Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling (AdaSense)

Noam Elata, Tomer Michaeli, and Michael Elad, Technion - Israel Institute of Technology.

This code implements Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling.

Running the Experiments

Please refer to environment.yml for a list of conda/mamba environments that can be used to run the code.

For the CT experiment, please install the torch-radon package, which can be done with the following command:

pip install --force-reinstall --no-cache-dir https://rosh-public.s3-eu-west-1.amazonaws.com/radon-v2/cuda-11.1/torch-1.8/torch_radon-2.0.0-cp38-cp38-linux_x86_64.whl

Pretrained models

We use pretrained model from SDEdit for the facial image experiment, and our own pre-trained diffusion models for medical images.

Our model checkpoints will be published soon!

Sampling from the model

To use adasense to restore images use the following command:

python main.py --ni --config {CONFIG}.yml --doc {DATASET} -i {IMAGE_FOLDER} --deg adasense

where the following are options

• CONFIG is the name of the config file (see configs/ for a list), including hyperparameters such as batch size and network architectures.
• DATASET is the name of the dataset used, to determine where the checkpoint file is found.
• IMAGE_FOLDER is the name of the folder the resulting images will be placed in (default: images)

As the code is derived from DDRM, the general command to sample from the model for any inverse problem is as follows:

python main.py --ni --config {CONFIG}.yml --doc {DATASET} -i {IMAGE_FOLDER} --timesteps {STEPS} --eta {ETA} --deg {DEGRADATION} 

where the following are options

• CONFIG is the name of the config file (see configs/ for a list), including hyperparameters such as batch size and network architectures.
• DATASET is the name of the dataset used, to determine where the checkpoint file is found.
• IMAGE_FOLDER is the name of the folder the resulting images will be placed in (default: images)
• STEPS controls how many timesteps used in the process.
• ETA is the eta hyperparameter in the paper. (default: 0.85)
• DEGREDATION is the type of degredation allowed. (One of: cs2, cs4, inp, inp_lolcat, inp_lorem, deno, deblur_uni, deblur_gauss, deblur_aniso, sr2, sr4, sr8, sr16, sr_bicubic4, sr_bicubic8, sr_bicubic16 color)

Image Compression

This section implements Zero-Shot Image Compression with Diffusion-Based Posterior Sampling

Coming soon...

References and Acknowledgements

@article{elata2024adaptive,
    title={Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling}, 
    author={Elata, Noam and Michaeli, Tomer and Elad, Michael},
    year={2024},
    journal={arXiv preprint arXiv:2407.08256},
}
@article{elata2024zero,
  title={Zero-Shot Image Compression with Diffusion-Based Posterior Sampling},
  author={Elata, Noam and Michaeli, Tomer and Elad, Michael},
  journal={arXiv preprint arXiv:2407.09896},
  year={2024}
}

This implementation is based on DDRM.