Encoder-Decoder Model for Grayscale to RGB Conversion

This script demonstrates creating and training an encoder-decoder model using random overlapping image patches.

This script demonstrates how to create random overlapping patches of an image to train an encoder-decoder model that converts grayscale images to RGB images.

Some steps:

• reading an example image (`peppers.png`)
• call the function `image2patch_randperm_overlap` wiht the parameter

clc
clear
close all force
X = imread('peppers.png') ;
figure, imagesc(X), title('Example')

imageOut = mat2gray( X ) ;
imageIn  = rgb2gray( imageOut ) ;

deltaX = 32 ;
deltaY = 32 ;
overlap = 8 ;

per = .8 ; % percent to train
seed = 1 ; % to control the random numbers
make_plot = 1 ;

[patchs] = image2patch_randperm_overlap( ...
    imageIn , imageOut , deltaX , deltaY , overlap , seed , per , make_plot) ;

• to visualizate the Train and Test data: