redR: REgularization by Denoising (RED)
Regularization by Denoising uses a denoising engine to solve
many image reconstruction ill-posed inverse problems. This is a R
implementation of the algorithm developed by Romano et.al. (2016) <arXiv:1611.02862>. Currently,
only the gradient descent optimization framework is implemented. Also,
only the median filter is implemented as a denoiser engine. However,
(almost) any denoiser engine can be plugged in. There are currently available
3 reconstruction tasks: denoise, deblur and super-resolution. And again,
any other task can be easily plugged into the main function 'RED'.
| Version: |
1.0.1 |
| Depends: |
R (≥ 3.4.0), imager |
| Published: |
2018-09-03 |
| Author: |
Adriano Passos [aut, cre] |
| Maintainer: |
Adriano Passos <adriano.utfpr at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
redR results |
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