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Advanced digital imaging laboratory using MATLAB{reg}
Title statement Advanced digital imaging laboratory using MATLAB{reg} / by Leonid P. Yaroslavsky. [elektronický zdroj] Edition statement Second edition. Publication Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, [2016] Phys.des. 1 online resource (various pagings) : color illustrations. ISBN 9780750312332 (online) 9780750312356 mobi Edition [IOP release 3] IOP expanding physics, ISSN 2053-2563 Note "Version: 20160901"--Title page verso. Revised edition of : Advanced digital imaging laboratory using MATLAB{reg}. 2014. Internal Bibliographies/Indexes Note Includes bibliographical references. Contents Preface to the second edition -- Preface -- 1. Introduction -- 1.1. General remarks about the book -- 1.2. Instructions for readers Content note 2. Image digitization -- 2.1. Introduction -- 2.2. Image discretization -- 2.3. Signal scalar quantization -- 2.4. Image compression. 3. Digital image formation and computational imaging -- 3.1. Introduction -- 3.2. Image recovery from sparse irregularly sampled data. Recovery of images with occlusions -- 3.3. Numerical reconstruction of holograms -- 3.4. Image reconstruction from projections. 4. Image resampling and building continuous image models -- 4.1. Introduction -- 4.2. Signal/image sub-sampling through fractional shifts -- 4.3. Comparison of DFT-based and DCT-based discrete sinc interpolations -- 4.4. Image resampling using 'continuous' image models -- 4.5. Three step image rotation algorithm -- 4.6. Comparison of image resampling methods -- 4.7. Comparison of signal numerical differentiation and integration methods. 5. Image and noise statistical characterization and diagnostics -- 5.1. Introduction -- 5.2. Image histograms -- 5.3. Image local moments and order statistics -- 5.4. Pixel attributes and neighborhoods -- 5.5. Image autocorrelation functions and power spectra -- 5.6. Image noise -- 5.7. Empirical diagnostics of image noise. 6. Statistical image models and pattern formation -- 6.1. Introduction -- 6.2. PWN models -- 6.3. LF models -- 6.4. PWN&LF and LF&PWN models -- 6.5. Evolutionary models. 7. Image correlators for detection and localization of objects -- 7.1. Introduction -- 7.2. Localization of a target on images contaminated with additive uncorrelated Gaussian noise. Normal and anomalous localization errors -- 7.3. Normal and anomalous localization errors -- 7.4. Matched filter correlator versus signal-to-clutter ratio-optimal correlator. Local versus global signal-to-clutter ratio-optimal correlators -- 7.5. Object localization and image edges. 8. Methods of image perfecting -- 8.1. Introduction -- 8.2. Correcting imaging system transfer functions -- 8.3. Filtering periodical interferences. Filtering 'banding' noise -- 8.4. Filtering 'banding' noise -- 8.5. 'Ideal' and empirical Wiener filtering for image denoising and deblurring -- 8.6. Local adaptive filtering for image denoising : achromatic images -- 8.7. Local adaptive filtering for image denoising : color images -- 8.8. Filtering impulsive noise using linear filters -- 8.9. Image denoising using nonlinear (rank) filters. 9. Methods of image enhancement -- 9.1. Introduction -- 9.2. Enhancement of achromatic images -- 9.3. Enhancement of color images. Notes to Availability Přístup pouze pro oprávněné uživatele Audience Graduate students and practitioners in image processing and engineering. Note Způsob přístupu: World Wide Web.. Požadavky na systém: Adobe Acrobat Reader. Another responsib. Institute of Physics (Great Britain), Subj. Headings MATLAB. Subj. Headings Image processing - Digital techniques. * Three-dimensional imaging. * Numerical analysis - Computer programs. * Image processing. * COMPUTERS / Image Processing. Form, Genre elektronické knihy electronic books Country Anglie Language angličtina Document kind Electronic books URL Plný text pro studenty a zaměstnance UPOL 
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The first edition of this text book focussed on providing practical hands-on experience in digital imaging techniques for graduate students and practitioners keeping to a minimum any detailed discussion on the underlying theory. In this new extended edition, the author builds on the strength of the original edition by expanding the coverage to include formulation of the major theoretical results that underlie the exercises as well as introducing numerous modern concepts and new techniques. Whether you are studying or already using digital imaging techniques, developing proficiency in the subject is not possible without mastering practical skills. Including more than 100 MATLAB{reg} exercises, this book delivers a complete applied course in digital imaging theory and practice.
Preface to the second edition -- Preface -- 1. Introduction -- 1.1. General remarks about the book -- 1.2. Instructions for readers2. Image digitization -- 2.1. Introduction -- 2.2. Image discretization -- 2.3. Signal scalar quantization -- 2.4. Image compression3. Digital image formation and computational imaging -- 3.1. Introduction -- 3.2. Image recovery from sparse irregularly sampled data. Recovery of images with occlusions -- 3.3. Numerical reconstruction of holograms -- 3.4. Image reconstruction from projections4. Image resampling and building continuous image models -- 4.1. Introduction -- 4.2. Signal/image sub-sampling through fractional shifts -- 4.3. Comparison of DFT-based and DCT-based discrete sinc interpolations -- 4.4. Image resampling using 'continuous' image models -- 4.5. Three step image rotation algorithm -- 4.6. Comparison of image resampling methods -- 4.7. Comparison of signal numerical differentiation and integration methods5. Image and noise statistical characterization and diagnostics -- 5.1. Introduction -- 5.2. Image histograms -- 5.3. Image local moments and order statistics -- 5.4. Pixel attributes and neighborhoods -- 5.5. Image autocorrelation functions and power spectra -- 5.6. Image noise -- 5.7. Empirical diagnostics of image noise6. Statistical image models and pattern formation -- 6.1. Introduction -- 6.2. PWN models -- 6.3. LF models -- 6.4. PWN&LF and LF&PWN models -- 6.5. Evolutionary models7. Image correlators for detection and localization of objects -- 7.1. Introduction -- 7.2. Localization of a target on images contaminated with additive uncorrelated Gaussian noise. Normal and anomalous localization errors -- 7.3. Normal and anomalous localization errors -- 7.4. Matched filter correlator versus signal-to-clutter ratio-optimal correlator. Local versus global signal-to-clutter ratio-optimal correlators -- 7.5. Object localization and image edges8. Methods of image perfecting -- 8.1. Introduction -- 8.2. Correcting imaging system transfer functions -- 8.3. Filtering periodical interferences. Filtering 'banding' noise -- 8.4. Filtering 'banding' noise -- 8.5. 'Ideal' and empirical Wiener filtering for image denoising and deblurring -- 8.6. Local adaptive filtering for image denoising : achromatic images -- 8.7. Local adaptive filtering for image denoising : color images -- 8.8. Filtering impulsive noise using linear filters -- 8.9. Image denoising using nonlinear (rank) filters9. Methods of image enhancement -- 9.1. Introduction -- 9.2. Enhancement of achromatic images -- 9.3. Enhancement of color images.
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