# compsensing_dip

**Repository Path**: zhangweizju/compsensing_dip

## Basic Information

- **Project Name**: compsensing_dip
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-07-31
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Compressed Sensing with Deep Image Prior

This repository provides code to reproduce results from the paper: [Compressed Sensing with Deep Image Prior and Learned Regularization](https://arxiv.org/pdf/1806.06438.pdf). 

Here are a few example results:

MNIST at 75 measurements                 | X-ray at 2000 measurements
-----------------------------------------|-----------------------------------------
<img src="https://github.com/davevanveen/compsensing_dip/blob/master/reconstructions/mnist/samp_recons_m75.png" alt="mnist_reconstr" width="400"> | <img src="https://github.com/davevanveen/compsensing_dip/blob/master/reconstructions/xray/samp_recons_x2000.png" alt="mnist_reconstr" width="400">
      

### Preliminaries
---

1. Clone the repository
    ```shell
    $ git clone https://github.com/davevanveen/compsensing_dip.git
    $ cd compsensing_dip
    ```
    Please run all commands from the root directory of the repository, i.e from ```compsensing_dip/```

2. Install requirements
    ```shell
    $ pip install -r requirements.txt
    ```


### Plotting reconstructions with existing data
---
1. Open jupyter notebook of plots
    ```shell
    $ jupyter notebook plot.ipynb
    ```	
2. Set variables in the second cell according to interest, e.g. ```DATASET```, ```NUM_MEASUREMENTS_LIST```, ```ALG_LIST```. Existing supported data is described in the comments.

3. Execute cells to view output.


### Generating new reconstructions on the MNIST, xray, or retinopathy datasets
---
1. Execute the baseline command
	```shell
	$ python comp_sensing.py
	```
	which will run experiments with the default parameters specified in ```configs.json```

2. To generate reconstruction data according to user-specified parameters, add command line arguments according to those available in ```parser.py```. Example:
	```shell
	$ python comp_sensing.py --DATASET xray --NUM_MEASUREMENTS 2000 4000 8000 --ALG csdip dct
	```

### Running CS-DIP on a new dataset
---
1. Create a new directory ```/data/dataset_name/sub/``` which contains your images
2. In ```utils.py```, create a new DCGAN architecture. This will be similar to the pre-defined architectures, e.g. ```DCGAN_XRAY```, but must have output dimension equal to the size of your new images. Output dimension can be changed by adjusting kernel_size, stride, and padding as discussed in the [torch.nn documentation](https://pytorch.org/docs/stable/nn.html#convtranspose2d). 
3. Update ```configs.json``` to set parameters for your dataset. Update ```utils.init_dcgan``` to import/initiate the corresponding DCGAN.
4. Generate and plot reconstructions according to instructions above.

Note: We recommend experimenting with the DCGAN architecture and dataset parameters to obtain the best possible reconstructions.


### Generating learned regularization parameters for a new dataset
---
The purpose of this section is to generate a new (\mu, \Sigma) based on layer-wise weights of the DCGAN. This functionality will be added soon.