https://github.com/guaishou74851/dccm
(Nature Communications Engineering 2024) Compressive Confocal Microscopy Imaging at the Single-Photon Level with Ultra-Low Sampling Ratios [PyTorch]
https://github.com/guaishou74851/dccm
compressed-sensing compressive-sampling compressive-sensing computer-vision deep-neural-networks deep-unfolding deep-unrolling image-reconstruction
Last synced: 2 months ago
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(Nature Communications Engineering 2024) Compressive Confocal Microscopy Imaging at the Single-Photon Level with Ultra-Low Sampling Ratios [PyTorch]
- Host: GitHub
- URL: https://github.com/guaishou74851/dccm
- Owner: Guaishou74851
- Created: 2023-05-26T11:35:55.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2025-03-09T07:00:30.000Z (3 months ago)
- Last Synced: 2025-03-09T08:17:40.606Z (3 months ago)
- Topics: compressed-sensing, compressive-sampling, compressive-sensing, computer-vision, deep-neural-networks, deep-unfolding, deep-unrolling, image-reconstruction
- Language: Python
- Homepage: https://nature.com/articles/s44172-024-00236-x
- Size: 793 KB
- Stars: 17
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# (Nature Communications Engineering 2024) Compressive Confocal Microscopy Imaging at the Single-Photon Level with Ultra-Low Sampling Ratios [PyTorch]
[](https://www.nature.com/articles/s44172-024-00236-x) 
Shuai Liu\*, [Bin Chen](https://scholar.google.com/citations?user=aZDNm98AAAAJ)\*, Wenzhen Zou, [Hao Sha](https://scholar.google.com/citations?user=-mqUZ8oAAAAJ), Xiaochen Feng, [Sanyang Han](https://www.sigs.tsinghua.edu.cn/hsy/main.htm), [Xiu Li](https://scholar.google.com/citations?user=Xrh1OIUAAAAJ), Xuri Yao, [Jian Zhang](https://jianzhang.tech/)†, and [Yongbing Zhang](https://scholar.google.com/citations?user=0KlvTEYAAAAJ)†
*Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.*
*School of Electronic and Computer Engineering, Peking University, Shenzhen, China.*
*School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.*
*Center for Quantum Technology Research, School of Physics, Beijing Institute of Technology, Beijing, China.*
\* Equal contribution † Corresponding authors
Accepted for publication in [Communications Engineering](https://www.nature.com/commseng/) (Nature Communications) 2024.
⭐ If DCCM is helpful to you, please star this repo. Thanks! 🤗
## 📝 Abstract
Laser-scanning confocal microscopy serves as a critical instrument for microscopic research in biology. However, it suffers from low imaging speed and high phototoxicity. Here we build a novel deep compressive confocal microscope, which employs a digital micromirror device as a coding mask for single-pixel imaging and a pinhole for confocal microscopic imaging respectively. Combined with a deep learning reconstruction algorithm, our system is able to achieve high-quality confocal microscopic imaging with low phototoxicity. Our imaging experiments with fluorescent microspheres demonstrate its capability of achieving single-pixel confocal imaging with a sampling ratio of only approximately 0.03% in specific sparse scenarios. Moreover, the deep compressive confocal microscope allows single-pixel imaging at the single-photon level, thus reducing the excitation light power requirement for confocal imaging and suppressing the phototoxicity. We believe that our system has great potential for long-duration and high-speed microscopic imaging of living cells.
## 🍭 Overview
## ⚙ Environment
```shell
torch.__version__ == '2.2.1+cu121'
numpy.__version__ == '1.24.4'
skimage.__version__ == '0.21.0'
```
## 📚 Data and Pretrained Model Weights
Download the [data](https://drive.google.com/file/d/1FCVwqjb8_J-yTc47t1E0mF8TlM-NdMp5/view) and [pretrained model weights](https://drive.google.com/file/d/1tHohEMx35Dg5qh8X-15CQesx6Q0mDTpv/view). Unzip the files into `./data` and `./code/weight` directories, respectively.
The paths of all files should be:
```
.
├── README.md
├── code
│ ├── model.py
│ ├── test.py
│ ├── test.sh
│ ├── train.py
│ ├── train.sh
│ ├── utils.py
│ └── weight
│ ├── f-actin
│ │ └── layer_9_f_128
│ │ └── net_params_30000.pkl
│ ├── flureoscent_microsphere
│ │ └── layer_9_f_128
│ │ └── net_params_30000.pkl
│ ├── nucleus
│ │ └── layer_9_f_128
│ │ └── net_params_30000.pkl
│ └── potato_tuber
│ └── layer_9_f_128
│ └── net_params_30000.pkl
├── data
│ ├── A_128.npy
│ ├── A_32.npy
│ ├── f-actin
│ │ ├── test_X.npy
│ │ ├── test_X_WF.npy
│ │ ├── test_Y128.npy
│ │ ├── test_Y32.npy
│ │ ├── train_X.npy
│ │ ├── train_X_WF.npy
│ │ ├── train_Y128.npy
│ │ └── train_Y32.npy
│ ├── flureoscent_microsphere
│ │ ├── test_X.npy
│ │ ├── test_X_WF.npy
│ │ ├── test_Y128.npy
│ │ ├── test_Y32.npy
│ │ ├── train_X.npy
│ │ ├── train_X_WF.npy
│ │ ├── train_Y128.npy
│ │ └── train_Y32.npy
│ ├── nucleus
│ │ ├── test_X.npy
│ │ ├── test_X_WF.npy
│ │ ├── test_Y128.npy
│ │ ├── test_Y32.npy
│ │ ├── train_X.npy
│ │ ├── train_X_WF.npy
│ │ ├── train_Y128.npy
│ │ └── train_Y32.npy
│ └── potato_tuber
│ ├── test_X.npy
│ ├── test_X_WF.npy
│ ├── test_Y128.npy
│ ├── test_Y32.npy
│ ├── train_X.npy
│ ├── train_X_WF.npy
│ ├── train_Y128.npy
│ └── train_Y32.npy
└── figs
└── overview.png
```
## ⚡ Test
```shell
cd code
python test.py --data_type=nucleus
python test.py --data_type=flureoscent_microsphere
python test.py --data_type=f-actin
python test.py --data_type=potato_tuber
```
The reconstructed images will be in `./code/result`.
## 🔥 Train
```shell
cd code
python train.py --data_type=nucleus
python train.py --data_type=flureoscent_microsphere
python train.py --data_type=f-actin
python train.py --data_type=potato_tuber
```
The log and model files will be in `./code/log` and `./code/weight`, respectively.
## 🎓 Citation
If you find the code helpful in your research or work, please cite the following paper:
```latex
@article{liu2024compressive,
title={Compressive confocal microscopy imaging at the single-photon level with ultra-low sampling ratios},
author={Liu, Shuai and Chen, Bin and Zou, Wenzhen and Sha, Hao and Feng, Xiaochen and Han, Sanyang and Li, Xiu and Yao, Xuri and Zhang, Jian and Zhang, Yongbing},
journal={Communications Engineering},
volume={3},
number={1},
pages={88},
year={2024},
publisher={Nature Publishing Group UK London}
}
```