Description Usage Arguments Details Value Examples

Use the GSCAD method under dictionary learning framework to learn a proper sized dictionary and denoise image. The noisy image is split into m by m patches and a dictionary with each atom of size m is learned. The final denoised image is reconstruncted on the denoised patches.

1 2 3 | ```
gscad.denoise(I_noise, sigma, D0 = NULL, m, p0, c = 3.7, lambda = 0.05,
maxrun = 20, maxrun_ADMM = 20, err_bnd = 1e-04, err_bnd2 = 1e-04,
rho = 16, cor_bnd = 1, L = NULL)
``` |

`I_noise` |
The image to be denoised. In form of matrix. |

`sigma` |
Noise level. |

`D0` |
Initial dictionary. If D0 specified, m and p0 are not needed, otherwise D0 is evaluated as overcompleted DCT basis using function ODCT(m,p0). Either D0 or (m,p0) needs to be specified. |

`m` |
The size of the small patches to be split. |

`p0` |
Initial size of the dictionary. |

`c, lambda` |
Parameters for GSCAD. |

`maxrun` |
(optional) Maximun number of outer iterations to run. Default is 20. |

`maxrun_ADMM` |
(optional) Maximun number of iterations to run for updating dictionary using ADMM. Default is 20. |

`err_bnd` |
(optional) Stopping criterion for iterations. Default is 1e-4. |

`err_bnd2` |
(optional) Stopping criterion for updating dictionary |

`rho` |
(optional) Parameter for ADMM. Default is 16. |

`cor_bnd` |
(optional) When normalize dictionary, checking if the correlation of any two atoms are above the cor_bnd, one of the atom is removed. Default is 1. |

`L` |
(optional) This parameter controls the maximum number of non-zero elements in each column of sparsecolding A.Default is m. |

See https://arxiv.org/abs/1605.07870

The learned dictionary `dictionary`

, its size `p`

and the denoised image `fitted image`

.

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