error......Page 250
9.2.2.2 Error Due to Second Order Nonlinearity......Page 251
9.2.2.4 Other Sources of Calibration Error......Page 254
9.3.1 Temporal Processing......Page 255
9.3.2 Spatio-Temporal Processing......Page 257
9.4 Imaging System Performance with Fixed-Pattern Noise......Page 260
9.5 Summary......Page 261
References......Page 262
10.1 Introduction......Page 264
10.2 Piece-Wise Linear Tone Scale......Page 265
10.3.1 Gamma Correction......Page 267
10.4 Perceptual Linearization Tone Scale......Page 269
10.5.1 Portal Image in Radiation Treatment......Page 272
10.5.3 Design of the Tone Scale Curves......Page 274
10.5.3.1 Scaling Selectively the Input Tone Scale Curve......Page 276
10.5.3.2 Adjusting the Tone Scale Curve Contrast......Page 277
10.5.3.3 Determining the Speed Point......Page 278
10.5.4 Contrast Enhancement......Page 279
10.6 Tone Scale Performance Example......Page 281
10.7 Summary......Page 283
References......Page 284
11.1 Introduction......Page 286
11.2 Objectives for Image Fusion......Page 287
11.3 Image Fusion Algorithms......Page 288
11.3.2 Laplacian Pyramid......Page 289
11.3.3 Ratio of a Lowpass Pyramid......Page 292
11.3.4 Perceptual-Based Multiscale Decomposition......Page 293
11.3.5 Discrete Wavelet Transform......Page 295
11.4 Benefits of Multiple Image Modes......Page 297
11.5 Image Fusion Quality Metrics......Page 298
11.5.1 Mean Squared Error......Page 299
11.5.4 Image Quality Index by Wang and Bovik......Page 300
11.5.5 Image Fusion Quality Index by Piella and Heijmans......Page 301
11.5.6 Xydeas and Petrovic Metric......Page 302
11.6 Imaging System Performance with Image Fusion......Page 303
References......Page 307
About the Authors......Page 310
Index......Page 312