How to preserve transparent edges in images after image processing?

Hello, everyone!
I’m facing a problem related to the processing of .png images with transparent edges obtained in a simulation software. Whenever I convert the image (RGB) to grayscale, together with a Graussian filter, the transparent edges become white, and this hinders the reading and training of the neural network model I’m using.
for i = 1:length(files)
img = imread(fullfile(path, files(i).name));
[~, name, ext] = fileparts(files(i).name);

if size(img, 3) == 3
grayImg = rgb2gray(img);
else
grayImg = img;
end

mean=0; variance=0.01;
noisedImg = imnoise(grayImg, ‘gaussian’, mean, variance);
imgDeformed = elasticDeform(noisedImg, 8, 30);

file(:, :, 1, i) = imresize(imgDeformed, fileSize);

separatorIndex = strfind(name, ‘-‘);

depth(i, 1) = round(str2double(name(1:separatorIndex-1)), 2);
time(i, 1) = round(str2double(name(separatorIndex+1:end)));
end

[train_idx, ~, val_idx] = dividerand(numFiles, 0.7, 0, 0.3);

XTrain = file(:, :, 1, train_idx);
YTrain = depth(train_idx, 1);

XVal = file(:, :, 1, val_idx);
YVal = depth(val_idx, 1);

save(‘data.mat’, ‘XTrain’, ‘XVal’, ‘YTrain’, ‘YVal’);
Where elasticDeform is:
function imgDeformed = elasticDeform(img, alpha, sigma)
[rows, cols] = size(img);

dx = alpha * randn(rows, cols);
dy = alpha * randn(rows, cols);

dx = imgaussfilt(dx, sigma);
dy = imgaussfilt(dy, sigma);

[X, Y] = meshgrid(1:cols, 1:rows);

X_new = X + dx; Y_new = Y + dy;

imgDeformed = interp2(X, Y, double(img), X_new, Y_new, ‘linear’, 0);
imgDeformed = uint8(imgDeformed);
end
The product of the image processing is therefore:
You can see that in the images there are white edgesHello, everyone!
I’m facing a problem related to the processing of .png images with transparent edges obtained in a simulation software. Whenever I convert the image (RGB) to grayscale, together with a Graussian filter, the transparent edges become white, and this hinders the reading and training of the neural network model I’m using.
for i = 1:length(files)
img = imread(fullfile(path, files(i).name));
[~, name, ext] = fileparts(files(i).name);

if size(img, 3) == 3
grayImg = rgb2gray(img);
else
grayImg = img;
end

mean=0; variance=0.01;
noisedImg = imnoise(grayImg, ‘gaussian’, mean, variance);
imgDeformed = elasticDeform(noisedImg, 8, 30);

file(:, :, 1, i) = imresize(imgDeformed, fileSize);

separatorIndex = strfind(name, ‘-‘);

depth(i, 1) = round(str2double(name(1:separatorIndex-1)), 2);
time(i, 1) = round(str2double(name(separatorIndex+1:end)));
end

[train_idx, ~, val_idx] = dividerand(numFiles, 0.7, 0, 0.3);

XTrain = file(:, :, 1, train_idx);
YTrain = depth(train_idx, 1);

XVal = file(:, :, 1, val_idx);
YVal = depth(val_idx, 1);

save(‘data.mat’, ‘XTrain’, ‘XVal’, ‘YTrain’, ‘YVal’);
Where elasticDeform is:
function imgDeformed = elasticDeform(img, alpha, sigma)
[rows, cols] = size(img);

dx = alpha * randn(rows, cols);
dy = alpha * randn(rows, cols);

dx = imgaussfilt(dx, sigma);
dy = imgaussfilt(dy, sigma);

[X, Y] = meshgrid(1:cols, 1:rows);

X_new = X + dx; Y_new = Y + dy;

imgDeformed = interp2(X, Y, double(img), X_new, Y_new, ‘linear’, 0);
imgDeformed = uint8(imgDeformed);
end
The product of the image processing is therefore:
You can see that in the images there are white edges Hello, everyone!
I’m facing a problem related to the processing of .png images with transparent edges obtained in a simulation software. Whenever I convert the image (RGB) to grayscale, together with a Graussian filter, the transparent edges become white, and this hinders the reading and training of the neural network model I’m using.
for i = 1:length(files)
img = imread(fullfile(path, files(i).name));
[~, name, ext] = fileparts(files(i).name);

if size(img, 3) == 3
grayImg = rgb2gray(img);
else
grayImg = img;
end

mean=0; variance=0.01;
noisedImg = imnoise(grayImg, ‘gaussian’, mean, variance);
imgDeformed = elasticDeform(noisedImg, 8, 30);

file(:, :, 1, i) = imresize(imgDeformed, fileSize);

separatorIndex = strfind(name, ‘-‘);

depth(i, 1) = round(str2double(name(1:separatorIndex-1)), 2);
time(i, 1) = round(str2double(name(separatorIndex+1:end)));
end

[train_idx, ~, val_idx] = dividerand(numFiles, 0.7, 0, 0.3);

XTrain = file(:, :, 1, train_idx);
YTrain = depth(train_idx, 1);

XVal = file(:, :, 1, val_idx);
YVal = depth(val_idx, 1);

save(‘data.mat’, ‘XTrain’, ‘XVal’, ‘YTrain’, ‘YVal’);
Where elasticDeform is:
function imgDeformed = elasticDeform(img, alpha, sigma)
[rows, cols] = size(img);

dx = alpha * randn(rows, cols);
dy = alpha * randn(rows, cols);

dx = imgaussfilt(dx, sigma);
dy = imgaussfilt(dy, sigma);

[X, Y] = meshgrid(1:cols, 1:rows);

X_new = X + dx; Y_new = Y + dy;

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