Advanced Digital Imaging Laboratory Using MATLAB(R) (IOP Expanding Physics) by Leonid P Yaroslavsky (PDF)

Description

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(R) 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.

User’s Reviews

Editorial Reviews: From the Inside Flap Whether you are studying or already using digital imaging techniques, developing proficiency in the subject is not possible without mastering practical skills. 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 outlying principles of each topic while offering more than 80 MATLAB(R) 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. Key features include: Supportsstudying of all aspects of digital imaging from image signal digitization to image parameter estimation, recovery, restoration and enhancement.MATLAB® source codes for exercises are provided, which readers can modify for their particular needs and tastes, to design new exercises and, in addition, to use them for solving particular image-processing tasks.Test signals and images provided in the book, as well as methodology of the experiments, will be useful for readers in their further studies and practical work.Exercises are supported by outlines of the corresponding theory. The book offers a unique combination of exercises, supportive software and data set that can be used not only for studying the subject, but in further practical work. From the Back Cover Whether you are studying or already using digital imaging techniques, developing proficiency in the subject is not possible without mastering practical skills. 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 outlying principles of each topic while offering more than 80 MATLAB(R) 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. Key features include: Supports studying of all aspects of digital imaging from image signal digitization to image parameter estimation, recovery, restoration and enhancement. MATLAB® source codes for exercises are provided, which readers can modify for their particular needs and tastes, to design new exercises and, in addition, to use them for solving particular image-processing tasks. Test signals and images provided in the book, as well as methodology of the experiments, will be useful for readers in their further studies and practical work. Exercises are supported by outlines of the corresponding theory. The book offers a unique combination of exercises, supportive software and data set that can be used not only for studying the subject, but in further practical work. About the Author Leonid P. Yaroslavsky, MS, (Summa cum laude, 1961), Ph.D. (1968), Dr. Sc. Habilitatus.in Phys.&Math, (1982), OSA Fellow. From 1965 till 1983, the head of a group of Digital Image Processing and Digital Holography at the Institute for Information Transmission Problems (IITP), Russian Academy of Sciences. From 1983 till 1995, the founder and head of the Laboratory of Digital Optics at the IITP. From 1995 till 2008, a full Professor at the Faculty of Engineering, Tel Aviv University. Presently a Professor Emeritus. A visiting Professor at University of Erlangen, Germany; National Institute of Health, Bethesda, MD, USA; Institute of Optics, Orsay, France; Institute Henri Poincare, Paris, France; International Centre For Signal Processing, Tampere University of Technology, Tampere, Finland; Agilent Laboratories, Palo Alto, Ca, USA; Gunma University, Kiryu, Japan; Autonomous University of Barcelona. Supervised 20 Ph. D. candidates. An author and editor of more than 20 books and numerous peer reviewed publications on digital image processing and digital holography. Read more

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