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      Physics-based Learned Design: Optimized Coded-Illumination for Quantitative Phase Imaging

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          Abstract

          Coded-illumination based reconstruction of Quantitative Phase (QP) is generally a non-linear iterative process. Thus, using traditional techniques for experimental design (e.g. condition number optimization or spectral analysis) may not be ideal as they characterize linear measurement formation models for linear reconstructions. Deep neural networks, DNNs, are end-to-end frameworks which can efficiently represent non-linear processes and can be optimized over by training. However, they do not necessarily include knowledge of the system physics and, as a result, require an enormous number of training examples and parameters to properly learn the phase retrieval process. Here, we present a new data-driven approach to optimize the coded-illumination patterns of a light-emitting diode (LED) array microscope to maximize a given QP reconstruction algorithm's performance. We establish a generalized formulation that incorporates the available information about the physics of a measurement model as well as the non-linearities of a reconstruction algorithm into the design problem. Our proposed design method enables an efficient parameterization of the design problem, which allows us to use only a small number of training examples to properly learn a design that generalizes well in the experimental setting without retraining. We image both a well-characterized phase target and mouse fibroblast cells using coded-illumination patterns optimized for a sparsity-based phase reconstruction algorithm. We obtain QP images similar to those of Fourier Ptychographic techniques with 69 measurements using only 2 learned design measurements.

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          Most cited references11

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          An Iterative Regularization Method for Total Variation-Based Image Restoration

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            Quantitative differential phase contrast imaging in an LED array microscope.

            Illumination-based differential phase contrast (DPC) is a phase imaging method that uses a pair of images with asymmetric illumination patterns. Distinct from coherent techniques, DPC relies on spatially partially coherent light, providing 2× better lateral resolution, better optical sectioning and immunity to speckle noise. In this paper, we derive the 2D weak object transfer function (WOTF) and develop a quantitative phase reconstruction method that is robust to noise. The effect of spatial coherence is studied experimentally, and multiple-angle DPC is shown to provide improved frequency coverage for more stable phase recovery. Our method uses an LED array microscope to achieve real-time (10 Hz) quantitative phase imaging with in vitro live cell samples.
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              Phase imaging by the transport equation of intensity

              N. Streibl (1984)
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                Author and article information

                Journal
                10 August 2018
                Article
                1808.03571
                b26a0209-4230-40ea-b865-dd0b9a6401ca

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                eess.SP cs.CV

                Computer vision & Pattern recognition,Electrical engineering
                Computer vision & Pattern recognition, Electrical engineering

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