# Data-Driven Design for Fourier Ptychographic Microscopy

arXiv: https://arxiv.org/abs/1904.04175 · HTML: https://mlsanity.com/paper/1904.04175/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2019.

## Built on by (most cited first, 18 of 18)

- [On the use of deep learning for phase recovery](https://mlsanity.com/paper/2308.00942/) (2023)
- [Time-Multiplexed Coded Aperture Imaging: Learned Coded Aperture and Pixel Exposures for Compressive Imaging Systems](https://mlsanity.com/paper/2104.02820/) (2021)
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- [Learning Spatially Varying Pixel Exposures for Motion Deblurring](https://mlsanity.com/paper/2204.07267/) (2022)
- [Physics vs. Learned Priors: Rethinking Camera and Algorithm Design for Task-Specific Imaging](https://mlsanity.com/paper/2204.09871/) (2022)
- [Adaptive Illumination based Depth Sensing using Deep Superpixel and Soft Sampling Approximation](https://mlsanity.com/paper/2103.12297/) (2021)
- [Differentiable model-based adaptive optics for two-photon microscopy](https://mlsanity.com/paper/2104.14308/) (2021)
- [Resource-Efficient Invariant Networks: Exponential Gains by Unrolled Optimization](https://mlsanity.com/paper/2203.05006/) (2022)
- [LWGNet: Learned Wirtinger Gradients for Fourier Ptychographic Phase Retrieval](https://mlsanity.com/paper/2208.04283/) (2022)
- [Hybrid deep learning and physics-based neural network for programmable illumination computational microscopy](https://mlsanity.com/paper/2403.12970/) (2024)
- [Joint Optimization of Hadamard Sensing and Reconstruction in Compressed Sensing Fluorescence Microscopy](https://mlsanity.com/paper/2105.07961/) (2021)
- [Differentiable Microscopy Designs an All Optical Phase Retrieval Microscope](https://mlsanity.com/paper/2203.14944/) (2022)
- [Differentiable Microscopy for Content and Task Aware Compressive Fluorescence Imaging](https://mlsanity.com/paper/2203.14945/) (2022)
- [Integration of Programmable Diffraction with Digital Neural Networks](https://mlsanity.com/paper/2406.10688/) (2024)
- [Latent Space Imaging](https://mlsanity.com/paper/2407.07052/) (2024)
- [Direct Image Classification from Fourier Ptychographic Microscopy Measurements without Reconstruction](https://mlsanity.com/paper/2505.05054/) (2025)
- [Batch-FPM: Random batch-update multi-parameter physical Fourier ptychography neural network](https://mlsanity.com/paper/2408.13782/) (2024)

## Abstract (arXiv)

Fourier Ptychographic Microscopy (FPM) is a computational imaging method that is able to super-resolve features beyond the diffraction-limit set by the objective lens of a traditional microscope. This is accomplished by using synthetic aperture and phase retrieval algorithms to combine many measurements captured by an LED array microscope with programmable source patterns. FPM provides simultaneous large field-of-view and high resolution imaging, but at the cost of reduced temporal resolution, thereby limiting live cell applications. In this work, we learn LED source pattern designs that compress the many required measurements into only a few, with negligible loss in reconstruction quality or resolution. This is accomplished by recasting the super-resolution reconstruction as a Physics-based Neural Network and learning the experimental design to optimize the network's overall performance. Specifically, we learn LED patterns for different applications (e.g. amplitude contrast and quantitative phase imaging) and show that the designs we learn through simulation generalize well in the experimental setting. Further, we discuss a context-specific loss function, practical memory limitations, and interpretability of our learned designs.