This repository contains official PyTorch code for implementing a differentiable snapshot microscope and the relevant experiment scripts from the paper: FourierNets enable the design of highly non-local optical encoders for computational imaging.
What is included:
- A differentiable simulation of a 3D snapshot microscope.
- Scripts to recreate experiments from the paper, both using the microscope and using DLMD for computational photography reconstruction.
- Implementations of FourierNet/FourierUNet architectures from the paper. This is a dependency for running the experiments, but not for using the simulation. These are included in this repository for ease of replication. If you just want implementations of FourierNets, you can obtain implementations for both PyTorch and JAX/Flax from TuragaLab/fouriernet (coming soon).
What is not included:
- This repository does not include the data required to run the experiments. The data can be obtained from Figshare (coming soon).
We have tested snapshotscope on Python 3.7 with PyTorch 1.7. Newer versions of PyTorch are not supported because they have swapped to a new FFT interface.
To install the library (required for running the experiment scripts), you can run:
$ pip install git+https://github.com/TuragaLab/snapshotscope
$ python exp.py <train | test>
You will probably want to change the locations where data is read and results are saved by modifying the provided exp.py files. These experiment scripts in experiments follow the same pattern: they take one argument, which is whether to train or test. If you want to modify any other aspect of the training, you can simply change those settings in the corresponding exp.py file.