Springer, 2013. — 497 p.This book is devoted to the issue of obtaining high-resolution images from single or multiple low-resolution images. You always see different algorithms for image interpolation and super-resolution without a common thread between the two processes. This book presents interpolation as a building block in the super-resolution reconstruction process. You see research papers on image interpolation either as a polynomial-based problem or an inverse problem without a comparison of the two trends. This book presents this comparison. Two chapters are devoted to two complementary steps that are used to obtain high-resolution images. These steps are image registration and image fusion. This book presents two directions for image super-resolution; super-resolution with a priori information and blind super-resolution. This book presents applications for image interpolation and super-resolution in medical and satellite image processing. MATLAB codes for most of the simulation experiments discussed in this book are included in Appendix D at the end of the book.Introduction Polynomial Image Interpolation Adaptive Polynomial Image Interpolation Neural Modeling of Polynomial Image Interpolation Color Image Interpolation Image Interpolation for Pattern Recognition Image Interpolation as Inverse Problem Image Registration Image Fusion Super-Resolution with a Priori Information Blind Super-Resolution Reconstruction of Images A: Discrete B-Splines B: Toeplitz-to-Circulant Approximations C: Newton’s Method D: MATLAB Codes
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