Computational Optical Imaging

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