inpainting: image recovery using Lasso regression

In ProxReg: Linear Models for Prediction and Classification using Proximal Operators

image recovery using Lasso regression

Description

predicts the missing pixels in an image using Lasso regression and fills the hole in the image

Usage

inpainting(image,h,stride,i,j,width,height,lambda=0.1,max_iter=50000,
fista=TRUE, verbose=TRUE,ini=0,glmnet=TRUE,noise=TRUE)

Arguments

image	image to be modified, it has to be a 3D array proceed with readImage function from EBImage package
h	size of the patch
stride	stride for the patch
i	row index of the upper left corner of the rectangle
j	column index of the upper left corner of the rectangle
width	width of the rectangle
height	height of the rectangle
lambda	a penalized parameter for the Lasso regression, it is 0.1 by default
max_iter	maximum number of iterations, it is 50000 by default
fista	fista=TRUE: use FISTA algortihm for the pixel prediction
verbose	print the iteration number and the size of the boundary
ini	initial value for the coefficients, default is 0
glmnet	use glmnet package for the Lasso regression
noise	display the image with the hole, it is TRUE by default

Details

inpainting

Value

a 3D array with the hole filled by pixels predicted by Lasso regression

Examples

test_img <- EBImage::readImage(system.file("extdata", "bird.jpg", package = "ProxReg"))
image_repaired <- inpainting(
  test_img, h = 10, stride = 6, i = 160, j = 160, width = 20, height = 20,
  lambda = 0.001, max_iter = 1000, verbose = TRUE, glmnet = TRUE,noise=TRUE)
RGB_repaired<-EBImage::Image(image_repaired,colormode = "Color")

ProxReg documentation built on April 3, 2025, 9:21 p.m.