Login / Signup

Enhancing the recovery of a temporal sequence of images using joint deconvolution.

Piergiorgio CaramazzaKali WilsonGenevieve GariepyJonathan LeachStephen McLaughlinDaniele FaccioYoann Altmann
Published in: Scientific reports (2018)
In this work, we address the reconstruction of spatial patterns that are encoded in light fields associated with a series of light pulses emitted by a laser source and imaged using photon-counting cameras, with an intrinsic response significantly longer than the pulse delay. Adopting a Bayesian approach, we propose and demonstrate experimentally a novel joint temporal deconvolution algorithm taking advantage of the fact that single pulses are observed simultaneously by different pixels. Using an intensified CCD camera with a 1000-ps gate, stepped with 10-ps increments, we show the ability to resolve images that are separated by a 10-ps delay, four time better compared to standard deconvolution techniques.
Keyphrases
  • deep learning
  • convolutional neural network
  • optical coherence tomography
  • high speed
  • machine learning
  • blood pressure
  • study protocol
  • living cells
  • clinical trial
  • fluorescent probe