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After 6 years, I imagine so. But since then, lucky for you, they've introduced the psnr() function into the Image Processing Toolbox so you can just simply use that, after using imresize() of course to make the image sizes match. Matlab Code for PSNR and MSE Spread the love PSNR is used to measure the quality of reconstruction of lossy and lossless compression (e.g., for image compression).

Find my demo:% Demonstration to determine PSNR of a gray scale image.%% Clean up. Close up all; very clear all; work area;%- GET DEMO IMAGES -% Study in a standard MATLAB gray scale demonstration image. GrayImage = imread('caméraman.tif'); rows coIumns = dimension(grayImage);% Display the first picture. Subplot(2, 2, 1); imshow(grayImage, ); title('Initial Gray Scale Image'); set(gcf, 'Placement', have(0,'Screensize'));% Maximize number.% Obtain a 2nd image by incorporating noise to the 1st picture. NoisyImage = imnoise(graylmage, 'gaussian', 0, 0.003);% Display the second image. Subplot(2, 2, 2); imshow(noisyImage, ); name('Loud Picture');%- PSNR Computation -% Right now we possess our two images and we can determine the PSNR.% First, compute the 'square mistake' picture.% Make certain they're toss to flying point so that we can obtain negative distinctions.% Otherwise two uint8'beds that should take away to give a negative number% would obtain clipped to stop and not really be bad. SquaredErrorImage = (increase(grayImage) - double(noisyImage)).^ 2;% Display the squared error picture.

Subplot(2, 2, 3); imshow(squaredErrorImage, ); title('Squared Mistake Picture');% Amount the Squared Picture and separate by the amount of elements% to obtain the Entail Squared Error. It will end up being a scalar (a individual quantity). Mse = sum(amount(squaredErrorImage)) / (rows. coIumns);% Calculate PSNR (Maximum Transmission to Noise Percentage) from the MSE according to the formulation. PSNR = 10. sign10( 256^2 / mse);% Alert consumer of the answer.

Message = sprintf('The mean square error is usually%.2f. NThe PSNR =%.2f', mse, PSNR); msgbox(message).

See my demo:% Demo to estimate PSNR of a grey scale picture.%% Clean up. Close all; clear all; workspace;%- Find DEMO IMAGES -% Study in a regular MATLAB grey scale demo picture. GrayImage = imread('caméraman.tif'); rows coIumns = dimension(grayImage);% Display the initial image.

Subplot(2, 2, 1); imshow(grayImage, ); name('Initial Gray Level Image'); set(gcf, 'Placement', have(0,'Screensize'));% Maximize figure.% Obtain a 2nd picture by including noise to the first image. NoisyImage = imnoise(graylmage, 'gaussian', 0, 0.003);% Display the second picture. Subplot(2, 2, 2); imshow(noisyImage, ); name('Noisy Picture');%- PSNR Computation -% Today we possess our two images and we can compute the PSNR.% First, determine the 'square mistake' image.% Make sure they're ensemble to floating stage so that we can obtain negative variations.% In any other case two uint8's i9000 that should subtract to provide a bad quantity% would obtain clipped to stop and not be harmful. SquaredErrorImage = (double(grayImage) - increase(noisyImage)).^ 2;% Display the squared mistake picture.  