How can I make mean filter for color image?

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Gleb Bulatovskiy
Gleb Bulatovskiy le 19 Déc 2018
Commenté : Gleb Bulatovskiy le 21 Déc 2018
I am trying to do something like this for a color image:
SetPixelsIn32by32BlocksToStandardDeviationExample_01.png SetPixelsIn32by32BlocksToStandardDeviationExample_02.png
I was trying to use plockproc function but kept getting an error; it is my first time working with an image in matlab but i feel like it should not be hard to do. Can anyone help please?

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Image Analyst
Image Analyst le 20 Déc 2018
Gleb, you just had to make a minor modification to my code to call it 3 times, once for each color channel, and then recombine the results into an RGB image.
meanFilterFunction4 = @(theBlockStructure) mean2( * ones(1, 1, class(;
% Now,here we actually to the actual filtering.
blockyImageR = blockproc(redChannel, blockSize, meanFilterFunction4);
blockyImageG = blockproc(greenChannel, blockSize, meanFilterFunction4);
blockyImageB = blockproc(blueChannel, blockSize, meanFilterFunction4);
[blockRows, blockColumns] = size(blockyImageR)
% Recombine separate color channels into a single, true color RGB image.
rgbImage2 = cat(3, blockyImageR, blockyImageG, blockyImageB);
Attached is a full demo using the standard peppers demo image and using a variety of input and output block sizes.
0000 Screenshot.png
Let me know if you still don't know how to adapt it to what you want.
  1 commentaire
Gleb Bulatovskiy
Gleb Bulatovskiy le 21 Déc 2018
It works, thank you!

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Plus de réponses (2)

Image Analyst
Image Analyst le 19 Déc 2018
See attached blockproc demo, which does this.
  1 commentaire
Gleb Bulatovskiy
Gleb Bulatovskiy le 20 Déc 2018
This code doesnt work on Mac for some reason, however it works on windows.
It works for a black and white image, and if I use color image, it makes it black and white first and then processes it and outputs black and white image. What I need to do is the same thing but I need the output to be colored image as well.

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Jan le 20 Déc 2018
Modifié(e) : Jan le 20 Déc 2018
X = RGBimage; % 2D matrix, 3D RGB array or any other array
V = 100; % Block size for 1st dim
W = 100; % and for 2nd dim
S = size(X);
M = S(1) - mod(S(1), V);
N = S(2) - mod(S(2), W);
MV = M / V;
NW = N / W;
% Cut and reshape input such that the 1st and 3rd dimension have the lengths V
% and W:
XM = reshape(X(1:M, 1:N, :), V, MV, W, NW, []);
Y = sum(sum(XM, 1), 3) ./ (V * W);
% Remove singleton dimensions:
S(1) = MV;
S(2) = NW;
Y = reshape(Y, S);

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