create a 256-point zero-mean random noise sample i(x)

create a 256-point zero-mean random noise sample i(x)

a )Calculate mean and variance of i(x)

b) Calculate autocorrelation function C(Δ) for i(x). Describe how you did this and any assumptions you made. What is the relationship between C(0) and i(x)?

c) Calculate Wiener spectrum W(u) for i(x). What is the relationship between C(0) and W(u)? How would you convert W(u) to MTF(u)?

I neeed so much help i beg you all

Answer 1

[Image Analyst]

Hints:

Set (the poorly-named) i to rand(......).

i = rand(.........................

Then use mean() and var() on i.

For autocorrelation, I'd use conv() with flip() and 'full' option. Note that the x-axis is longer after correlation, so what x represents the first element is different -- your prof should have gone over that.

I don't know if there is a wiener function built in. There is a non-linear one for removing noise of images, wiener2() in the Image Processing Toolbox, but I don't think that is what you need. Review your course notes or check Wikipedia.

Read this link if you still need help.

[deniz koc]

i need the b and c part . I checked the Wiener function built but I didnt understand so i send the whole question ''maybe white noise rand is diff then other '''

Thanks anyway (Genius-Named)

[Image Analyst]

I told you how I'd do b. Use conv. It's literally one line of code. If I give much more of a hint, then it would no longer be your code, it would be my code and you'd be turning in someone else's code, which is probably against your course's policy. About all I can do it

outputSignal = conv(inputSignal, ...........................);

Surely you can type in the few final characters of fliplr() and 'full'. If not, why not?