Metadata-Version: 2.1
Name: sparselandtools
Version: 1.0.1
Summary: A package for sparse representations and dictionary learning
Home-page: https://github.com/fubel/py-sparselandtools
Author: Fabian Herzog
Author-email: fabian.herzog.dev@gmail.com
License: UNKNOWN
Description: # Sparselandtools
        
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        Sparselandtools is a Python 3 package that provides implementations for
        sparse representations and dictionary learning. In particular, it
        includes implementations for
        
        **For Sparse Representations:**
        * Matching Pursuit
        * Orthogonal Matching Pursuit
        * Thresholding Pursuit
        * Basis Pursuit
        
        **For Dictionaries in General:**
        * Mutual Coherence
        * DCT Dictionary
        * Haar Dictionary
        * Overcomplete DCT Dictionary
        * Visualization Tools for Dictionaries
        
        **For Dictionary Learning:**
        * K-SVD Algorithm
        * Approximate K-SVD Algorithm
        
        **For Application:**
        * Approximate K-SVD Image Denoiser
        
        **Note:** I did this project mainly to generate plots for my Master's thesis.
        Some of the implementations are more *educational* than *efficient*. If you want
        to learn more about sparse representations and dictionary learning using Python,
        or use dictionary learning algorithms in small dimensions this ,package is for you.
        If you want to use these functions for industrial applications, you should have a
        look at more efficient C++-based implementations:
        
        * [The Efficient K-SVD Algorithm by Rubinstein](http://www.cs.technion.ac.il/~ronrubin/software.html)
        * [The Efficient K-SVD Denoiser by Lebrun](https://github.com/npd/ksvd)
        
        
        ## Getting Started
        
        Sparselandtools is available as a PyPI package. You can install it using
        
        ```
        pip install sparselandtools
        ```
        
        ![DCT and Haar Dictionary](https://snag.gy/h7Il2j.jpg)
        
        The following code creates a redundant (=overcomplete) DCT-II dictionary
        and plots it. It also prints out the dictionaries mutual coherence.
        
        ```python
        from sparselandtools.dictionaries import DCTDictionary
        import matplotlib.pyplot as plt
        
        # create dictionary
        dct_dictionary = DCTDictionary(8, 11)
        
        # plot dictionary
        plt.imshow(dct_dictionary.to_img())
        plt.show()
        
        # print mutual coherence
        print(dct_dictionary.mutual_coherence())
        ```
        
        More examples can be found in the corresponding Jupyter Notebook.
        
        
        ## Contribute
        
        There are a lot of algorithms based on sparse representations and
        dictionary learning that are not (yet) included in this package. These
        include - among others:
        
        * The Double Sparsity Method
        * Trainlets
        * Denoiser with Method Noise Post Processing
        * Boosted Denoiser with Patch Disagreement
        
        and much more. It would also be interesting to see more applications in this package.
        Currently, this package only provides the K-SVD image denoiser [based on the work of
        Aharon and Elad](https://www.egr.msu.edu/~aviyente/elad06.pdf). K-SVD can also
        be used in many other applications, such as face recognition. Furthermore,
        it would be nice to have GPU-versions of all the algorithms available as well.
        
        If you want to see a specific algorithm in this package,
        please consider opening a feature request here on Github. If you have written
        an algorithm that you think would fit into this package, please fork this
        repository, add your algorithm and file a pull request. If something
        doesn't work as expected, please open an issue.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
