cocolib / light field suite Code

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**Description:**

COCOLIB is a library for variational image analysis, specifically continuous global optimization. It focuses on the minimization of functionals of the type

E(u) = J(u) + F(u)

where u is an image (i.e. vector-valued function on an interval), J is a convex and closed regularizer, and F a closed and convex data term.


**The following models will be part for the initial release:**

- Total Variation regularizer:
    - Linear data term (segmentation) [1]
    - Denoising data term (ROF model) [2,3]
    - Deblurring data term [1]
    - Inpainting data term (weighted ROF) [1]

- Vectorial Total Variation regularizer [4 and 1,3]:
    - Denoising data term (Vectorial ROF and L^1)
    - Deblurring data term
    - Inpainting data term

- Total Curvature regularizer [7 and 1]:
    - Denoising data term (Vectorial ROF)
    - Deblurring data term
    - Inpainting data term

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**The following models are planned for the release after that:**

- Dataterms for continuous multilabel problems with a variety of regularizers [5,6].


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** Documentation **
[Documentation is available in the project Wiki](https://sourceforge.net/p/cocolib/wiki/Home/)

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**Authors:**
[[project_admins]]

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**References:**

Algorithm descriptions can be found in a variety of research papers, including

[1] Beck and Teboulle, "Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems", IEEE Transactions on Image Processing 2009.

[2] Chambolle, "Total variation minimization and a class of binary MRF models", EMMCVPR 2005.

[3] Chambolle and Pock, "A first-order primal-dual algorithm for convex problems with
applications to imaging", preprint 2010.

[4] Goldluecke, Strekalovskiy and Cremers, "The natural vectorial total variation which arises from geometric measure theory". CVPR 2010 and extended Technical Report, 2011 (available upon request).

[5] Goldluecke and Cremers, "Convex Relaxation for Multilabel Problems with Product Label Spaces", ECCV 2010.

[6] Goldluecke, Strekalovskiy and Cremers, "Tight Convex Relaxations for Vector-Valued Labeling". ICCV 2011 and extended Technical Report, 2011 (available upon request).

[7] Goldluecke and Cremers, "Introducing Total Curvature for Image Processing", ICCV 2011.