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
.
1 2 3 4 5 6 7 8 9 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.