Read a grayscale image, then apply strong Gaussian noise to it. Display the noisy image.

I = imread('pout.tif'); Inoisy = imnoise(I,'gaussian',0,0.005); imshow(Inoisy) title('Noisy Image')

Estimate the gradient threshold and number of iterations needed to perform anisotropic diffusion filtering of the image.

[gradThresh,numIter] = imdiffuseest(Inoisy)

gradThresh =1x5 uint8 row vector64 50 39 34 29

numIter = 5

Filter the noisy image by using anisotropic diffusion with the estimated parameters.

Idiffuseest = imdiffusefilt(Inoisy,'GradientThreshold',...gradThresh,'NumberOfIterations',numIter);

For comparison, also filter the noisy image by using anisotropic diffusion with the default parameters. The default gradient threshold is 25.5 because the data type of the image isuint8, and the default number of iterations is 5.

Idiffusedef = imdiffusefilt(Inoisy);

Visually compare the two filtered images.

montage({Idiffusedef,Idiffuseest},'ThumbnailSize',[]) title(['Anisotropic Diffusion Filtering Using '...'Default Parameters (Left) vs. Estimated Parameters (Right)'])

Some noise remains in the image that was filtered using default parameters. The noise is almost completely absent from the image that was filtered using estimated parameters. The sharpness of edges in both images, especially high-contrast edges such as the trellis and white collar, is preserved.