Super-Resolution Imaging

This book functions as the definitive overview of the field of super-resolution imaging. Written by the leading researchers in the field of image and video super-resolution, it surveys state-of-the-art techniques, and each detailed chapter covers the implementations and applications of super-resolut...

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Bibliographische Detailangaben
1. Verfasser: Milanfar, Peyman
Format: E-Book Buch
Sprache:Englisch
Veröffentlicht: United States CRC Press 2017
Taylor & Francis Group
Ausgabe:1
Schriftenreihe:Digital imaging and computer vision
Schlagworte:
Computer programming, programs, data
Digital techniques
High resolution imaging
Image processing
Image processing > Digital techniques
LR Image
NLM
Sr Result
Nonlocal Means
Image Prior
Super-resolution Algorithms
Registration Parameters
Observation Model
SR Method
Blind Source Separation
Low Resolution Images
Psf
Bicubic Interpolation
HSV Color Space
Hr Image
Pixel Selection
Low Resolution Observations
Decimation Operators
Motion Estimation
Motion Vectors
SR Reconstruction
Hyper-spectral Images
SDOCT Image
PSNR Value
FFT Spectrum
ISBN:9781439819319, 1439819319, 1439819300, 9781439819302
Online-Zugang:Volltext
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Inhaltsangabe:
  • Front cover -- Contents -- Preface -- Chapter 1: Image Super-Resolution: Historical Overviewand Future Challenges -- Chapter 2: Super-Resolution Using Adaptive Wiener Filters -- Chapter 3: Locally Adaptive Kernel Regression for Space-Time Super-Resolution -- Chapter 4: Super-Resolution with Probabilistic Motion Estimation -- Chapter 5: Spatially Adaptive Filtering asRegularization in Inverse Imaging:Compressive Sensing, Super-Resolution, and Upsampling -- Chapter 6: Registration for Super-Resolution: Theory,Algorithms, and Applications in Image and Mobile Video Enhancement -- Chapter 7: Towards Super-Resolution in the Presence of Spatially Varying Blur -- Chapter 8: Toward Robust Reconstruction-Based Super-Resolution -- Chapter 9: Multiframe Super-Resolution from a Bayesian Perspective -- Chapter 10: Variational Bayesian Super-Resolution Reconstruction -- Chapter 11: Pattern Recognition Techniques for Image Super-Resolution -- Chapter 12: Super-Resolution Reconstruction of Multichannel Images -- Chapter 13: New Applications of Super-Resolution in Medical Imaging -- Chapter 14: Practicing Super-Resolution: What Have We Learned? -- Index -- Back cover