Advanced digital imaging laboratory using MATLAB / Leonid P. Yaroslavsky, Professor Emeritus, School of Electrical Engineering, Tel Aviv University and Tel Aviv, Israel

"Version: 20160901"--Title page verso

Revised edition of : Advanced digital imaging laboratory using MATLAB. 2014

Machine generated contents note: 1.Introduction -- 1.1.General remarks about the book -- 1.2.Instructions for readers -- 2.Image digitization -- 2.1.Introduction -- 2.2.Image discretization -- 2.2.1.Signal discretization as its expansion over a set of basis functions -- 2.2.2.Image sampling -- Questions for self-testing -- 2.3.Signal scalar quantization -- 2.3.1.Introduction -- 2.3.2.Quantization of achromatic images -- 2.3.3.Quantization of color images -- 2.3.4.Quantization of stereoscopic images -- Questions for self-testing -- 2.4.Image compression -- 2.4.1.Introduction -- Questions for self-testing -- 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 -- 3.3.1.Introduction -- 3.3.2.Reconstruction of a simulated Fresnel hologram -- 3.3.3.Reconstruction of a real off-axis hologram -- 3.3.4.Comparison of Fourier and Convolutional reconstruction algorithms -- 3.4.Image reconstruction from projections -- Questions for self-testing -- 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 sine interpolations -- 4.4.Image resampling using ̀continuous' image models -- 4.4.1.Extracting image arbitrary profiles -- 4.4.2.Image local zoom -- 4.4.3.Image re-sampling according to random pixel X/Y displacement maps -- 4.4.4.Cartesian-to-polar coordinate conversion -- 4.5.Three step image rotation algorithm -- 4.6.Comparison of image resampling methods -- 4.6.1.Point spread functions and frequency responses of different interpolators -- 4.6.2.Multiple rotations of a test image -- 4.6.3.Image multiple zoom-in/zoom-out -- 4.7.Comparison of signal numerical differentiation and integration methods -- 4.7.1.Discrete frequency responses of numerical differentiators and integrators -- 4.7.2.Comparison of numerical differentiation methods -- 4.7.3.Iterative differentiation/integration -- Questions for self-testing -- 5.Image and noise statistical characterization and diagnostics -- 5.1.Introduction -- 5.2.Image histograms -- 5.2.1.Histograms of achromatic images -- 5.2.2.Histograms of color images -- 5.3.Image local moments and order statistics -- 5.4.Pixel attributes and neighborhoods -- 5.4.1.Pixel statistical attributes -- 5.4.2.Pixel neighborhoods -- 5.5.Image autocorrelation functions and power spectra -- 5.5.1.Image autocorrelation functions -- 5.5.2.Image power spectra -- 5.6.Image noise -- 5.6.1.Additive noise -- 5.6.2.Impulsive noise -- 5.6.3.Speckle noise -- 5.7.Empirical diagnostics of image noise -- 5.7.1.Wide band noise -- 5.7.2.Moire noise -- 5.7.3.Banding noise -- Questions for self-testing -- 6.Statistical image models and pattern formation -- 6.1.Introduction -- 6.2.PWN models -- 6.2.1.Binary spatially inhomogeneous texture with controlled local probabilities of òne' -- 6.2.2.Spatially inhomogeneous texture with controlled variances (̀multiplicative noise') -- 6.2.3.Spatially inhomogeneous texture with controlled local histograms -- 6.3.LF models -- 6.3.1.Introduction -- 6.3.2.̀Ring of stars', circular and ring-shaped spectra, ̀fractal' textures -- 6.3.3.Imitation of natural textures -- 6.3.4.Spatially inhomogeneous textures with controlled local spectra -- 6.4.PWN&LF and LF&PWN models -- 6.5.Evolutionary models -- 6.5.1.Generating patchy patterns -- 6.5.2.Generating maze-like patterns -- Questions for self-testing -- 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 -- 7.2.1.Localization of a target on uniform background -- 7.2.2.Localization of a character in text -- 7.2.3.Threshold effect in the probability of false target detection error -- 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 -- 7.4.1.Matched filter correlator versus SCR optimal correlator -- 7.4.2.Local versus global SCR optimal correlators -- 7.5.Object localization and image edges -- 7.5.1.Ìmage whitening' -- 7.5.2.Exchange of amplitude spectra of two images -- Questions for self-testing -- 8.Methods of image perfecting -- 8.1.Introduction -- 8.2.Correcting imaging system transfer functions -- 8.2.1.Correction of imaging system gray scale transfer function -- 8.2.2.Correction of imaging system frequency transfer function -- 8.3.Filtering periodical interferences. Filtering ̀banding' noise -- 8.3.1.Introduction -- 8.3.2.Filtering periodical interferences -- 8.4.Filtering ̀banding' noise -- 8.5.Ìdeal' and empirical Wiener filtering for image denoising and deblurring -- 8.5.1.Introduction -- 8.5.2.Comparing image deblurring/denoising capabilities of the ideal and empirical Wiener filters -- 8.5.3.Inspection of potentials of image restoration capability of the ideal and empirical Wiener filters -- 8.6.Local adaptive filtering for image denoising: achromatic images -- 8.6.1.Introduction -- 8.6.2.1D denoising filtering -- 8.6.3.2D denoising filtering: principle -- 8.6.4.2D denoising filtering: global versus local -- 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 -- 8.9.1.Filtering additive noise -- 8.9.2.Filtering impulsive noise -- Questions for self-testing -- 9.Methods of image enhancement -- 9.1.Introduction -- 9.2.Enhancement of achromatic images -- 9.2.1.Contrast enhancement -- 9.2.2.Edge extraction: Max-Min and Size-EV methods -- 9.3.Enhancement of color images -- 9.3.1.Introduction