redR (version 1.0.1)

RED: RED: Regularization by Denoising

Description

RED: Regularization by Denoising

REgularization by Denoising

Usage

RED(y, x0 = NULL, lambda = 1, sigma = 1, functional = "SR",
  engine = "MF", niter = 50, step = NULL, tol = 0.001, args = NULL)

Arguments

y

cimg object with the observed frame(s)

x0

initial guess for the output image, if NULL an educated guess will be used. If a custom functional is provided this cant be NULL

lambda, sigma

numeric indicating the regularization parameters

functional

character with the optimization task or function with the functional to be used

engine

character indicating the denoised engine or function with the denoiser engine to be used

niter

numeric indicating the maximum number of iterations

step

numeric indicating the step size (if NULL an optimal step size will be used)

tol

numeric indicating the stopping criteria. The algorithm will stop when step < tol. Default = 0.001

args

arguments to be passed implicitly to H HT and f

Examples

# NOT RUN {
im <- lenna
y <- degrade(im, noise = 0.05)
x <- RED(y, sigma = 1, lambda = 5, functional = 'DN', niter = 50)
par(mfrow = c(1,2), mar = c(0,0,2,0)+0.1)
plot(y, interp = FALSE, axes = FALSE, main = 'Degraded im')
mtext(paste(round(PSNR(im, y),2), 'dB'), side = 1, line = -2)
plot(x, interp = FALSE, axes = FALSE, main = 'Restored im')
mtext(paste(round(PSNR(im, x),2), 'dB'), side = 1, line = -2)

# }
# NOT RUN {
im <- cameraman
y <- degrade(im, blur = 5)
y<- isoblur(im, 3, gaussian = TRUE)
x <- RED(y, sigma = 1, lambda = 4, functional = 'DB', niter = 1500)
par(mfrow = c(1,2), mar = c(0,0,2,0)+0.1)
plot(y, interp = FALSE, axes = FALSE, main = 'Degraded image')
mtext(paste(round(PSNR(im, y),2), 'dB'), side = 1, line = -2)
plot(x, interp = FALSE, axes = FALSE, main = 'Restored image')
mtext(paste(round(PSNR(im, x),2), 'dB'), side = 1, line = -2)

im <- cameraman
L = 2
s <- cbind(c(0,1,2,-2,1,3,-1,-3,-1), c(0,-1,2,1,-2,-3,3,-2,-3))
y <- degrade(im, L = L, s = s, noise = 0.05)
xref <- resize(imsplit(y,'z')[[1]], -100*L, -100*L, interpolation_type = 5)
x <- RED(y, sigma = 1, lambda = 5, functional = 'SR', niter = 50, args = list(scale = L, s=s))
par(mfrow = c(1,2), mar = c(0,0,2,0)+0.1)
plot(xref, interp = FALSE, axes = FALSE, main = 'Bicubic Interpolation')
mtext(paste(round(PSNR(im, xref),2), 'dB'), side = 1, line = -2)
plot(x, interp = FALSE, axes = FALSE, main = 'Super Resolved')
mtext(paste(round(PSNR(im, x),2), 'dB'), side = 1, line = -2)

im0 <- 0.2*pad(cameraman, 256, 'xy')
im1 <- lenna
im2 <- im1 - im0
y1 <- degrade(im1, noise = 0.05)
y2 <- degrade(im2, noise = 0.05)
y0 <- y1 - y2
x0 <- RED(y0, sigma = 1, lambda = 50, functional = 'DN', niter = 100)

par(mfrow = c(1,2), mar = c(0,0,2,0)+0.1)
plot(y0, interp = FALSE, axes = FALSE, main = 'naive')
mtext(paste(round(PSNR(im0, y0),2), 'dB'), side = 1, line = -2)
plot(x0, interp = FALSE, axes = FALSE, main = 'proposed')
mtext(paste(round(PSNR(im0, x0),2), 'dB'), side = 1, line = -2)
# }