IDEAL-IO decouples density estimation from optical optimization to make information-theoretic imaging design practical, cutting runtime and memory by up to 6x while enabling more expressive density models.
Fourier DiffuserScope: Single-shot 3D Fourier light field microscopy with a diffuser
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abstract
Light field microscopy (LFM) uses a microlens array (MLA) near the sensor plane of a microscope to achieve single-shot 3D imaging of a sample without any moving parts. Unfortunately, the 3D capability of LFM comes with a significant loss of lateral resolution at the focal plane. Placing the MLA near the pupil plane of the microscope, instead of the image plane, can mitigate the artifacts and provide an efficient forward model, at the expense of field-of-view (FOV). Here, we demonstrate improved resolution across a large volume with Fourier DiffuserScope, which uses a diffuser in the pupil plane to encode 3D information, then computationally reconstructs the volume by solving a sparsity-constrained inverse problem. Our diffuser consists of randomly placed microlenses with varying focal lengths; the random positions provide a larger FOV compared to a conventional MLA, and the diverse focal lengths improve the axial depth range. To predict system performance based on diffuser parameters, we for the first time establish a theoretical framework and design guidelines, which are verified by numerical simulations, then build an experimental system that achieves $< 3$ um lateral and $4$ um axial resolution over a $1000 \times 1000 \times 280$ um$^3$ volume. Our diffuser design outperforms the MLA used in LFM, providing more uniform resolution over a larger volume, both laterally and axially.
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Computationally Efficient Information-Driven Optical Design with Interchanging Optimization
IDEAL-IO decouples density estimation from optical optimization to make information-theoretic imaging design practical, cutting runtime and memory by up to 6x while enabling more expressive density models.