English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Point-spread-function (PSF) engineering is a powerful computational imagingtechniques wherein a custom phase mask is integrated into an optical system toencode additional information into captured images. Used in combination withdeep learning, such systems now offer state-of-the-art performance at monoculardepth estimation, extended depth-of-field imaging, lensless imaging, and othertasks. Inspired by recent advances in spatial light modulator (SLM) technology,this paper answers a natural question: Can one encode additional informationand achieve superior performance by changing a phase mask dynamically overtime? We first prove that the set of PSFs described by static phase masks isnon-convex and that, as a result, time-averaged PSFs generated by dynamic phasemasks are fundamentally more expressive. We then demonstrate, in simulation,that time-averaged dynamic (TiDy) phase masks can offer substantially improvedmonocular depth estimation and extended depth-of-field imaging performance.

TiDy-PSFs: Computational Imaging with Time-Averaged Dynamic Point-Spread-Functions