Computational Imaging

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.