2,371 research outputs found
Compressive Holographic Video
Compressed sensing has been discussed separately in spatial and temporal
domains. Compressive holography has been introduced as a method that allows 3D
tomographic reconstruction at different depths from a single 2D image. Coded
exposure is a temporal compressed sensing method for high speed video
acquisition. In this work, we combine compressive holography and coded exposure
techniques and extend the discussion to 4D reconstruction in space and time
from one coded captured image. In our prototype, digital in-line holography was
used for imaging macroscopic, fast moving objects. The pixel-wise temporal
modulation was implemented by a digital micromirror device. In this paper we
demonstrate temporal super resolution with multiple depths recovery
from a single image. Two examples are presented for the purpose of recording
subtle vibrations and tracking small particles within 5 ms.Comment: 12 pages, 6 figure
Sparsity-regularized coded ptychography for robust and efficient lensless microscopy on a chip
In ptychographic imaging, the trade-off between the number of acquisitions
and the resultant imaging quality presents a complex optimization problem.
Increasing the number of acquisitions typically yields reconstructions with
higher spatial resolution and finer details. Conversely, a reduction in
measurement frequency often compromises the quality of the reconstructed
images, manifesting as increased noise and coarser details. To address this
challenge, we employ sparsity priors to reformulate the ptychographic
reconstruction task as a total variation regularized optimization problem. We
introduce a new computational framework, termed the ptychographic proximal
total-variation (PPTV) solver, designed to integrate into existing ptychography
settings without necessitating hardware modifications. Through comprehensive
numerical simulations, we validate that PPTV-driven coded ptychography is
capable of producing highly accurate reconstructions with a minimal set of
eight intensity measurements. Convergence analysis further substantiates the
robustness, stability, and computational feasibility of the proposed PPTV
algorithm. Experimental results obtained from optical setups unequivocally
demonstrate that the PPTV algorithm facilitates high-throughput,
high-resolution imaging while significantly reducing the measurement burden.
These findings indicate that the PPTV algorithm has the potential to
substantially mitigate the resource-intensive requirements traditionally
associated with high-quality ptychographic imaging, thereby offering a pathway
toward the development of more compact and efficient ptychographic microscopy
systems.Comment: 15 pages, 7 figure
Deep learning in computational microscopy
We propose to use deep convolutional neural networks (DCNNs) to perform 2D and 3D computational imaging. Specifically, we investigate three different applications. We first try to solve the 3D inverse scattering problem based on learning a huge number of training target and speckle pairs. We also demonstrate a new DCNN architecture to perform Fourier ptychographic Microscopy (FPM) reconstruction, which achieves high-resolution phase recovery with considerably less data than standard FPM. Finally, we employ DCNN models that can predict focused 2D fluorescent microscopic images from blurred images captured at overfocused or underfocused planes.Published versio
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