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

Downloads:

Reference manual: redR.pdf
Package source: redR_1.0.1.tar.gz
Windows binaries: r-devel: redR_1.0.1.zip, r-release: redR_1.0.1.zip, r-oldrel: redR_1.0.1.zip
OS X binaries: r-release: redR_1.0.1.tgz, r-oldrel: redR_1.0.1.tgz
Old sources: redR archive

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