The blur or degradation associated with an imaging system can often be modeled b

Question

The blur or degradation associated with an imaging system can often be modeled by a linear convolution. Here, we will blur the image with a Gaussian (of varying width) as an example of imaging degradation. We will then attempt to restore the image using inverse filtering. a) Read in an image, f. b) Use the function degradew (image, sigma) to create a degraded image f. This will blur the image with a Gaussian of width sigma. Use sigma equal to 1. Display and describe the results. c) Carry out inverse filtering using inversew (image, sigma). Display the result and comment. You may need to threshold out large values in order to display the result, e.g. img. * (img

Explanation / Answer

clc; clear all; close all;

% a) Read an image f
f = imread('cameraman.tif');
figure; imshow(f); title('Original image')

% d) Blurr image with Gaussian noise
mean=0; var=1
f1 = imnoise(f,'gaussian',mean,var);
figure; imshow(f1); title('Blurr image with Gaussian noise');

var2=0.00000001;
f2 = imnoise(f,'gaussian',mean,var2);
figure; imshow(f2); title('Blurr image var=0.00000001');

var3=0.000001;
f3 = imnoise(f,'gaussian',mean,var3);
figure; imshow(f3); title('Blurr image var=0.000001');

var4=0.01;
f4 = imnoise(f,'gaussian',mean,var4);
figure; imshow(f4); title('Blurr image var=0.01');

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