MSE Mean Square Error

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Soum on 3 Jul 2013
Edited: Rik on 2 Feb 2022
I De-noise some images and I want to evaluate them so I calculate SNR but I want to use another like Mean Square Error (MSE) I saw some people use it but I don't know what is express in my case I have a noisy image like input and De-noised one in the out put Or maybe PSNR please help me
Salako on 9 Mar 2021
I have two RGB images to determine their MSE and PSNR.
Sir, kindly help me with the information required to get the task done.
My emmail is:
Thankl you

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Accepted Answer

Image Analyst
Image Analyst on 3 Jul 2013
See my demo:
% Demo to calculate PSNR of a gray scale image.
% Clean up.
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
clear; % Erase all existing variables. Or clearvars if you want.
workspace; % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 20;
%------ GET DEMO IMAGES ----------------------------------------------------------
% Read in a standard MATLAB gray scale demo image.
grayImage = imread('cameraman.tif');
[rows columns] = size(grayImage);
% Display the first image.
subplot(2, 2, 1);
imshow(grayImage, []);
title('Original Gray Scale Image', 'FontSize', fontSize);
set(gcf, 'Position', get(0,'Screensize')); % Maximize figure.
% Get a second image by adding noise to the first image.
noisyImage = imnoise(grayImage, 'gaussian', 0, 0.003);
% Display the second image.
subplot(2, 2, 2);
imshow(noisyImage, []);
title('Noisy Image', 'FontSize', fontSize);
%------ PSNR CALCULATION ----------------------------------------------------------
% Now we have our two images and we can calculate the PSNR.
% First, calculate the "square error" image.
% Make sure they're cast to floating point so that we can get negative differences.
% Otherwise two uint8's that should subtract to give a negative number
% would get clipped to zero and not be negative.
squaredErrorImage = (double(grayImage) - double(noisyImage)) .^ 2;
% Display the squared error image.
subplot(2, 2, 3);
imshow(squaredErrorImage, []);
title('Squared Error Image', 'FontSize', fontSize);
% Sum the Squared Image and divide by the number of elements
% to get the Mean Squared Error. It will be a scalar (a single number).
mse = sum(sum(squaredErrorImage)) / (rows * columns);
% Calculate PSNR (Peak Signal to Noise Ratio) from the MSE according to the formula.
PSNR = 10 * log10( 256^2 / mse);
% Alert user of the answer.
message = sprintf('The mean square error is %.2f.\nThe PSNR = %.2f', mse, PSNR);
hayat ali
hayat ali on 21 Feb 2019
thank yuo for your cooperation. but I have question
what are the range values that can be used in D, M,and V in both salt & pepper and gaussian
I = imread('eight.tif');
J = imnoise(I,'salt & pepper', D);
figure, imshow(I), figure, imshow(J)
J = imnoise(I,'gaussian ',M,V)

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More Answers (4)

ashkan abbasi
ashkan abbasi on 11 Apr 2014
% MSE & PSNR for a grayscale image (cameraman.tif) & its filtered
% version
MaxI=1;% the maximum possible pixel value of the images.
Salako on 9 Mar 2021
Good afternoon, sir.
Sir, I have two RGB images to determine their MSE and PSNR.
Sir, kindly help me with the information required to get the task done.
My email:
Thankl you, sir

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jensi asir
jensi asir on 18 Jan 2014
im also getting the same message which show 3 times psnr values ? whats the wrong in it.can you please help me
  1 Comment
Image Analyst
Image Analyst on 18 Jan 2014
You probably changed my demo to use a color image of your own.

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iza on 20 Jan 2015
Can anyone check mine? Is it correct?...
%Load single MRI image I = imread('IM_00042.tif');
% addition of graininess (i.e. noise) I_noise = imnoise(I, 'gaussian', 0.09);
% the average of 3^2, or 9 values(filters the multidimensional array A with the multidimensional filter h) h = ones(3,3) / 3^2; I2 = imfilter(I_noise,h);
% Measure signal-to-noise ratio img=I; img=double(img(:)); ima=max(img(:)); imi=min(img(:)); mse=std(img(:)); snr=20*log10((ima-imi)./mse)
% Measure Peak SNR [peaksnr, snr] = psnr(I_noise, I); fprintf('\n The Peak-SNR value is %0.4f', peaksnr); fprintf('\n The SNR value is %0.4f \n', snr); fprintf('\n The MSE value is %0.4f \n', mse);
%Plot original & filtered figure
subplot(1,2,1), imshow(I_noise), title('Original image')
subplot(1,2,2), imshow(I2), title('Filtered image')
text(size(I,2),size(I,1)+15, ...
'Gaussian = 0.09', ...

Desmond Michael
Desmond Michael on 10 Feb 2016
Edited: Rik on 2 Feb 2022
Hello everyone, I've found a website regarding the above and its very helpful.
Edit @Rik:
The link seems to have gone down. Here is a capture of that page from 2019.
Image Analyst
Image Analyst on 27 Mar 2019
You have to decide what you want when you think of PSNR for a color image. Maybe you want the average PSNR of each color channel.

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