Computational Imaging
- Length: 488 pages
- Edition: 1
- Language: English
- Publisher: The MIT Press
- Publication Date: 2022-10-25
- ISBN-10: 0262046474
- ISBN-13: 9780262046473
- Sales Rank: #971540 (See Top 100 Books)
A comprehensive and up-to-date textbook and reference for computational imaging, which combines vision, graphics, signal processing, and optics.
Computational imaging involves the joint design of imaging hardware and computer algorithms to create novel imaging systems with unprecedented capabilities. In recent years such capabilities include cameras that operate at a trillion frames per second, microscopes that can see small viruses long thought to be optically irresolvable, and telescopes that capture images of black holes. This text offers a comprehensive and up-to-date introduction to this rapidly growing field, a convergence of vision, graphics, signal processing, and optics. It can be used as an instructional resource for computer imaging courses and as a reference for professionals. It covers the fundamentals of the field, current research and applications, and light transport techniques.
The text first presents an imaging toolkit, including optics, image sensors, and illumination, and a computational toolkit, introducing modeling, mathematical tools, model-based inversion, data-driven inversion techniques, and hybrid inversion techniques. It then examines different modalities of light, focusing on the plenoptic function, which describes degrees of freedom of a light ray. Finally, the text outlines light transport techniques, describing imaging systems that obtain micron-scale 3D shape or optimize for noise-free imaging, optical computing, and non-line-of-sight imaging. Throughout, it discusses the use of computational imaging methods in a range of application areas, including smart phone photography, autonomous driving, and medical imaging. End-of-chapter exercises help put the material in context.
Title Page Copyright Contents List of Figures List of Tables Preface 1. Introduction to Computational Imaging 1.1. What Is Computational Imaging? 1.2. Historical Roots of Computational Imaging 1.3. Modern Uses of Computational Imaging 1.4. Roadmap of the Book Part I: Toolkits 2. Imaging Toolkit 2.1. Optics 2.1.1. Animal Eyes 2.1.2. Light, Waves, and Particles 2.1.3. Measuring Light with Rays 2.1.4. Pinhole Model 2.1.5. Ray Bending and Lenses 2.1.6. Lenses and Focus 2.1.7. Masks and Aperture Manipulation 2.2. Image Sensors 2.2.1. Cameras, Rays, and Radiance 2.2.2. Digital Image Formation 2.2.3. Image Interpolation 2.2.4. Digital Imaging Pipeline 2.3. Illumination 2.3.1. Duration and Intensity 2.3.2. Auxiliary Lighting 2.3.3. Modifying Color, Wavelength, and Polarization 2.3.4. Modifying Position and Orientation 2.3.5. Modifying Space and Time Exercises 3. Computational Toolkit 3.1. Modeling: Forward vs. Inverse Problems 3.2. Mathematical Tools 3.2.1. Signal Processing 3.2.2. Linear Algebra 3.3. Model-Based Inversion 3.3.1. Examples of Ill-Posed Inverse Problems 3.3.2. Tools and Techniques 3.3.3. Examples of Model-Based Reconstruction 3.4. Data-Driven Inversion Techniques 3.4.1. Machine Learning 3.4.2. Neural Networks and Deep Learning 3.4.3. Convolutional Neural Networks and Computer Vision 3.5. Hybrid Inversion Techniques (Data Driven and Model Based) 3.5.1. Physics-Based Regularization 3.5.2. Physics-Guided Network Initialization 3.5.3. Physics-Based Network Architectures 3.5.4. Hybrid Models 3.5.5. Optical Neural Networks Exercises Part II: Plenoptic Imaging 4. Spatially Coded Imaging 4.1. Coding the Aperture 4.1.1. Physical Perspective 4.1.2. Mathematical Perspective 4.1.3. Noncoded Aperture 4.1.4. Pinhole 4.1.5. Coded Aperture 4.2. Coding the Sensor 4.2.1. Coded Sensors for Color Imaging 4.2.2. Coded Sensors for High Dynamic Range Imaging 4.2.3. Modulo Sensors for HDR Imaging 4.2.4. Tone Mapping 4.2.5. Exposure Metering 4.2.6. Improving the Resolution 4.2.7. Capturing Fast Phenomena 4.2.8. Using Coded Sensors for Light Field Capture 4.3. Coding the Illumination 4.3.1. Coded Illumination Imaging with Flash 4.3.2. Coded Illumination Imaging with Lasers 4.3.3. Coded Illumination Imaging with LEDs 4.4. Further Research 4.4.1. Compressive Imaging 4.4.2. Ghost Imaging 4.4.3. Spectrometry Exercises 5. Temporally Coded Imaging 5.1. A Brief History of the Time-of-Flight Revolution 5.2. Optical Time-Resolved Imaging 5.3. Time-Resolved Image Formation Model 5.3.1. Probing Function 5.3.2. Scene Response Function 5.3.3. Reflected Function 5.3.4. Instrument Response Function 5.3.5. Continuous-Time Measurements 5.3.6. Discrete-Time Measurements 5.4. Lock-in Sensor–based 3D Imaging 5.4.1. Continuous Wave Imaging 5.4.2. Coded Time-of-Flight Imaging 5.5. Application Areas 5.5.1. Diffuse Imaging 5.5.2. Light-in-Flight Imaging 5.5.3. Multidepth Imaging 5.5.4. Fluorescence Lifetime Imaging 5.5.5. Non-Line-of-Sight Imaging 5.6. Summary of Recent Advances and Further Applications 5.6.1. Time-Resolved Imaging through Scattering Media 5.6.2. Time-Resolved Imaging Systems 5.7. Related Optical Imaging Techniques 5.7.1. Optical Coherence Tomography 5.7.2. Digital Holography 5.7.3. Time-Stretched Optics Exercises 6. Light Field Imaging and Display 6.1. Historical Highlight: Lippmann Light Field Camera (1908) 6.2. Light Field Processing 6.2.1. Light Field Formulation 6.2.2. Refocusing 6.2.3. Generating Novel Views 6.2.4. Depth Estimation 6.2.5. Further Research 6.3. Light Field Capture 6.3.1. Camera Arrays 6.3.2. Dappled Photography 6.3.3. Microscopic Light Field Imaging 6.3.4. Further Research and Applications 6.4. Light Field Displays 6.4.1. Traditional 3D Displays 6.4.2. Multilayer and Multiframe Displays 6.4.3. Tensor Displays 6.4.4. Open Problems with Light Field Displays Exercises 7. Polarimetric Imaging 7.1. Principles of Polarization 7.1.1. Formal Definition of Polarization 7.1.2. Coding with Polarization 7.1.3. Information in Polarization 7.2. Full Stokes Imaging 7.2.1. Parametrization of Polarization 7.2.2. Measuring Stokes Parameters 7.3. 3D Shape Reconstruction 7.4. Imaging through Scattering Media 7.4.1. Underwater Imaging 7.4.2. Imaging through Haze and Fog 7.4.3. Polarization-ToF Fusion for Depth Maps 7.5. Reflectance Decomposition Using Polarimetric Cues 7.5.1. Specular vs. Diffuse Reflection 7.5.2. Virtual vs. Real Image Decomposition Exercises 8. Spectral Imaging 8.1. Spectral Effects on Light-Matter Interaction 8.1.1. Formal Definition of Spectrum 8.1.2. Absorption, Reflectance, and Transmittance 8.1.3. Multispectral and Hyperspectral Imaging 8.1.4. Applications of Nonvisible Light 8.2. Color Theory 8.2.1. Retinal Color 8.2.2. Perceptual Color 8.2.3. Information Loss in Human-Inspired Vision 8.3. Optical Setups for Spectral Imaging 8.3.1. Prisms, Gratings, and Scanners 8.3.2. Multispectral Filter Arrays and Compound Imaging 8.3.3. Spectrum-RGB Parallel Capture 8.3.4. Coded Spectral Illumination 8.4. Computational Methods for Analyzing Spectral Data 8.4.1. Spatiospectral Matrix Representations 8.4.2. Dimensionality Reduction 8.4.3. Multispectral Demosaicking Exercises Part III: Shading and Transport of Light 9. Programmable Illumination and Shading 9.1. Scene Reflectance and Photometry 9.1.1. Albedo, Radiance, and Irradiance 9.1.2. Lambert’s Law 9.1.3. Bidirectional Reflectance Distribution Function 9.2. Shape from Intensity 9.2.1. Reflectance Maps and Gradient Space 9.2.2. Calibrated Diffuse Photometric Stereo 9.2.3. Uncalibrated Diffuse Photometric Stereo 9.2.4. Dichromatic Reflection Model 9.2.5. Shape from Interreflections 9.2.6. Example-Based Photometric Stereo 9.3. Multiplexed Illumination 9.4. Applications in Graphics 9.4.1. Light Stage 9.4.2. Image Rendering and Relighting 9.4.3. Local Shading Adaptation Exercises 10. Light Transport 10.1. Motivation 10.1.1. Curse of Dimensionality 10.1.2. Light Transport Addresses Curse of Dimensionality 10.1.3. Forward vs. Inverse Light Transport 10.1.4. Chapter Organization 10.2. Light Transport Matrix 10.2.1. Light Transport Matrix: Forward Perspective 10.2.2. Light Transport Matrix: Inverse Perspective 10.3. Relaxations of Inverse Light Transport 10.3.1. Global and Direct Separation 10.3.2. Optical Probing of the Light Transport Matrix 10.4. Non-Line-of-Sight Imaging 10.4.1. Time-of-Flight Methods 10.4.2. Intensity-Based Methods 10.5. Applications 10.5.1. Applications in ToF Imaging 10.5.2. Skin Imaging 10.5.3. Imaging through Scattering Media Exercises Glossary References Index
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