IOP Publishing Advanced Digital Imaging Laboratory Using MATLAB(R) (IOP Expanding Physics)

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. Product Description This is an unusual book. It is a book of exercises, exercises in digital imaging engineering, one of the most important and rapidly developing branches of modern information technology. Studying digital imaging engineering, mastering this profession and working in the area is not possible without obtaining practical skills based on fundamental knowledge in the subject. The current book is aimed at providing technical support for this. It contains exercises on all major topics of digital imaging for students, researchers in experimental sciences and, generally, all practitioners in imaging engineering. 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(R) 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