MATLAB: Calculation SNR of image

calculate snr

Hello everyone,

I need to calculate SNR in edge target image (half black and half white). How can i do this? I thought to take black part of the image and white part of the image, to calculate the standard deviation of the black part and calculate the mean of the white part, dividing the average in variance gives the SNR, is this calculation correct?

thank you.

Best Answer

It looks like you're going to assume the black part is "no signal" and that it's all noise. Actually if the noise goes above and below your signal, then you do have a signal in the dark part. Maybe you can just assume it's the mean. And you assume that the white part is signal plus the same amount of noise. Why don't calculate the stddev of both white and dark parts separately and see if they're the same? If it's additive, then it should be the same. Anyway, each pixel has an SNR. So you can calculate the mean divided by the stddev (not variance) for each pixel. You can average over all pixels in the light and dark regions if you want to get the average SNR in those regions. To average across white and black regions collectively doesn't really make sense.

Related Solutions

MATLAB: How can i determine the percentage of noise given the variance of imnoise ?

From the help:

J = imnoise(I,'gaussian',m,v) adds Gaussian white noise of mean m and variance v to the image I. The default is zero mean noise with 0.01 variance.

Let's get the mean of the entire image

meanOfI = mean2(I);

So, on average, I think you're saying you want sqrt(variance) to equal 40% of meanOfI. So now you know what v has to be. Try experimenting around a bit and see what you learn.

Perhaps this demo will be instructive:

clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
imtool close all;  % Close all imtool figures.
clear;  % Erase all existing variables.
workspace;  % Make sure the workspace panel is showing.
format longg;
format compact;
fontSize = 14;
% Get the original image.
grayImage = imread('eight.tif');
subplot(2, 2, 1);
imshow(grayImage);
title('Original Gray Scale Image', 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'units','normalized','outerposition',[0 0 1 1]);
% Add Gaussian noise to the pixel values.
noisyImage = imnoise(grayImage,'gaussian', 0, 0.02);
subplot(2, 2, 2);
imshow(noisyImage, []);
title('Noisy Image', 'FontSize', fontSize);
% Get the noise alone.
noiseOnly = single(noisyImage) - single(grayImage);
subplot(2, 2, 3);
imshow(noiseOnly, []);
title('Image of Only the Noise', 'FontSize', fontSize);
% Calculate the signal to noise ratio
snrImage = abs(noiseOnly) ./ double(grayImage);
subplot(2, 2, 4);
imshow(snrImage, []);
title('Image of the Signal-to-Noise Ratio', 'FontSize', fontSize);
% Get the mean SNR
snrMean = mean2(snrImage);
message = sprintf('The mean Signal-to-Noise Ratio = %.2f', snrMean);
uiwait(msgbox(message));

MATLAB: How to calculate SNR

Assuming the noise mean has zero bias, just take the mean of the image over the white part of the target and the black part of the target, and take the standard deviation, then divide

meanWhiteGL = mean2(whitePartOfImage);
stdWhiteGL = std(double(whitePartOfImage(:)));
SNRWhite = meanWhiteGL / stdWhiteGL;

Same for the black part of the image. It's possible that the two regions could have different SNRs.

Best Answer

Related Solutions

MATLAB: How can i determine the percentage of noise given the variance of imnoise ?

MATLAB: How to calculate SNR

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