Use a two-dimensional Wiener filter to obtain a restored image f(u,upsilon), fro

Question

Use a two-dimensional Wiener filter to obtain a restored image f(u,upsilon), from the attached noisy and blurred image. G(u, uosilon). The noisy and blurred image can be modeled as G(u,uosilon) = H(u,uosilon)F(u, upsilon) -+- N(u,upsilon) where -H(u, v) = exp(-0.002(u^2 + upsilon^2)5/6) -N(u,v) is white Gaussian noise with sigma_N^2= 1 Use a two-dimensional Wiener filter to obtain a restored image f(u,upsilon), from the attached noisy and blurred image. G(u, upsilon). The noisy and blurred image can be modeled as G(u,upsilon) = H(u,upsilon)F(u, upsilon) + N(u,upsilon) where - H(u,upsilon) =sin T/pi(ua + upsilonb) sin(pi(ua +upsilonb))exp(-jpi(ua +upsilonb))is a two dimen-sional motion, with a = b = 0.1 and T = 1 - N(u, upsilon) is white Gaussian noise with sigma_N^2 = 0.01

Explanation / Answer

creating an array named mean_quiz_score;

mean_quiz_scores = [99,85,67;90,65,69;98,95,97;80,85,89;98,77,87]

   m= mean(mean_quiz_scores)

Create an aray mean_student_scores;

mean _student_scores = [99,90,98,80,98;85,65,95,85,77;67,69,97,89,87]

m= mean(mean_Student_scores) it creats a 3X1 matrix

mean_quiz_scores = [99,85,67;90,65,69;98,95,97;80,85,89;98,77,87]

M = mean(mean_quiz_scores,5)

if(mean(98,77,87) > mean(80,85,89)

disp("quiz 5 is easy")
else
disp (quiz 5 is not easy")

mean_student_scores = [99,85,67;90,65,69;98,95,97;80,85,89;98,77,87]

high = max(mean_student_scores)

if (high = max(67,69,97,89,87))
   disp("Student 3 is a hero")
else
disp ("student3 is a zero")

  

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