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Learning for lensless mask-based Imaging

This code is based on the paper: "Learned reconstructions for practical mask-based lensless imaging" available here: (https://www.osapublishing.org/oe/fulltext.cfm?uri=oe-27-20-28075&id=420747)

Setup:

Clone this project using:

git clone https://github.com/Waller-Lab/LenslessLearning.git

The dependencies can be installed by using:

conda env create -f environment.yml
source activate lensless_learning

In addition, the LPIPS package is needed (this is used in the loss function during training). Instructions for installing LPIPS can be found here: (https://github.com/richzhang/PerceptualSimilarity)

Loading in the models

The pre-trained models can be downloaded here

Jupyter Notebook: pre-trained reconstructions.ipynb

  • Loads in the pre-trained models and runs reconstructions on sample lensless images.
  • Initializes un-trained models and shows output images before training. Changes model parameters to the pre-loaded parameters and shows sample reconstructions

Dataset