Git Product home page Git Product logo

lrdunet's Introduction

LRDUNet

U-Net based neural network for fringe pattern denoising

This repository is for the LRDUNet model proposed in the following paper:

Javier Gurrola-Ramos, Oscar Dalmau and Teresa E. Alarcón, "U-Net based neural network for fringe pattern denoising", Optics and Lasers in Engineering, vol 149, pp. 106829, 2022, doi: 10.1016/j.optlaseng.2021.106829.

Citation

If you use this paper work in your research or work, please cite our paper:

@article{gurrola2022u,
  title = {U-Net based neural network for fringe pattern denoising},
  journal = {Optics and Lasers in Engineering},
  volume = {149},
  pages = {106829},
  year = {2022},
  issn = {0143-8166},
  doi = {https://doi.org/10.1016/j.optlaseng.2021.106829},
  url = {https://www.sciencedirect.com/science/article/pii/S0143816621002992},
  author = {Javier Gurrola-Ramos and Oscar Dalmau and Teresa Alarcón},
}

LRDUNet

Pre-trained model and datasets

Link to download the pretrained model, and the training and test datasets.

Dependencies

  • Python 3.6
  • Numpy 1.19.2
  • PyTorch 1.8.0
  • torchvision 0.9.0
  • pytorch-msssim 0.2.1
  • ptflops 0.6.4
  • tqdm 4.49.0
  • scikit-image 0.17.2
  • sewar 0.4.4
  • yaml 0.2.5

Training

Default parameters used in the paper are set in the config.yaml file:

groups: 8
dense covolutions: 2
downsampling: 'Conv2'
residual: true
patch size: 64
batch size: 16
learning rate: 1.e-3
weight decay: 1.e-2
scheduler gamma: 0.5
scheduler step size: 5
epochs: 20

Additionally, you can choose the device, the number of workers of the data loader, and enable multiple GPU use.

To train the model use the following command:

python main_train.py

Test

Place the pretrained model in the './Pretrained' folder. To test the model in the simulated fringe patterns, use the following command:

python main_test_simulated.py

To test the model in the experimetal fringe patterns, use the following command:

python main_test_experimental.py

Results

Results

Contact

If you have any question about the code or paper, please contact [email protected] .

lrdunet's People

Contributors

javiergurrola avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.