Computational Optical Imaging
Will be published approx. on 27. January 2026
Book
Paperback/Softback
978-1-5106-8891-9 (ISBN)
Description
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.
More details
Content
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
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