In this book, Prof. Yaroslavsky delivers a complete applied course in digital imaging aimed at advanced students and practitioners. Covering all areas of digital imaging, the text provides an outline of underlying principles of each topic before offering more than 80 MATLAB® based exercises. Subjects addressed embrace image digitization (discretization, quantization, compression), digital image formation and computational imaging, image resampling and building continuous image models, image and noise statistical characterization and diagnostics, statistical image models and pattern formation, image correlators for localization of objects, methods of image perfecting (denoising, deblurring), and methods of image enhancement.
Autorentext
Leonid P Yaroslavsky is a professor emeritus at Tel Aviv University. A fellow of the Optical Society of America, Prof. Yaroslavsky has authored more than 100 papers on digital image processing and digital holography.
Inhalt
1 Introduction
2 Image digitization
3 Digital image formation and computational imaging
4 Image resampling and building continuous image models
5 Image and noise statistical characterization and diagnostics
6 Statistical image models and pattern formation
7 Image correlators for detection and localization of objects
8 Methods of image perfecting
9 Methods of image enhancement