Compressed Sensing with Deep Image Prior

Here are a few example results:

MNIST at 75 measurements	X-ray at 2000 measurements

Preliminaries

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1. Clone the repository

   $ git clone https://github.com/anon-iclr/csdip-iclr.git
   $ cd csdip-iclr

   Please run all commands from the root directory of the repository, i.e from csdip-iclr/

2. Install requirements

   $ pip install -r requirements.txt

Plotting reconstructions with existing data

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1. Open jupyter notebook of plots

   $ 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

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1. Execute the baseline command

   $ 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:

   $ python comp_sensing.py --DATASET xray --NUM_MEASUREMENTS 2000 4000 8000 --ALG csdip dct

Running CS-DIP on a new dataset

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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.
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

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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.