/LapCSNet

Officical code of paper "An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios" ICASSP2018

Primary LanguageMATLAB

LapCSNet

  • Officical code of paper "An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios" ICASSP2018
  • Download the paper: https://arxiv.org/pdf/1804.04970.pdf

Framework of LapCSNet

image

Requirements

How to Run

Training

  • Copying the function in +dagnn folder to your Matconvnet location <MatconvNet>\matlab\+dagnn
  • Preparing the training data. (T91 and BSDS200 are included in our repo)
  • Train the LapCSNet, run the code train_LapCSN(0.1, 2, 0);
The first param is CS subrate
The second param is the number of conv layers in each pyramid level
The third param is gpu setting. (0 is CPU, 1 is GPU)

Testing

  • Preparing the testing data. (Set5 and Set14 are included in our repo)
  • Test the LapCSNet, run the code test_LapCSN_main(100, 200)
The first param is start epoch for testing model
The second param is end epoch for testing model

Experimental Results

  • Subjective results

image

  • Objective results

image

Additional instructions

  • For training data, you can choose any dataset by yourself.
  • When subrate<=0.25, the laplacian structure can be used.
  • If you like this repo, Star or Fork to support my work. Thank you.
  • If you have any problem, please email wxcui@hit.edu.cn

Citation

  • If you find the code is useful in your research, please cite:
@article{Cui2018An,
  title={An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios},
  journal={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  author={Cui, Wenxue and Xu, Heyao and Gao, Xinwei and Zhang, Shengping and Jiang, Feng and Zhao, Debin},
  year={2018},
}

Acknowledgments

This code is built based on the repo https://github.com/phoenix104104/LapSRN