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 (2016) <doi:10.48550/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>
License: GPL-3
NeedsCompilation: no
CRAN checks: redR results


Reference manual: redR.pdf


Package source: redR_1.0.1.tar.gz
Windows binaries: r-prerel:, r-release:, r-oldrel:
macOS binaries: r-prerel (arm64): redR_1.0.1.tgz, r-release (arm64): redR_1.0.1.tgz, r-oldrel (arm64): redR_1.0.1.tgz, r-prerel (x86_64): redR_1.0.1.tgz, r-release (x86_64): redR_1.0.1.tgz
Old sources: redR archive


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