This library provides functions for sparse coding and dictionary learning, together with miscellaneous support functions for signal and image processing with sparse representations. The sparse coding algorithms are based on the ADMM framework; while similar codes for some of these functions can be found elsewhere, those provided here include enhancements that are not present in other publicly available codes.

There are two versions of SPORCO, one implemented in Matlab, and the other in Python (the Matlab version is indicated by an 'M' in the version number). Note that the Python version is under far more active development, and supports a number of features that the Matlab version does not. Matlab users are therefore encouraged to make use of the Python version of the library. A number of useful resources are available are available for those who decide to do so:

• NumPy for Matlab users
• Matlab to Python Migration Guide
• NumPy for MATLAB users

Matlab Version

The Matlab version is available here: download sporco-m0.0.9.tar.gz (gzipped tar file, 636k). Please read the instructions in the ReadMe file in the distribution before using the library.

Python Version

The Python version is available on GitHub and on PyPI. The current release can be installed using the source package available from PyPI or by doing "pip install sporco" (assuming that pip is installed and the command is issued with root privileges). Documentation is available at ReadTheDocs.

The most significant contribution of this library is the inclusion of implementations of recent efficient algorithms for convolutional sparse coding and dictionary learning. These algorithms are described in the papers

• Brendt Wohlberg, "Efficient Convolutional Sparse Coding", in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (Florence, Italy), pp. 7173-7177, May 2014
• Brendt Wohlberg, "Efficient Algorithms for Convolutional Sparse Representations", IEEE Transactions on Image Processing, vol. 25, no. 1, pp. 301-315, Jan 2016
• Brendt Wohlberg, "Convolutional Sparse Representation of Color Images", in Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), (Santa Fe, NM, USA), Mar 2016
• Brendt Wohlberg, "Convolutional Sparse Representations as an Image Model for Impulse Noise Restoration", in Proceedings of the IEEE Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), (Bordeaux, France), Jul 2016
• Brendt Wohlberg, "Boundary Handling for Convolutional Sparse Representations", in Proceedings of IEEE International Conference on Image Processing (ICIP), (Phoenix, USA), Sep 2016
• Cristina Garcia-Cardona and Brendt Wohlberg, "Subproblem Coupling in Convolutional Dictionary Learning", in Proceedings of IEEE International Conference on Image Processing (ICIP), (Beijing, China), Sep 2017
• Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg and Wotao Yin, "Online Convolutional Dictionary Learning", in Proceedings of IEEE International Conference on Image Processing (ICIP), (Beijing, China), Sep 2017
• Brendt Wohlberg, "Convolutional Sparse Representations with Gradient Penalties", in Proceedings of IEEE International Conference on Image Processing (ICIP), (Calgary, Alberta, Canada), Apr 2018
• Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg and Wotao Yin, "First and Second Order Methods for Online Convolutional Dictionary Learning", SIAM Journal on Imaging Sciences, 2018
• Cristina Garcia-Cardona and Brendt Wohlberg, "Convolutional Dictionary Learning: A Comparative Review and New Algorithms", IEEE Transactions on Computational Imaging, 2018
• Erik Skau and Brendt Wohlberg, "A Fast Parallel Algorithm for Convolutional Sparse Coding", in Proceedings of the IEEE Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), (Aristi, Greece), Jun 2018

This library has been used in a variety of signal and image processing applications of convolutional sparse coding methods, including

• Brendt Wohlberg, "Endogenous Convolutional Sparse Representations for Translation Invariant Image Subspace Models", in Proceedings of IEEE International Conference on Image Processing (ICIP), (Paris, France), pp. 2859-2863, Oct 2014
• Ping-Keng Jao, Yi-Hsuan Yang and Brendt Wohlberg, "Informed Monaural Source Separation of Music based on Convolutional Sparse Coding", in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (Brisbane, Australia), pp. 236-240, Apr 2015
• Diego Carrera, Giacomo Boracchi, Alessandro Foi and Brendt Wohlberg, "Detecting Anomalous Structures by Convolutional Sparse Models", in The International Joint Conference on Neural Networks (IJCNN), (Killarney, Ireland), pp. 1--8, Jul 2015
• Andrea Cogliati, Zhiyao Duan and Brendt Wohlberg, "Piano Music Transcription with Fast Convolutional Sparse Coding", in Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, (Boston, MA, USA), pp. 1-6, Sep 2015
• Ping-Keng Jao, Li Su, Yi-Hsuan Yang and Brendt Wohlberg, "Monaural Music Source Separation using Convolutional Sparse Coding", IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2016
• Andrea Cogliati, Zhiyao Duan and Brendt Wohlberg, "Context-dependent Piano Music Transcription with Convolutional Sparse Coding", IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2016
• Andrea Cogliati, Zhiyao Duan and Brendt Wohlberg, "Piano Transcription with Convolutional Sparse Lateral Inhibition", Signal Processing Letters, 2017

License

This library was developed at Los Alamos National Laboratory, and has been approved for public release under the approval number LA-CC-14-057. It is made available under the terms of the BSD 3-Clause License.