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    Image Super-Resolution and Applications

    ISBN-10: 1466557966
    ISBN-13: 9781466557963
    Author(s): Fathi E. Abd El-Samie
    Description: Image super resolution is the process by which a single HR image is obtained from multiple degraded LR images. Image super resolution can be carried out with or without a priori information. The problem of super resolution reconstruction of images  More...
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    Publisher: CRC Press LLC
    Binding: Hardcover
    Pages: 502
    Size: 6.50" wide x 9.00" long x 1.00" tall
    Weight: 1.848
    Language: English

    Image super resolution is the process by which a single HR image is obtained from multiple degraded LR images. Image super resolution can be carried out with or without a priori information. The problem of super resolution reconstruction of images can be solved in successive steps: image registration, multi-channel image restoration, image fusion, and, finally, image interpolation. This book provides complete coverage of image super resolution and its applications. With MATLAB® programs, the text presents various techniques polynomial image interpolation, adaptive polynomial image interpolation, and color image interpolation. It also analyzes image interpolation for pattern recognition.

    Preface
    Acknowledgments
    Authors
    Introduction
    Image Interpolation
    Image Super-Resolution
    Polynomial Image Interpolation
    Introduction
    Classical Image Interpolation
    B-Spline Image Interpolation
    Polynomial Splines
    B-Spline Variants
    Nearest Neighbor Interpolation
    Linear Interpolation
    Cubic Spline Interpolation
    Digital Filter Implementation of B-Spline Interpolation
    O-MOMS Interpolation
    Keys' (Bicubic) Interpolation
    Artifacts of Polynomial Image Interpolation
    Ringing
    Aliasing
    Blocking
    Blurring
    Adaptive Polynomial Image Interpolation
    Introduction
    Low-Resolution Image Degradation Model
    Linear Space-Invariant Image Interpolation
    Warped-Distance Image Interpolation
    Weighted Image Interpolation
    Iterative Image Interpolation
    Simulation Examples
    Neural Modeling of Polynomial Image Interpolation
    Introduction
    Fundamentals of ANNs
    Cells
    Layers
    Arcs
    Weights
    Activation Rules
    Activation Functions
    Identity Function
    Step Function
    Sigmoid Function
    Piecewise-Linear Function
    Arc Tangent Function
    Hyperbolic Tangent Function
    Outputs
    Learning Rules
    Supervised Learning
    Unsupervised Learning
    Neural Network Structures
    Multi-Layer Perceptrons
    Radial Basis Function Networks
    Wavelet Neural Network
    Recurrent ANNs
    Training Algorithm
    Neural Image Interpolation
    Simulation Examples
    Color Image Interpolation
    Introduction
    Color Filter Arrays
    White Balance
    Bayer Interpolation
    Linear Interpolation with Laplacian Second Order Correction
    Adaptive Color Image Interpolation
    Image Interpolation for Pattern Recognition
    Introduction
    Cepstral Pattern Recognition
    Feature Extraction
    Extraction of MFCCs
    Framing and Windowing
    Discrete Fourier Transform
    Mel Filter Bank
    Discrete Cosine Transform
    Polynomial Coefficients
    Feature Extraction from Discrete Transforms
    Discrete Wavelet Transform
    Discrete Cosine Transform
    Discrete Sine Transform
    Feature Matching Using ANNs
    Simulation Examples
    Image Interpolation as Inverse Problem
    Introduction
    Adaptive Least-Squares Image Interpolation
    LMMSE Image Interpolation
    Maximum Entropy Image Interpolation
    Regularized Image Interpolation
    Simulation Examples
    Interpolation of Infrared Images
    Image Registration
    Introduction
    Applications of Image Registration
    Different Viewpoints (Multi-View Analysis)
    Different Times (Multi-Temporal Analysis)
    Different Sensors (Multi-Modal Analysis)
    Scene-to-Model Registration
    Steps of Image Registration
    Feature Detection Step
    Feature Matching Step
    Area-Based Methods
    Feature-Based Methods
    Transform Model Estimation
    Global Mapping Models
    Local Mapping Models
    Image Resampling and Transformation
    Evaluation of Image Registration Accuracy
    Image Fusion
    Introduction
    Objectives of Image Fusion
    Implementation of Image Fusion
    Pixel Level Image Fusion
    Principal Component Analysis Fusion
    Wavelet Fusion
    DWT Fusion
    DWFT Fusion
    Curvelet Fusion
    Sub-Band Filtering
    Tiling
    Ridgelet Transform
    IHS Fusion
    High-Pass Filter Fusion
    Gram-Schmidt Fusion
    Fusion of Satellite Images
    Fusion of MR and CT Images
    Super-Resolution with a Priori Information
    Introduction
    Multiple Observation LR Degradation Model
    Wavelet-Based Image Super-Resolution
    Simplified Multi-Channel Degradation Model
    Multi-Channel Image Restoration
    Multi-Channel LMMSE Restoration
    Multi-Channel Maximum Entropy Restoration
    Multi-Channel Regularized Restoration
    Simulation Examples
    Blind Super-Resolution Reconstruction of Images
    Introduction
    Problem Formulation
    Two-Dimensional GCD Algorithm
    Blind Super-Resolution Reconstruction Approach
    Simulation Examples
    Discrete B-Splines
    Toeplitz-to-Circulant Approximations
    Newton's Method
    MATLAB� Codes
    References
    Index

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