research-article

Learned large field-of-view imaging with thin-plate optics

Authors:

Gordon Wetzstein,

Wolfgang Heidrich,

Felix HeideAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 38, Issue 6

Article No.: 219, Pages 1 - 14

https://doi.org/10.1145/3355089.3356526

Published: 08 November 2019 Publication History

Abstract

Typical camera optics consist of a system of individual elements that are designed to compensate for the aberrations of a single lens. Recent computational cameras shift some of this correction task from the optics to post-capture processing, reducing the imaging optics to only a few optical elements. However, these systems only achieve reasonable image quality by limiting the field of view (FOV) to a few degrees - effectively ignoring severe off-axis aberrations with blur sizes of multiple hundred pixels.

In this paper, we propose a lens design and learned reconstruction architecture that lift this limitation and provide an order of magnitude increase in field of view using only a single thin-plate lens element. Specifically, we design a lens to produce spatially shift-invariant point spread functions, over the full FOV, that are tailored to the proposed reconstruction architecture. We achieve this with a mixture PSF, consisting of a peak and and a low-pass component, which provides residual contrast instead of a small spot size as in traditional lens designs. To perform the reconstruction, we train a deep network on captured data from a display lab setup, eliminating the need for manual acquisition of training data in the field. We assess the proposed method in simulation and experimentally with a prototype camera system.

We compare our system against existing single-element designs, including an aspherical lens and a pinhole, and we compare against a complex multielement lens, validating high-quality large field-of-view (i.e. 53°) imaging performance using only a single thin-plate element.

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 38, Issue 6

December 2019

1292 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3355089

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Published: 08 November 2019

Published in TOG Volume 38, Issue 6

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