Advanced digital imaging laboratory using MATLAB® 9. Methods of image enhancement 9.1. Introduction 9.2. Enhancement of achromatic images 9.3. Enhancement of color images 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 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{reg} exercises this book delivers a complete applied course in digital imaging theory and practice 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 Preface to the second edition Preface 1. Introduction 1.1. General remarks about the book 1.2. Instructions for readers 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 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 2. Image digitization 2.1. Introduction 2.2. Image discretization 2.3. Signal scalar quantization 2.4. Image compression 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 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