Description: he K-SVD is a new algorithm for training dictionaries for linear
representation of signals. Given a set of signals, the K-SVD tries to
extract the best dictionary that can sparsely represent those signals.
Thorough discussion concerning the K-SVD algorithm can be found in:
"The K-SVD: An Algorithm for Designing of Overcomplete Dictionaries for
Sparse Representation", written by M. Aharon, M. Elad, and A.M. Bruckstein,
and appeared in the IEEE Trans. On Signal Processing, Vol. 54, no. 11,
pp. 4311-4322, November 2006.
To Search:
File list (Check if you may need any files):
KSVD_Matlab_ToolBox
...................\KSVD.m
...................\KSVD_NN.m
...................\MOD.m
...................\NN_BP.m
...................\OMP.m
...................\OMPerr.m
...................\README.txt
...................\barbara.png
...................\boat.png
...................\demo1.m
...................\demo2.m
...................\demo3.m
...................\denoiseImageDCT.m
...................\denoiseImageGlobal.m
...................\denoiseImageKSVD.m
...................\displayDictionaryElementsAsImage.asv
...................\displayDictionaryElementsAsImage.m
...................\gererateSyntheticDictionaryAndData.m
...................\globalTrainedDictionary.mat
...................\house.png
...................\lena.png
...................\my_im2col.m
...................\peppers256.png
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