Vincent C. H. Leung ([email protected]), Jun-Jie Huang, Yonina C. Eldar and Pier Luigi Dragotti
This repository is contains the code and pretrained models for "Learning-Based Reconstruction of FRI Signals" (TSP' 23; arxiv).
The code consists of two different learning-based FRI models: Deep Unfolded Projected Wirtinger Gradient Descent (Deep Unfolded PWGD) and FRI Encoder-Decoder Network (FRIED-Net). Deep Unfolded PWGD operates in the frequency domain and unfolds the iterative denoising process, while FRIED-Net models the FRI acquisition process and the fact that FRI signals are determined by a small number of parameters to form an autoencoder-like network architecture.
The code was tested on Python 3.10 so this particular version is recommended.
The code for both models was developed based on PyTorch. torch>=2.0.0
is required specifically for Deep Unfolded PWGD since it deals with complex numbers. You can install the latest version of PyTorch here.
Other required packages are listed in requirements.txt
.
Please refer to DeepUnfoldedPWGD
for the implementation and pretrained models.
Please refer to FRIED-Net
for the implementation and pretrained models.
Two types of data are used in the paper: synthetic and calcium imaging (cai-1) dataset. Please refer to datasets
for related MATLAB codes and detailed instructions on how to use them.
If you find this repository, please cite the following paper:
V. C. H. Leung, J.-J. Huang, Y. C. Eldar and P. L. Dragotti. "Learning-Based Reconstruction of FRI signals," IEEE Transactions on Signal Processing, vol. 71, pp. 2564-2578, 2023.
@article{Leung2023,
title = {Learning-Based Reconstruction of {{FRI}} Signals},
author = {Leung, Vincent C. H. and Huang, Jun-Jie and Eldar, Yonina C. and Dragotti, Pier Luigi},
date = {2023},
journal={IEEE Transactions on Signal Processing},
volume={71},
number={},
pages={2564--2578},
doi = {10.1109/TSP.2023.3290355}
}