Image super-resolution and applications

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010336551	TA1637 E47 2013	Open Access Book	Book	Searching... Unknown
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This book is devoted to the issue of image super-resolution--obtaining high-resolution images from single or multiple low-resolution images. Although there are numerous algorithms available for image interpolation and super-resolution, there's been a need for a book that establishes a common thread between the two processes. Filling this need, Image Super-Resolution and Applications presents image interpolation as a building block in the super-resolution reconstruction process.

Instead of approaching image interpolation as either a polynomial-based problem or an inverse problem, this book breaks the mold and compares and contrasts the two approaches. It presents two directions for image super-resolution: super-resolution with a priori information and blind super-resolution reconstruction of images. It also devotes chapters to the two complementary steps used to obtain high-resolution images: image registration and image fusion.

Details techniques for color image interpolation and interpolation for pattern recognition Analyzes image interpolation as an inverse problem Presents image registration methodologies Considers image fusion and its application in image super resolution Includes simulation experiments along with the required MATLAB® code

Supplying complete coverage of image-super resolution and its applications, the book illustrates applications for image interpolation and super-resolution in medical and satellite image processing. It uses MATLAB® programs to present various techniques, including polynomial image interpolation and adaptive polynomial image interpolation. MATLAB codes for most of the simulation experiments supplied in the book are included in the appendix.

Author Notes

Fathi E. Abd El-Samie, earned his BSc (Hons) in 1998, MSc in 2001, and PhD in 2005 all from Menoufia University, Menouf, Egypt. Since 2005, he has been a teaching staff member with the Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University. He is a coauthor of 160 papers published in international conference proceedings and journals.

His current research areas of interest include image enhancement, image restoration, image interpolation, super-resolution reconstruction of images, data hiding, multimedia communications, medical image processing, optical signal processing, and digital communications. Dr. Abd El-Samie was a recipient of the Most Cited Paper Award from the Digital Signal Processing journal in 2008.

Mohiy M. Hadhoud PhD , received his BSc (Hons) in 1976 and MSc in 1981 from the Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt, and his PhD from Southampton University in 1987. He joined the teaching staff of the Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt from 1981 to 2001. He is currently a professor in the Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shiben El-Kom.

Dr. Hadhoud has published more than 100 scientific papers in national and international conferences and journals. His current research areas of interest include adaptive signal and image processing techniques, image enhancement, image restoration, super-resolution reconstruction of images, data hiding and image coloring.

Said El-Khamy PhD, received his PhD from the University of Massachusetts, Amherst, in 1971. He is currently a professor emeritus, Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria, Egypt. He served as the Chairman of the Electrica

Preface	p. ix
Acknowledgments	p. xi
Authors	p. xiii
1 Introduction	p. 1
1.1 Image Interpolation	p. 2
1.2 Image Super-Resolution	p. 3
2 Polynomial Image Interpolation	p. 5
2.1 Introduction	p. 5
2.2 Classical Image Interpolation	p. 6
2.3 B-Spline Image Interpolation	p. 8
2.3.1 Polynomial Splines	p. 8
2.3.2 B-Spline Variants	p. 10
2.3.2.1 Nearest Neighbor Interpolation	p. 10
2.3.2.2 Linear Interpolation	p. 11
2.3.2.3 Cubic Spline Interpolation	p. 11
2.3.3 Digital Filter Implementation of B-Spline Interpolation	p. 12
2.4 O-MOMS Interpolation	p. 14
2.5 Keys' (Bicubic) Interpolation	p. 15
2.6 Artifacts of Polynomial Image Interpolation	p. 16
2.6.1 Ringing	p. 16
2.6.2 Aliasing	p. 16
2.6.3 Blocking	p. 17
2.6.4 Blurring	p. 17
3 Adaptive Polynomial Image Interpolation	p. 19
3.1 Introduction	p. 19
3.2 Low-Resolution Image Degradation Model	p. 20
3.3 Linear Space-Invariant Image Interpolation	p. 21
3.4 Warped-Distance Image Interpolation	p. 23
3.5 Weighted Image Interpolation	p. 23
3.6 Iterative Image Interpolation	p. 25
3.7 Simulation Examples	p. 32
4 Neural Modeling of Polynomial Image Interpolation	p. 69
4.1 Introduction	p. 69
4.2 Fundamentals of ANNs	p. 69
4.2.1 Cells	p. 70
4.2.2 Layers	p. 70
4.2.3 Arcs	p. 70
4.2.4 Weights	p. 70
4.2.5 Activation Rules	p. 70
4.2.6 Activation Functions	p. 71
4.2.6.1 Identity Function	p. 71
4.2.6.2 Step Function	p. 71
4.2.6.3 Sigmoid Function	p. 71
4.2.6.4 Piecewise-Linear Function	p. 72
4.2.6.5 Arc Tangent Function	p. 72
4.2.6.6 Hyperbolic Tangent Function	p. 72
4.2.7 Outputs	p. 72
4.2.8 Learning Rules	p. 72
4.2.8.1 Supervised Learning	p. 72
4.2.8.2 Unsupervised Learning	p. 72
4.3 Neural Network Structures	p. 73
4.3.1 Multi-Layer Perceptrons	p. 73
4.3.2 Radial Basis Function Networks	p. 73
4.3.3 Wavelet Neural Network	p. 74
4.3.4 Recurrent ANNs	p. 74
4.4 Training Algorithm	p. 74
4.5 Neural Image Interpolation	p. 77
4.6 Simulation Examples	p. 77
5 Color Image Interpolation	p. 89
5.1 Introduction	p. 89
5.2 Color Filter Arrays	p. 89
5.2.1 White Balance	p. 91
5.2.2 Bayer Interpolation	p. 91
5.3 Linear Interpolation with Laplacian Second Order Correction	p. 93
5.4 Adaptive Color Image Interpolation	p. 94
6 Image Interpolation for Pattern Recognition	p. 99
6.1 Introduction	p. 99
6.2 Cepstral Pattern Recognition	p. 102
6.3 Feature Extraction	p. 102
6.3.1 Extraction of MFCCs	p. 103
6.3.1.1 Framing and Windowing	p. 104
6.3.1.2 Discrete Fourier Transform	p. 104
6.3.1.3 Mel Filter Bank	p. 104
6.3.1.4 Discrete Cosine Transform	p. 105
6.3.2 Polynomial Coefficients	p. 105
6.4 Feature Extraction from Discrete Transforms	p. 106
6.4.1 Discrete Wavelet Transform	p. 106
6.4.2 Discrete Cosine Transform	p. 109
6.4.3 Discrete Sine Transform	p. 110
6.5 Feature Matching Using ANNs	p. 110
6.6 Simulation Examples	p. 110
7 Image Interpolation as Inverse Problem	p. 161
7.1 Introduction	p. 161
7.2 Adaptive Least-Squares Image Interpolation	p. 162
7.3 LMMSE Image Interpolation	p. 164
7.4 Maximum Entropy Image Interpolation	p. 166
7.5 Regularized Image Interpolation	p. 168
7.6 Simulation Examples	p. 170
7.7 Interpolation of Infrared Images	p. 178
8 Image Registration	p. 195
8.1 Introduction	p. 195
8.2 Applications of Image Registration	p. 196
8.2.1 Different Viewpoints (Multi-View Analysis)	p. 196
8.2.2 Different Times (Multi-Temporal Analysis)	p. 196
8.2.3 Different Sensors (Multi-Modal Analysis)	p. 196
8.2.4 Scene-to-Model Registration	p. 197
8.3 Steps of Image Registration	p. 197
8.3.1 Feature Detection Step	p. 198
8.3.2 Feature Matching Step	p. 198
8.3.2.1 Area-Based Methods	p. 198
8.3.2.2 Feature-Based Methods	p. 200
8.3.3 Transform Model Estimation	p. 201
8.3.3.1 Global Mapping Models	p. 202
8.3.3.2 Local Mapping Models	p. 203
8.3.4 Image Resampling and Transformation	p. 203
8.4 Evaluation of Image Registration Accuracy	p. 203
9 Image Fusion	p. 205
9.1 Introduction	p. 205
9.2 Objectives of Image Fusion	p. 206
9.3 Implementation of Image Fusion	p. 207
9.4 Pixel Level Image Fusion	p. 209
9.5 Principal Component Analysis Fusion	p. 210
9.6 Wavelet Fusion	p. 211
9.6.1 DWT Fusion	p. 211
9.6.2 DWFT Fusion	p. 212
9.7 Curvelet Fusion	p. 214
9.7.1 Sub-Band Filtering	p. 216
9.7.2 Tiling	p. 216
9.7.3 Ridgelet Transform	p. 216
9.8 IHS Fusion	p. 219
9.9 High-Pass Filter Fusion	p. 221
9.10 Gram-Schmidt Fusion	p. 222
9.11 Fusion of Satellite Images	p. 223
9.12 Fusion of MR and CT Images	p. 255
10 Super-Resolution with a Priori Information	p. 267
10.1 Introduction	p. 267
10.2 Multiple Observation LR Degradation Model	p. 268
10.3 Wavelet-Based Image Super-Resolution	p. 270
10.4 Simplified Multi-Channel Degradation Model	p. 271
10.5 Multi-Channel Image Restoration	p. 272
10.5.1 Multi-Channel LMMSE Restoration	p. 272
10.5.2 Multi-Channel Maximum Entropy Restoration	p. 275
10.5.3 Multi-Channel Regularized Restoration	p. 276
10.6 Simulation Examples	p. 280
11 Blind Super-Resolution Reconstruction of Images	p. 293
11.1 Introduction	p. 293
11.2 Problem Formulation	p. 294
11.3 Two-Dimensional GCD Algorithm	p. 295
11.4 Blind Super-Resolution Reconstruction Approach	p. 296
11.5 Simulation Examples	p. 299
Appendix A Discrete B-Splines	p. 305
Appendix B Toeplitz-to-Circulant Approximations	p. 307
Appendix C Newton's Method	p. 311
Appendix D MATLAB® Codes	p. 315
References	p. 473
Index	p. 481

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Author Notes

Table of Contents