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FRIST learning accompanies the following publications:

1. "FRIST — flipping and rotation invariant sparsifying transform learning and applications", International Inverse Problems (IVP), 2017. IVP 2017, PDF available

2. "Learning Flipping and Rotational Invariant Sparsifying Transform", Proc. IEEE International Conference on Image Processing (ICIP), 2016. ICIP 2016, PDF available, Poster

FRIST is a formulation and methodology for learning a Flipping and Rotation Invariant Sparsifying Transform, and simultaneously clusters the data via their directional orientation.

The FRIST package includes (1) a collection of the FRIST Matlab functions, and (2) example demo data used in the FRIST paper including image denoising, and MRI reconstruction.

You can download our other software packages at: My Homepage and Transform Learning Site.

Paper

In case of use, please cite our publications:

1. B. Wen, S. Ravishankar, and Y. Bresler, “FRIST Flipping and Rotational Invariant Sparsifying Transform Learning and Applications,” Inverse Problems (IVP), vol. 33, no. 7, 2017.

@article{wen2017frist,
  title={{FRIST} — flipping and rotation invariant sparsifying transform learning and applications},
  author={Wen, Bihan and Ravishankar, Saiprasad and Bresler, Yoram},
  journal={Inverse Problems},
  volume={33},
  number={7},
  pages={074007},
  year={2017},
  publisher={IOP Publishing}
}

2. B. Wen, S. Ravishankar, and Y. Bresler. “Learning flipping and rotation invariant sparsifying transforms," IEEE International Conference on Image Processing (ICIP), pp. 3857-3861, 2016.

@inproceedings{wen2016learning,
  title={Learning flipping and rotation invariant sparsifying transforms},
  author={Wen, Bihan and Ravishankar, Saiprasad and Bresler, Yoram},
  booktitle={Image Processing (ICIP), 2016 IEEE International Conference on},
  pages={3857--3861},
  year={2016},
  organization={IEEE}
}

All codes are subject to copyright and may only be used for non-commercial research. In case of use, please cite our publication.

Contact Bihan Wen ([email protected]) for any questions.

The development of this software was supported in part by the National Science Foundation (NSF) under grants CCF 06-35234 and CCF 10-18660.