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

With applications ranging from metabolomics to histopathology, quantitativephase microscopy (QPM) is a powerful label-free imaging modality. Despitesignificant advances in fast multiplexed imaging sensors anddeep-learning-based inverse solvers, the throughput of QPM is currently limitedby the speed of electronic hardware. Complementarily, to improve throughputfurther, here we propose to acquire images in a compressed form such that moreinformation can be transferred beyond the existing electronic hardwarebottleneck. To this end, we present a learnable opticalcompression-decompression framework that learns content-specific features. Theproposed differentiable quantitative phase microscopy ($\partial \mu$) firstuses learnable optical feature extractors as image compressors. The intensityrepresentation produced by these networks is then captured by the imagingsensor. Finally, a reconstruction network running on electronic hardwaredecompresses the QPM images. In numerical experiments, the proposed systemachieves compression of $\times$ 64 while maintaining the SSIM of $\sim 0.90$and PSNR of $\sim 30$ dB on cells. The results demonstrated by our experimentsopen up a new pathway for achieving end-to-end optimized (i.e., optics andelectronic) compact QPM systems that may provide unprecedented throughputimprovements.

From Hours to Seconds: Towards 100x Faster Quantitative Phase Imaging via Differentiable Microscopy