https://github.com/laknath1996/deepphaseunwrap
This repository Introduces a joint convolutional and spatial quad-directional LSTM (SQD-LSTM) network for phase unwrapping in 2D images (ICASSP 2021)
https://github.com/laknath1996/deepphaseunwrap
cnn deep-learning lstm phase-unwrapping
Last synced: about 1 year ago
JSON representation
This repository Introduces a joint convolutional and spatial quad-directional LSTM (SQD-LSTM) network for phase unwrapping in 2D images (ICASSP 2021)
- Host: GitHub
- URL: https://github.com/laknath1996/deepphaseunwrap
- Owner: Laknath1996
- License: mit
- Created: 2021-06-03T05:51:32.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2024-10-31T21:59:47.000Z (over 1 year ago)
- Last Synced: 2025-03-28T11:11:32.967Z (over 1 year ago)
- Topics: cnn, deep-learning, lstm, phase-unwrapping
- Language: Python
- Homepage:
- Size: 15 MB
- Stars: 41
- Watchers: 2
- Forks: 5
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
A Joint Convolutional and Space Quad-Directional LSTM Network for Phase Unwrapping
==============================
[](https://2021.ieeeicassp.org/) [](https://ieeexplore.ieee.org/document/9414748)
This repository contains the source code for the deep neural arcihetcure proposed by the ICASSP 2021 paper titled ["A Joint Convolutional and Space Quad-Directional LSTM Network for Phase Unwrapping"](https://ieeexplore.ieee.org/document/9414748).
If you use this code/paper for your research, please consider citing,
```
@INPROCEEDINGS{9414748,
author={Perera, Malsha V. and De Silva, Ashwin},
booktitle={ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={A Joint Convolutional and Spatial Quad-Directional LSTM Network for Phase Unwrapping},
year={2021},
volume={},
number={},
pages={4055-4059},
doi={10.1109/ICASSP39728.2021.9414748}}
```
Project Organization
------------
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data <- Datasets created/ used by the project
├── models <- Trained and serialized models
│
├── notebooks <- A tutorial on the project
│
├── reports
│ └── figures <- Generated graphics and figures
├── requirements.txt <- The requirements file for reproducing the analysis environment
│
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to generate data
│ ├── models <- Scripts to define models and losses.
| └── visualization <- Scripts to create plots
├── create_synthetic_phase_dataset.py <- Create datasets
├── train_model.py <- Train models
└── test_model.py <- Test models
--------
## Installation Guide
### Step 1 : Clone the Repository
Clone the repository using the following command.
````
$ git clone https://github.com/Laknath1996/DeepPhaseUnwrap.git
````
### Step 2 : Install Dependencies
Use the `requirements.txt` file given in the repository to install the dependencies via `pip`.
````
$ pip install -r requirements.txt
````
### Step 3 : Install Dependencies
Use the `create_synthetic_phase_dataset.py`, `train_model.py` and `test_model.py` files to create phase datasets, train models, and validate them, respectively.
## Tutorial
`notebooks/tutorial.ipynb` describes the specifics and the execution steps of the network.
## Authors
* **Ashwin De Silva** - ashwindesilva@yahoo.com
* **Malsha Perera** - malshaperera14@gmail.com
At the time of this work, both the authors were with the Department of Electronics and Telecommunication Engineering, University of Moratuwa, Sri Lanka. Feel free to contact the authors regarding this work.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details
## Acknowledgments
* Biomedical Engineering Laboratory, Dept. of Electronic and Telecommunication Eng., University of Moratuwa, Sri Lanka.
## References
[1] M. V. Perera and A. De Silva, "A Joint Convolutional and Spatial Quad-Directional LSTM Network for Phase Unwrapping," ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 4055-4059, doi: 10.1109/ICASSP39728.2021.9414748.
Project based on the cookiecutter data science project template. #cookiecutterdatascience