USE MATLAB CODE!

) Load and visualize the image septagon.tif.

(a) Add Gaussian noise with mean 0 and variance 10 to this image, and visualize the noisy

image and its histogram using the following code:

septagon = double(imread(septagon.tif));

noise = 10*randn(size(septagon));

septagonNoisy10 = septagon + noise;

figure;imshow(septagonNoisy10/max(septagonNoisy10(:)))

figure;histogram(septagonNoisy10)

What do you observe?

(b) Choose an appropriate threshold T and segment the image using the following code:

septagonSegmented = imbinarize(septagonNoisy10,T);

figure;imshow(septagonSegmented)

Have you managed to successfully segment the image?

(c) Repeat part (b) when the noise variance is 20. Can you successfully segment the image in this case? Is there any T value that will result in an acceptable segmentation

performance?

(d) Denoise the image you have generated in part (c) before segmentation, using the following code:

h = ones(3)/9;

denoisedSeptagon = filter2(h,septagonNoisy20);

figure;imshow(denoisedSeptagon/max(denoisedSeptagon(:)))

figure;histogram(denoisedSeptagon)

septagonSegmented = imbinarize(denoisedSeptagon,T);

Have denoising resulted in better segmentation performance?