The trade-off between throughput and image quality is an inherent challenge in microscopy. To improve throughput, compressive imaging under-samples image signals; the images are then computationally reconstructed. However, the information loss in the acquisition process sets the compression bounds. Here we propose differentiable compressive fluorescence microscopy (∂μ) that includes a realistic generalizable forward model with learnable-physical parameters (i.e. illumination patterns), and a novel physics-inspired inverse model. The cascaded model is end-to-end differentiable and can learn optimal compressive sampling schemes through training data. Proposed learned sampling outperforms widely used traditional compressive sampling schemes at higher compressions. We also demonstrate task-aware sampling (e.g. segmentation-aware) with the proposed framework.
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