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Book Synopsis
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 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

Advanced Digital Imaging Laboratory Using MATLABR

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A Hardback by Leonid P Yaroslavsky

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    View other formats and editions of Advanced Digital Imaging Laboratory Using MATLABR by Leonid P Yaroslavsky

    Publisher: Institute of Physics Publishing
    Publication Date: 7/10/2014 12:00:00 AM
    ISBN13: 9780750310512, 978-0750310512
    ISBN10: 0750310510
    Also in:
    Imaging systems and technology

    Description

    Book Synopsis
    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 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

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