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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 contrast to traditional imaging, computational imaging is distinguished by a heavy use of mathematical algorithms. 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.
There are six major classes of Machine Learning: (1) clustering, (2) classification, (3) regression, (4) Deep Learning, (5) dimensionality reduction, and (6) reinforcement learning. Our discussions are confined to classes 1 through 4. Neural networks are a Machine Learning models inspired by the pattern of neurons firing in the human brain. Neural networks attempt to process information and data similarly to our brains. As a simplified model, a neural network contains several layers of processing. Each layer contains several nodes, which are information processing units that simulate the neurons. Nodes are densely interconnected between adjacent layers. These artificial neural networks (ANN) contain between dozens and millions of artificial neurons. This subset of Machine Learning is known as Deep Learning, in which deep layers of neurons are used to compute complex nonlinear inverse function mappings