SPORCO: A Python package for standard and convolutional sparse representations

SParse Optimization Research COde (SPORCO) is an open-source Python package for solving optimization problems with sparsity-inducing regularization, consisting primarily of sparse coding and dictionary learning, for both standard and convolutional forms of sparse representation. In the current version, all optimization problems are solved within the Alternating Direction Method of Multipliers (ADMM) framework. SPORCO was developed for applications in signal and image processing, but is also expected to be useful for problems in computer vision, statistics, and machine learning.

[1]  Emmanuel J. Candès,et al.  A Singular Value Thresholding Algorithm for Matrix Completion , 2008, SIAM J. Optim..

[2]  Brendt Wohlberg,et al.  Subproblem coupling in convolutional dictionary learning , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[3]  Gordon Wetzstein,et al.  Fast and flexible convolutional sparse coding , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Anders P. Eriksson,et al.  Fast Convolutional Sparse Coding , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Brendt Wohlberg,et al.  Convolutional sparse representation of color images , 2016, 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI).

[6]  Brendt Wohlberg,et al.  Efficient convolutional sparse coding , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  Brendt Wohlberg,et al.  Efficient Algorithms for Convolutional Sparse Representations , 2016, IEEE Transactions on Image Processing.

[8]  Gene H. Golub,et al.  Generalized cross-validation as a method for choosing a good ridge parameter , 1979, Milestones in Matrix Computation.

[9]  Graham W. Taylor,et al.  Deconvolutional networks , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Stefano Alliney,et al.  Digital filters as absolute norm regularizers , 1992, IEEE Trans. Signal Process..

[11]  Terrence J. Sejnowski,et al.  Coding Time-Varying Signals Using Sparse, Shift-Invariant Representations , 1998, NIPS.

[12]  Jean Ponce,et al.  Sparse Modeling for Image and Vision Processing , 2014, Found. Trends Comput. Graph. Vis..

[13]  Brendt Wohlberg,et al.  ADMM Penalty Parameter Selection by Residual Balancing , 2017, ArXiv.

[14]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[15]  C. Stein Estimation of the Mean of a Multivariate Normal Distribution , 1981 .

[16]  Brendt Wohlberg,et al.  Boundary handling for convolutional sparse representations , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[17]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[18]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[19]  Brendt Wohlberg Convolutional sparse representations as an image model for impulse noise restoration , 2016, 2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP).