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Ayush Bhandari - Computational Imaging

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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 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.

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Contents
List of Figures
Guide
Pagebreaks of the print version
Computational Imaging Ayush Bhandari Achuta Kadambi Ramesh Raskar - photo 1

Computational Imaging

Ayush Bhandari
Achuta Kadambi
Ramesh Raskar

The MIT Press
Cambridge, Massachusetts
London, England

2022 Massachusetts Institute of Technology

All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.

This work is subject to a Creative Commons CC-BY-ND-NC license. Subject to such license, all rights are reserved.

The MIT Press would like to thank the anonymous peer reviewers who provided - photo 2

The MIT Press would like to thank the anonymous peer reviewers who provided comments on drafts of this book. The generous work of academic experts is essential for establishing the authority and quality of our publications. We acknowledge with gratitude the contributions of these otherwise uncredited readers.

Library of Congress Cataloging-in-Publication Data

Names: Bhandari, Ayush, author. | Kadambi, Achuta, author. | Raskar, Ramesh, author.

Title: Computational imaging / Ayush Bhandari, Imperial College, London, Achuta Kadambi, University of California, Los Angeles, Ramesh Raskar, Massachusetts Institute of Technology.

Description: Cambridge, Massachusetts: The MIT Press, [2022] | Includes bibliographical references and index.

Identifiers: LCCN 2021037259 | ISBN 9780262046473 (hardcover)

Subjects: LCSH: Image processingDigital techniquesMathematics. | Imaging systems.

Classification: LCC TA1637.5.B53 2022 | DDC 006.6dc23
LC record available at https:// lccn .loc .gov /2021037259

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Contents
List of Figures

Motion deblurring: a comparison of traditional approach with computational imaging approach. Longer exposure period results in motion blurring because the object moves during the exposure. One can use a hardware-based solution that entails a short exposure period, with the disadvantage that the resulting image is noisy. Alternatively, one can use a computational approach that entails deconvolution and is inexact. The computational imaging alternative is based on coded exposure imaging. By coding the cameras shutter pattern, the resulting image can be recovered using an algorithm that results in superior performance.

Example of a room-sized pinhole camera.

Roadmap and organization of the book.

The ideal point source: a point source with radiant flux and the irradiance/exitance for an imaginary sphere.

The steradian and the solid angle of a cone-shaped beam.

The area illuminated by a parallel beam as a function of the incident angle.

The pinhole camera. (a) The camera obscura, a darkened room with only a hole in a wall, is an example of the pinhole camera principle; (b) a diagram represents the pinhole camera principle.

The pinspeck camera: this imaging device is based on the opposite functioning principle of the pinhole camera, casting shadows that form a negative image.

The pinhole camera diffraction: when the pinhole size is comparable to the wavelength of the incoming light, a distant object is imaged as a circular disk with rings around it.

The refraction principle: a ray of light is bent at the boundary of two materials with an angle given by Snells law.

The diagram of a thin lens: a point in the scene (black dot) is in focus if the light it reflects toward the lens converges on the sensor plane. When the camera refocuses on a different point, the sensor plane moves relative to the lens.

Lens-based camera obscura: the first cameras required a manual adjustment of the exposure time. Reprinted from (Raskar and Tumblin, 2011).

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