Overview

Computational optical imaging uses electromagnetic signals in the infrared, visible, ultraviolet, and x-ray wavelength ranges to characterize remote objects. This text explains how to create mathematical forward models describing tomographic, holographic, ptychographic, and photographic imaging systems. It describes image estimation algorithms, including those that use artificial neural networks and nonlinear estimators, to estimate still, video, and spectral images from measured data. The text considers geometric, diffractive, and statistical optical radiation models. It shows that advanced sensing and estimation strategies allow optical imagers to resolve targets with resolution exceeding conventional limits. It also considers how to maximize measurement efficiency and imager capacity using coded and feature-specific sampling and physical system design. Details not found in previous textbooks include coding strategies for compressive tomography, phase curvature in coherent imaging systems, the coherence transfer function, and interferometric focal planes. The last part of the book discusses digital camera design, including sampling optimization for photographic and video imaging and array camera design. This book focuses particularly on deep physical modeling of optical systems and algorithms. It aims to fill the gap between detector design and high-level image processing and to give readers the tools to design end-to-end imaging systems.

Full Product Details

Author:   David J. Brady
Publisher:   SPIE Press
Imprint:   SPIE Press
ISBN:  

9781510688919

ISBN 10:   1510688919
Publication Date:   22 December 2025
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Forthcoming
Availability:   Not yet available  
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Table of Contents

1. Introduction Magic The Treachery of Images Computational Imaging context 2. Forward Models Objects, Fields, and Measurements Modes Transformations Sampling and Discrete Analysis Bases and Dictionaries Discrete Forward Models Spectral Analysis Neural Representation Noise Resolution Channel Capacity Exercises 3. Image Estimation Methods and Metrics Shift-Invariant Systems Linear Regression LASSO Regression Expectation Maximization Neural Estimation Exercises 4. Ray Imaging Rays Pinhole and Coded Aperture Imaging Projection Tomography Coded Aperture Tomography Resolution in Geometric Imaging Systems Focal Imaging Snapshot Compressive Imaging Exercises 5. Wave Imaging Rays, Waves, and Coherence Wave Fields Diffraction Holography Phase Retrieval Diffraction Tomography Compressive Diffraction Tomography Temporal Holography Scatter Imaging Imaging through Inhomogeneous Media Exercises 6. Coherent Focal Systems Optics in Coherent Imaging Planar Optical Elements The Coherent Impulse Response Phase Curvature and Spatial Bandpass Defocus Ptychography Wavefront Cameras Exercises 7. Coherence Imaging A Third Field Model Coherence Fields Coherence Propagation Two-Beam Interferometry Coherence Tomography The Rayleigh Criterion Coherent Modes Imaging through Turbulence Exercises 8. Focal Imaging The Magic of Lenses Focal Transformations Fourier Analysis of Focal Imaging Focus and Depth of Field The Coherence Transfer Function Coherent Modes Revisited PSF Diversity Radiance Tomography Exercises 9. Digital Imaging Computational Photography Discrete Sampling and Aliasing Display of Discrete Images Compression The Camera Equation Intrinsic Calibration Extrinsic Calibration Multiframe Fusion Exericses 10. Sampling Strategy Data Cubes Feature-Specific Measurement Spectral Imaging Optical Coding for Temporal Imaging Dynamic Range Focus Lens Design Interferometric Focal Planes The Sampling Pipeline Exercises 11. Design Examples Computational ImagingRange Imaging Heterogeneous Array Cameras Event Capture Object Detection Object Identification Analytics and Machine Vision Exercises 12. Epilogue Chapter 12 reflects on the transformative potential of computational imaging, emphasizing its role in improving safety, efficiency, and quality of life. It highlights the global impact of traffic accidents and envisions a future where intelligent sensing systems prevent such tragedies. The chapter revisits the three core challenges of computational imaging – physical measurement, data representation, and image transformation – underscoring the need for continued innovation in each area. While the book focused on optimizing physical measurements, the author anticipates that advances in computing and neural processing will address the remaining challenges. The chapter notes rapid progress in imaging technologies like phase imaging, ptychography, and wavefront cameras, while pointing out that coherence and advanced spectral sampling remain underutilized. Ultimately, it concludes that computational imaging is still in its early stages, with vast potential ahead. Back Matter

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