Ramesh Raskar

Ayush Bhandari

Achuta Kadambi

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.

Computational Imaging

Ben Hutchinson

Qiang Yang

Graham Cormode

Yang Liu

David Evans

Han Yu

Mehryar Mohri

Zheng Xu

Rachel Cummings

Dawn Song

Mehdi Bennis

Peter Kairouz

H. Brendan McMahan

Brendan Avent

Aurélien Bellet

Arjun Nitin Bhagoji

Kallista Bonawit

Zachary Charles

Rafael G. L. D'Oliveira

Hubert Eichner

Salim El Rouayheb

Josh Gardner

Zachary Garrett

Adrià Gascón

Badih Ghazi

Phillip B. Gibbons

Marco Gruteser

Zaid Harchaoui

Chaoyang He

Lie He

Zhouyuan Huo

Justin Hsu

Martin Jaggi

Tara Javidi

Gauri Joshi

Mikhail Khodak

Jakub Konecný

Aleksandra Korolova

Farinaz Koushanfar

Sanmi Koyejo

Tancrède Lepoint

Prateek Mittal

Richard Nock

Ayfer Özgür

Rasmus Pagh

Daniel Ramage

Mariana Raykova

Weikang Song

Sebastian U. Stich

Ziteng Sun

Ananda Theertha Suresh

Florian Tramèr

Praneeth Vepakomma

Jianyu Wang

Li Xiong

Felix X. Yu

Sen Zhao

The term Federated Learning was coined as recently as 2016 to describe a machine learning setting where multiple entities collaborate in solving a machine learning problem, under the coordination of a central server or service provider. Each client's raw data is stored locally and not exchanged or transferred; instead, focused updates intended for immediate aggregation are used to achieve the learning objective.Since then, the topic has gathered much interest across many different disciplines and the realization that solving many of these interdisciplinary problems likely requires not just machine learning but techniques from distributed optimization, cryptography, security, differential privacy, fairness, compressed sensing, systems, information theory, statistics, and more.This monograph has contributions from leading experts across the disciplines, who describe the latest state-of-the art from their perspective. These contributions have been carefully curated into a comprehensive treatment that enables the reader to understand the work that has been done and get pointers to where effort is required to solve many of the problems before Federated Learning can become a reality in practical systems.Researchers working in the area of distributed systems will find this monograph an enlightening read that may inspire them to work on the many challenging issues that are outlined. This monograph will get the reader up to speed quickly and easily on what is likely to become an increasingly important topic: Federated Learning.

Advances and Open Problems in Federated Learning

Jack Tumblin

Computational Photography combines plentiful computing, digital sensors, modern optics, actuators, probes, and smart lights to escape the limitations of traditional film cameras and enables novel imaging applications. This book provides a practical guide to topics in image capture and manipulation methods for generating compelling pictures for graphics, special effects, scene comprehension, and art. The computational techniques discussed cover topics in exploiting new ideas in manipulating optics, illumination, and sensors at time of capture. In addition, the authors describe sophisticated reconstruction procedures from direct and indirect pixel measurements that go well beyond the traditional digital darkroom experience.

Mastering New Techniques for Lenses, Lighting, and Sensors

Computational Photography

Oliver Bimber

Like virtual reality, augmented reality is becoming an emerging platform in new application areas for museums, edutainment, home entertainment, research, industry, and the art communities using novel approaches which have taken augmented reality beyond traditional eye-worn or hand-held displays. In this book, the authors discuss spatial augmented r

Merging Real and Virtual Worlds

Spatial Augmented Reality

Ramesh Raskar

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