Digital Image Restoration in Matlab: A Case Study on Inverse and Wiener Filtering

In this paper, at first, a color image of a car is taken. Then the image is transformed into a grayscale image. After that, the motion blurring effect is applied to that image according to the image degradation model described in equation 3. The blurring effect can be controlled by a and b component...

Full description

Saved in:
Bibliographic Details
Published in2018 International Conference on Innovation in Engineering and Technology (ICIET) pp. 1 - 6
Main Authors Khan, Mohammad Mahmudur Rahman, Sakib, Shadman, Arif, Rezoana Bente, Siddique, Md. Abu Bakr
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2018
Subjects
Automobiles
Color image
Degradation
Filtering
grayscale image
Image restoration
inverse filtering
Mathematical model
motion blurring
Noise measurement
random noise
restoration of an image
Wiener filtering
Wiener filters
Online AccessGet full text

Cover

More Information
Summary:In this paper, at first, a color image of a car is taken. Then the image is transformed into a grayscale image. After that, the motion blurring effect is applied to that image according to the image degradation model described in equation 3. The blurring effect can be controlled by a and b components of the model. Then random noise is added in the image via Matlab programming. Many methods can restore the noisy and motion blurred image; particularly in this paper Inverse filtering as well as Wiener filtering are implemented for the restoration purpose. Consequently, both motion blurred and noisy motion blurred images are restored via Inverse filtering as well as Wiener filtering techniques and the comparison is made among them.
DOI:10.1109/CIET.2018.8660797