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Untrained deep learning-based differential phase-contrast microscopy
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DOI：10.1364/OL.493391.png)
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Abstract
En 中文
Quantitative differential phase-contrast (DPC) microscopy produces phase images of transparent objects based on a number of intensity images. To reconstruct the phase, in DPC microscopy, a linearized model for weakly scatte-ring objects is considered; this limits the range of objects to be imaged, and requires additional measurements and complicated algorithms to correct for system aberrations. Here, we present a self-calibrated DPC microscope using an untrained neural network (UNN), which incorporates the nonlinear image formation model. Our method alleviates the restrictions on the object to be imaged and simulta-neously reconstructs the complex object information and aberrations, without any training dataset. We demonstrate the viability of UNN-DPC microscopy through both numeri-cal simulations and LED microscope-based experiments. & COPY; 2023 Optica Publishing Group
Keywords:
DIGITAL HOLOGRAPHIC MICROSCOPY
HIGH-RESOLUTION
ILLUMINATION
Journal
IF:
3.3
Papers:
4.0W
Citations:
7.6W