Advanced digital imaging laboratory using MATLAB® /

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 ob...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: I͡Aroslavskiĭ, L. P. (Leonid Pinkhusovich) (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, [2014]
Colección:IOP (Series). Release 1.
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
  • Author biography
  • Introduction
  • General remarks about the book
  • Instructions for readers
  • Image digitization
  • Introduction
  • Image discretization
  • Signal scalar quantization
  • Image compression
  • Digital image formation and computational imaging
  • Introduction
  • Image recovery from sparse irregularly sampled data. Recovery of images with occlusions
  • Numerical reconstruction of holograms
  • Image reconstruction from projections
  • Questions for self-testing
  • Image resampling and building continuous image models
  • Introduction
  • Signal/image subsampling through fractional shifts
  • Image resampling using 'continuous' image models
  • The three-step rotation algorithm
  • Comparison of image resampling methods
  • Comparison of signal numerical differentiation and integration methods
  • Questions for self-testing
  • Image and noise statistical characterization and diagnostics
  • Introduction
  • Image histograms
  • Image local moments and order statistics
  • Pixel attributes and neighborhoods
  • Image autocorrelation functions and power spectra
  • Image noise
  • Empirical diagnostics of image noise
  • Questions for self-testing
  • Statistical image models and pattern formation
  • Introduction
  • PWN models
  • LF models
  • PWN&LF and LF&PWN models
  • Evolutionary models
  • Questions for self-testing
  • Image correlators for detection and localization of objects
  • Introduction
  • Localization of a target on images contaminated with additive uncorrelated Gaussian noise. Normal and anomalous localization errors
  • 'Matched filter' correlator versus signal-to-clutter ratio optimal correlator and local versus global signal-to-clutter ratio optimal correlators
  • Object localization and image edges
  • Questions for self-testing
  • Methods of image perfecting
  • Introduction
  • Correcting imaging system transfer functions
  • Filtering periodical interferences. Filtering 'banding' noise
  • 'Ideal' and empirical Wiener filtering for image denoising and deblurring
  • Local adaptive filtering for image denoising
  • Filtering impulsive noise using linear filters
  • Image denoising using nonlinear (rank) filters
  • Questions for self-testing
  • Methods of image enhancement
  • Introduction
  • Contrast enhancement
  • Edge extraction. Max-Min and Size-EV methods
  • Questions for self-testing.

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