Advanced digital imaging laboratory using MATLAB(R) / by Leonid P. Yaroslavsky.Material type: TextSeries: IOP (Series). Release 3. | IOP expanding physicsPublisher: Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, Edition: Second editionDescription: 1 online resource (various pagings) : color illustrationsContent type: text Media type: electronic Carrier type: online resourceISBN: 9780750312332; 9780750312356Subject(s): MATLAB | Image processing -- Digital techniques | Three-dimensional imaging | Numerical analysis -- Computer programs | Image processing | COMPUTERS / Image ProcessingAdditional physical formats: Print version:: No titleDDC classification: 006.6 LOC classification: TA1637 | .I273 2016ebOnline resources: Click here to access online Also available in print.
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Revised edition of : Advanced digital imaging laboratory using MATLAB(R). 2014.
"Version: 20160901"--Title page verso.
Includes bibliographical references.
Preface to the second edition -- Preface -- 1. Introduction -- 1.1. General remarks about the book -- 1.2. Instructions for readers
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.
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(R) exercises, this book delivers a complete applied course in digital imaging theory and practice.
Graduate students and practitioners in image processing and engineering.
Also available in print.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
Leonid P. Yaroslavsky MS (summa cum laude, 1961), PhD (1968), Dr Sc. Habilitatus in physics and mathematics (1982), OSA Fellow. From 1965-1983, the head of the Digital Image Processing and Digital Holography Group at the Institute for Information Transmission Problems (IITP), Russian Academy of Sciences. From 1983 until 1995, the founder and head of the Laboratory of Digital Optics at the IITP. 1995-2008, a full professor at the Faculty of Engineering, Tel Aviv University. He is currently a professor emeritus. A visiting professor at the University of Erlangen, Germany; National Institute of Health, Bethesda, MD, USA; Institute of Optics, Orsay, France; Institute Henri Poincaré, Paris, France; International Centre for Signal Processing, Tampere University of Technology, Finland; Agilent Laboratories, Palo Alto, CA, USA; Gunma University, Kiryu, Japan; Autonomous University of Barcelona, Spain. He has supervised 20 PhD candidates. Professor Yaroslavsky is author and editor of more than 20 books and over 100 peer-reviewed publications on digital image processing and digital holography.
Title from PDF title page (viewed on October 10, 2016).