Skip to content
master
Switch branches/tags
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
src
 
 
 
 
 
 

'A Bit Too Much? High Speed Imaging from Sparse Photon Counts' Python Implementation

This repository is a python implementation of the scheme proposed in

Chandramouli, Paramanand, et al. "A bit too much? high speed imaging from sparse photon counts." 
2019 IEEE International Conference on Computational Photography (ICCP). IEEE, 2019.

A preprint can be found at https://arxiv.org/abs/1811.02396.

Code

The code is structered into data loading (dataloader.py, torch_augment.py, torch_augment_functions.py), definition of the network architecture (network.py), specification of the loss function (loss.py) and auxiliary files to define the training and testing procedure (solver.py, scheduler.py) as well as code for n-dimensional stitching (torch_stitching.py). Some exemplary network snapshots can be found in /snapshots.

Requirements

  • PyTorch
  • Scikit-image
  • H5py

The exact setup can be installed by running

conda env create -f environment.yml
conda activate HighSpeedImaging

Data

The dataset used for training and testing of the network is the Deep Video Deblurring for Hand-held Cameras Dataset which is publicly available.

This code has been developed together with mj9 as part of a university project.

About

No description, website, or topics provided.

Resources

License

Releases

No releases published

Packages

No packages published

Languages