Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Alternating Direction Method for Total Variation Denoising   We consider the image denoising problem using total variation (TV)
regularization. This problem can be computationally challenging to solve due to
the non-differentiability and non-linearity of the regularization term. We
propose an alternating direction augmented Lagrangian (ADAL) method, based on a
new variable splitting approach that results in subproblems that can be solved
efficiently and exactly. The global convergence of the new algorithm is
established for the anisotropic TV model. For the isotropic TV model, by doing
further variable splitting, we are able to derive an ADAL method that is
globally convergent. We compare our methods with the split Bregman method
\cite{goldstein2009split},which is closely related to it, and demonstrate their
competitiveness in computational performance on a set of standard test images.

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The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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The categories of this paper are: nucl-th
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The categories of this paper are: physics
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The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: chao-dyn
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: supr-con
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The categories of this paper are: quant-ph