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Iterative methods for total variation denoising
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DOI：10.1137/0917016.png)
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Total variation (TV) methods are very effective for recovering ''blocky,'' possibly discontinuous, images from noisy data. A fixed point algorithm for minimizing a TV-penalized least squares functional is presented and compared with existing minimization schemes. A variant of the cell-centered finite difference multigrid method of Ewing and Shen is implemented for solving the (large, sparse) linear subproblems. Numerical results are presented for one- and two-dimensional examples; in particular, the algorithm is applied to actual data obtained from confocal microscopy.
Keyword:
total variation
denoising
image reconstruction
multigrid methods
confocal microscopy
fixed point iteration
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