1.Generate motion blurred images, apply Wiener filtering for image restoration, and display the results. I=imread('f:\1.jpg') I=rgb2gray(s) I = im2double(I); % Simulate motion blur LEN = 21; THETA = 11; PSF = fspecial('motion', LEN, THETA); blurred = imfilter(I, PSF , 'conv', 'circular'); % restore image wnr2 = deconvwnr(blurred, PSF); subplot(1,2,1);imshow(blurred);title('motion blurred image') subplot(1,2,2);imshow(wnr2);title('restore image')
1.Generate motion blurred images, apply Wiener filtering for image restoration, and display the results. I=imread('f:\1.jpg') I=rgb2gray(s) I = im2double(I); % Simulate motion blur LEN = 21; THETA = 11; PSF = fspecial('motion', LEN, THETA); blurred = imfilter(I, PSF , 'conv', 'circular'); % restore image wnr2 = deconvwnr(blurred, PSF); subplot(1,2,1);imshow(blurred);title('motion blurred image') subplot(1,2,2);imshow(wnr2);title('restore image')
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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1.Generate motion blurred images, apply Wiener filtering for image restoration, and display the results.
I=imread('f:\1.jpg')
I=rgb2gray(s)
I = im2double(I); % Simulate motion blur
LEN = 21; THETA = 11;
PSF = fspecial('motion', LEN, THETA);
blurred = imfilter(I, PSF , 'conv', 'circular'); % restore image
wnr2 = deconvwnr(blurred, PSF);
subplot(1,2,1);imshow(blurred);title('motion blurred image')
subplot(1,2,2);imshow(wnr2);title('restore image')
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