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

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    Peng&Sun2019LearnLargeFOV.pdf
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    Type
    Article
    Authors
    Peng, Yifan
    Sun, Qilin cc
    Dun, Xiong
    Wetzstein, Gordon
    Heidrich, Wolfgang cc
    Heide, Felix
    KAUST Department
    Computational Imaging Group
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering
    Electrical Engineering Program
    Visual Computing Center (VCC)
    Date
    2019-11-08
    Permanent link to this record
    http://hdl.handle.net/10754/661069
    
    Metadata
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    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.
    Citation
    Peng, Y., Sun, Q., Dun, X., Wetzstein, G., Heidrich, W., & Heide, F. (2019). Learned large field-of-view imaging with thin-plate optics. ACM Transactions on Graphics (TOG), 38(6), 1–14. doi:10.1145/3355089.3356526
    Sponsors
    The authors thank Liang Xu and Xu Liu from Zhejiang University for assisting in the manufacturing of the lens prototypes.
    Publisher
    Association for Computing Machinery (ACM)
    Journal
    ACM Transactions on Graphics (TOG)
    DOI
    10.1145/3355089.3356526
    Additional Links
    http://dl.acm.org/doi/10.1145/3355089.3356526
    ae974a485f413a2113503eed53cd6c53
    10.1145/3355089.3356526
    Scopus Count
    Collections
    Articles; Computer Science Program; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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