Advanced digital imaging laboratory using MATLAB® Statistical image models and pattern formation Introduction PWN models LF models PWN & LF and LF & PWN models Evolutionary models Questions for self testing Methods of image enhancement Introduction Contrast enhancement Edge extraction. Max Min and Size EV methods 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 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 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 Preface Author biography Introduction General remarks about the book Instructions for readers 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 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 obtaining practical skills based on fundamental knowledge in the subject. The current book is aimed at providing technical support for this. It contains exercises on all major topics of digital imaging for students researchers in experimental sciences and generally all practitioners in imaging engineering 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