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Physics-Based Learned Diffuser for Single-shot 3D Imaging

A diffuser in the Fourier space of an imaging system can encode 3D fluorescence intensity information in a single-shot 2D measurement, which is then recovered by a compressed sensing algorithm. Typically, the diffusers used in such systems are either off-the-shelf, heuristically designed, or merit function driven. In this work we use a differentiable forward model of single-shot 3D microscopy in conjunction with an invertible and differentiable reconstruction algorithm, ISTA-Net+, to jointly optimize both the diffuser surface shape and the reconstruction parameters. By choosing a differentiable and invertible reconstruction method, we enable the use of memory-efficient backpropagation to trade off storage with a reasonable increase in compute time, in order to fit an unrolled network containing a large-scale 3D volume into a single GPU’s memory. We validate our method on 2D and 3D single-shot imaging, where our learned diffuser demonstrates improved reconstruction quality compared to previous heuristic designs.

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