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Instructions for SR using multiple dictionary learning code, Release 1
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Author: Pulak Purkait (pulak_r@isical.ac.in)
        [Used SPAMS code for Sparse dictionary learning]
		SPAMS : http://spams-devel.gforge.inria.fr/
Date: October 10, 2012
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Website for updates: http://www.isical.ac.in/~vlrg/?q=node/10


This code is provided as is, with no guarantees except that 
bugs are almost surely present.  Published reports of research 
using this code (or a modified version) should cite the 
article that describes the algorithm: 

[Purkait12]	P. Purkait, B. Chanda (2012).  Image upscaling using multiple 
dictionaries of natural image patches, ACCV 2012, November 2012.  

Comments and bug reports are welcome.  Email to pulak_r@isical.ac.in, 
for any query or improvements.   

You are free to modify, extend or distribute this code, provided  
you cite the above article. 

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This is the MATLAB implementation of Purkait12. To make this code workable you 
must have SPAMS executables available at http://spams-devel.gforge.inria.fr/
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Download the SPAMS executables from the above website and replace SPAMS2.1/mkl64 directory. 
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This code is built on Linux system, for windows you need to make appropriate 
changes (minor, probably the directory paths). Run the .sh file 
(run_jvm_matlab_Sr_Topic.sh) on terminal. Check the Matlab installation path 
on the sh file. If everything goes fine then it would enter to the matlab 
terminal without any warning. 

[pulak@ecsu SR_Topic_Multi_Dict]$ ./run_jvm_matlab_SR_Topic.sh 

Now to learn the topics and train the dictionaries corresponding to each topic 
run the Parametre_Training.m code. 

>>Parametre_Training

To upsample an image run Test_Upscale.m

>>Test_Upscale

If you are getting stuck anywhere put a mail on pulak_r@isical.ac.in

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