VCD-Net

High-efficiency light field microscopy reconstruction for VCD-LFM. todo: add reference

This package contains:

1. /datapre: Matlab scripts (GUI interface provided) for VCD-Net training dataset synthesis
2. /vcdnet: Source code for deep-learning based light field microscopy reconstruction

Requirements

• Python 3
• (Optional but highly recommended) CUDA 10.2 and CUDNN
• Packages:
  • easydict==1.9
  • imageio==2.4.1
  • numpy==1.15.4
  • scikit-image==0.14.1
  • scipy==1.2.0
  • tensorflow-gpu==1.14.0
• Matlab

Usage

For usage demo, we created a jupyter notebook to navigate through the pipeline. Example data can be downloaded from here. After unzip, put the data/ and checkpoint/ folder under vcdnet/.

Updates 10.22.2020

In current revision, we added an option to switch to a modified model for function imaging data reconstruction.

To check the model, go to /vcdnet/model/unet.py->UNet_B

The general pipeline stays the same and the only thing to do for model switching is to change from config.model = 'structure' to config.model = 'function' in /vcdnet/config.py

Edit the /vcdnet/config.py to ensure:

config.PSF.n_slices = 31
label                             = 'neuron_8um_simu_40x_n11_[m30-0]_step1um'
config.model                      = 'function'
config.VALID.lf2d_path            = './data/to_predict2/'

And run

cd /code/vcdnet
python3 eval.py

or

cd /code/vcdnet
python3 eval.py --cpu

to reconstruct the demo images.

Updates 11.26.2020

More examples were added to the example dataset.

Fluo Beads

Edit the /vcdnet/config.py to ensure:

config.PSF.n_slices = 61
label                             = 'beads_empirical_40x_n11_[m30-30]_step1um'
config.model                      = 'structure'
config.VALID.lf2d_path            = './data/to_predict_beads/'

And run

cd /code/vcdnet
python3 eval.py

Worms

Edit the /vcdnet/config.py to ensure:

config.PSF.n_slices = 31
label                             = 'worm_empirical_40x_n11_[m30-0]_step1um'
config.model                      = 'function'
config.VALID.lf2d_path            = './data/to_predict_worm/'

And run

cd /code/vcdnet
python3 eval.py