readme

.gitignore

@@ -6,3 +6,7 @@
 
 # datasets
 synthetic_bead_3DM/
+
+# models
+*.pth
+*.pt

README.md

@@ -1,85 +1,59 @@
 <h2 align="center">3DM: Deep decomposition and deconvolution microscopy</h2>
 
 <p align="center">
-<img width="60%" src="src/Visualization1_resize75.gif">
+<img width="55%" src="src/Visualization1_resize.gif">
 </p>
 <h6 align="center">Demo video acquired using 3DM.
 
- Compression is applied. See Supplementary video 1.
+ Significant compression is applied. [Supplementary video 1](TODO) is the video before compression.
  </h6>
 
-Official source codes for "3DM: Deep decomposition and deconvolution microscopy", Optics Express.
-
-
-## Running BEAR
 
-You can run BEAR which does Robust PCA by very simple code.
-Just download or clone this repository and use the code below.
-`--D` refers the name of the data and `--d` enables the default settings of several hyperparameters.
+## [Paper](TODO)
 
-```bash
-python scripts/run_BEAR.py --D hall --d True
-```
-
-Results will be saved in `results/BEAR/hall_(Y/L/S)` directory.
+Official source codes for "3DM: Deep decomposition and deconvolution microscopy", Optics Express.
 
-## Hardware specification & Requirements
+**ABSTRACT**
 
-**Hardware specification**
+## Requirements
 
 ```markdown
-OS : Ubuntu 18.0.4
-CPU : Intel Xeon Silver 4214 CPU @ 2.20GHz
-GPU : GeForce RTX 2080 Ti 11GB
-RAM : 128GM
+python==3.7.6
+torch==1.5.0
+skimage==0.16.2
 ```
 
-**Requirements (May be okay with different versions.)**
-```markdown
-torch==1.7.0
-numpy==1.19.4
-psutil==5.7.2
-
-scipy==1.5.3
-scikit-image==0.17.2
-mat73==0.46
-```
-
-## Reproduce the paper
-
-We have already added small surveillance video, and calcium imaging data of mouse which are widely used in RPCA paper.
-You can first try these data.
-Calcium imaging data of zebrafish we have used in paper is in this [Google Drive](https://drive.google.com/file/d/115lCnwIVU0TtKedQ_31FDaOG8wksGmG9/view?usp=sharing) (~10GB).
-Download, unzip, and move .tif and .mat files inside the data folder. 
-
-1. Figure 3. Phase diagram.
+## Demo: BEAR + Deconvolution with pretrained weight
 ```bash
-python scripts/phase_diagram.py --D None --d None
+./run_jupyter.sh
 ```
-
-2. Table 1. and Figure 4. Decomposition of the zebrafish caclium imaging data.
+This notebook lets you:
+- Download a portion (t=1~50, size=**TODO**) of calcium imaging data acquired with our wide-field microscope.
+- Do unsupervised low rank and sparse decomposition using BEAR.
+- Load the pretrained 3-D deconvolution network.
+- Do deconvolution for each 50 sparse volumes.
+- Visualize the results.
+
+## Train
+The following command starts training the 3-D deconvolution network:
 ```bash
-python scripts/run_Greedy_BEAR.py --D zebrafish_150 --d True
-python scripts/run_Greedy_BEAR.py --D zebrafish_1000 --d True
+python codes/train.py
 ```
-Due to the size of data, loading files itself does take long time (Minutes in HDD).
-And for 1000 length video, about 100GB of RAM is required.
 
-3. Figure 5. and Figure 6. Cascaded BEAR for analysis of neuronal activity.
-```bash
-python scripts/run_Cascaded_BEAR.py --D demoMovie --d True
-python scripts/run_Cascaded_BEAR.py --D spinning_confocal --d True
+## Test
+The following commands do deconvolution after loading the pretrained weight.
+```markdown
+python codes/eval_simulation.py --exp_name 3DM --epoch 26000
+python codes/eval_3DM_video.py --exp_name 3DM --epoch 26000
 ```
-For accuracy, number of epochs in default settings of Cascaded BEAR is very large.
-Can be observed that loss value does not decrease actually after small number of epochs.
-You can reduce the `args.epoch` if you want.
+- `eval_simulation.py` do deconvolution for simulated wide-field data. (See Section 3.1)
+- `eval_3DM_video.py` do deconvolution for wide-field data acquired with our microscope. (See Section 3.2)
+
 
 ## Citation
+**TODO**
 ```markdown
-@inproceedings{han2021efficient,
-  title={Efficient neural network approximation of robust PCA for automated analysis of calcium imaging data},
-  author={Seungjae Han and Eun-Seo Cho and Inkyu Park and Kijung Shin and Young-Gyu Yoon},
-  booktitle={International Conference on Medical Image Computing and Computer Assisted Intervention},
-  year={2021}
+@article{cho2021deep,
+  title={3DM: Deep decomposition and deconvolution microscopy},
 }
 ```