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.
Table of Contents
Introduction. Polynomial Image Interpolation. Adaptive Polynomial Image Interpolation. A Neural Modeling Method for Polynomial Image Interpolation. Color Image Interpolation. Image Interpolation for Pattern Recognition. Image Interpolation as an Inverse Problem. Image Registration Methodologies. Image Fusion and Its Application in Image Super Resolution. Image Super Resolution with A Priori Information. Blind Image Super Resolution.
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