Advanced digital imaging laboratory using MATLAB(R) /

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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: IA͡roslavskiĭ, L. P. (Leonid Pinkhusovich) (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, [2016]
Edición:Second edition.
Colección:IOP (Series). Release 3.
IOP expanding physics.
Temas:
MATLAB.
Image processing > Digital techniques.
Three-dimensional imaging.
Numerical analysis > Computer programs.
Image processing.
COMPUTERS / Image Processing.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.

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