Mercurial > hg > smallbox
changeset 29:a07585dff65c
(none)
| author | idamnjanovic |
|---|---|
| date | Tue, 11 May 2010 12:02:16 +0000 |
| parents | 78c1e71cd269 |
| children | dc6aaa255836 |
| files | README.txt |
| diffstat | 1 files changed, 47 insertions(+), 29 deletions(-) [+] |
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--- a/README.txt Mon May 10 14:59:59 2010 +0000 +++ b/README.txt Tue May 11 12:02:16 2010 +0000 @@ -1,12 +1,19 @@ -SMALLbox Version 0.5 +SMALLbox Version 1.0 beta -31st July, 2009 +11th May, 2010 + +Version 1.0 beta of SMALLbox is the release candidate that is distributed +for testing and bug fixing purposes. Please send all bugs, requests and suggestions to: + +ivan.damnjanovic@elec.qmul.ac.uk --------------------------------------------------------------------------- -Copyright (2009): Ivan Damnjanovic, Matthew Davies +Copyright (2010): Ivan Damnjanovic, Matthew Davies + Centre for Digital Music, + Queen Mary University of London SMALLbox is distributed under the terms of the GNU General Public License 3 @@ -38,21 +45,19 @@ --------------------------------------------------------------------------- -Version 0.5 of SMALLbox will download and install the following existing toolboxes +will download and install the following existing toolboxes related to Sparse Representations, Compressed Sensing and Dictionary Learning: -Sparco: http://www.cs.ubc.ca/labs/scl/sparco/ +- SPARCO (v.1.2) - set of sparse representation problems[5] +- SparseLab (v.2.1) - set of sparse solvers [1] +- Sparsify (v.0.4) - set of greedy and hard thresholding algorithms [2] +- SPGL1 (v.1.7) - large-scale sparse reconstruction solver [3] +- GPSR (v.6.0) - Gradient projection for sparse reconstruction [4] +- KSVD-box (v.13) and OMP-box (v.10) - dictionary learning [6] +- KSVDS-box (v.11) and OMPS-box (v.1) - sparse dictionary learning [7]. -SPGL1: http://www.cs.ubc.ca/labs/scl/spgl1/ -SparseLab: http://sparselab.stanford.edu/ - -Sparsify: http://www.see.ed.ac.uk/~tblumens/sparsify/sparsify.html - -GPSR: http://www.lx.it.pt/~mtf/GPSR/ - -OMPbox and KSVDBox: http://www.cs.technion.ac.il/~ronrubin/ IMPORTANT: In order to use SparseLab please register at @@ -65,30 +70,25 @@ IMPORTANT: Because the toolboxes are downloaded automatically, you must have an internet connection to successfully install SMALLbox. - -IMPORTANT: -If you are running Matlab on MAC OSX or Linux, you must start Matlab with the jvm enabled. + +IMPORTANT: +If you are running Matlab on MAC OSX or Linux, you must start Matlab with the jvm enabled. Not doing so, will prevent you being able to unzip the downloaded toolboxes. To install the toolbox run the command "SMALLboxsetup" from the MATLAB command prompt. -Once installed, there are two optional demo functions that can be run, - -SMALL_solver_test.m : - -Example test of solvers from different toolboxes on Sparco compressed sensing problems - -and - -SMALL_solver_test_Audio.m : - -Example test of solvers on Sparco audio source separation problems +Once installed, there are two optional demo functions that can be run: + +Example test of solvers from different toolboxes on Sparco compressed sensing problems and +Example test of dictionary learning techniques on image denoising problems + +Further examples can be found in {SMALLbox root}/examples directory --------------------------------------------------------------------------- For more information on the SMALL Project, please visit the following website: -http://small.inria.fr +http://small-project.eu Contacts: ivan.damnjanovic@elec.qmul.ac.uk @@ -96,4 +96,22 @@ This code is in experimental stage; any comments or bug reports are -very welcome. +very welcome. More information about using SMALLbox will be includied in release version +documenation file. + +References: + +1. Donoho, D., Stodden, V., Tsaig, Y.: Sparselab. 2007, http://sparselab.stanford.edu/ +2. Blumensath, T., Davies, M. E.: Gradient pursuits. + In IEEE Transactions on Signal Processing, vol. 56, no. 6, pp. 2370–2382, June 2008. +3. Berg, E. v., Friedlander, M. P.: Probing the pareto frontier for basis pursuit solutions. + In SIAM Journal on Scientific Computing, vol. 31, no. 2, pp. 890–912, 2008. +4. Figueiredo, M. A. T., Nowak, R. D., Wright, S. J.: Gradient projection for sparse + reconstruction: Application to compressed sensing and other inverse problems. In Journal + of Selected Topics in Signal Processing:Special Issue on Convex Optimization for Signal Processing, December 2007. +5. Berg, E. v., Friedlander, M. P., Hennenfent, G., Herrmann, F., Saab, R., Yilmaz, O.: Sparco: + A testing framework for sparse reconstruction. In ACM Trans. on Mathematical Software, 35(4):1-16, February 2009. +6. Aharon, M., Elad, M., Bruckstein, A. M.: The K-SVD: An algorithm for designing of overcomplete + dictionaries for sparse representation. In IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4311–4322, 2006. +7. Rubinstein, R., Zibulevsky, M. and Elad, M.: Double Sparsity: Learning Sparse Dictionaries + for Sparse Signal Approximation. In IEEE Transactions on Signal Processing, Vol. 58, No. 3, Pages 1553-1564, March 2010.