denoisePatches: Denoising of image patches

In MBCbook: Companion Package for the Book "Model-Based Clustering and Classification for Data Science"

Denoising of image patches

Description

Denoising of image patches based on the clustering of patches.

Usage

denoisePatches(Y,out,P,sigma=10)

Arguments

Y	a data frame containing as rows the image patches to denoise
out	the mixmodCluster object that contains mixture parameters
P	the posterior probabilities that patches belong to the clusters
sigma	the noise standard deviation

Value

A data fame of the denoised patches is returned.

Note

C. Bouveyron & J. Delon

Examples

Im = diag(16) 
ImNoise = Im + rnorm(256,0,0.1)
X = imageToPatch(ImNoise,4)
out = mixmodCluster(X,10,model=mixmodGaussianModel(family=c("spherical")))
res = mixmodPredict(X,out@bestResult)
Xdenoised = denoisePatches(X,out,P = res@proba,sigma = 0.1) 
ImRec = reconstructImage(Xdenoised,16,16)
oldpar <- par(no.readonly = TRUE)
par(mfrow=c(1,3))
imshow(Im); imshow(ImNoise); imshow(ImRec)
par(oldpar)

MBCbook documentation built on June 24, 2024, 9:08 a.m.