Preprint Article Version 2 This version is not peer-reviewed

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

Version 1 : Received: 21 November 2018 / Approved: 23 November 2018 / Online: 23 November 2018 (14:22:33 CET)
Version 2 : Received: 5 February 2019 / Approved: 5 February 2019 / Online: 5 February 2019 (16:13:14 CET)

A peer-reviewed article of this Preprint also exists.

Khan, M.M.R.; Sakib, S.; Arif, R.B.; Siddique, M.A.B. 2018, December. Digital Image Restoration in Matlab: A Case Study on Inverse and Wiener Filtering. In 2018 International Conference on Innovation in Engineering and Technology (ICIET) (pp. 1-6). IEEE. Khan, M.M.R.; Sakib, S.; Arif, R.B.; Siddique, M.A.B. 2018, December. Digital Image Restoration in Matlab: A Case Study on Inverse and Wiener Filtering. In 2018 International Conference on Innovation in Engineering and Technology (ICIET) (pp. 1-6). IEEE.

Journal reference: 2018 International Conference on Innovation in Engineering and Technology (ICIET), IEEE 2018
DOI: 10.1109/CIET.2018.8660797

Abstract

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.

Subject Areas

Color image, grayscale image, motion blurring, random noise, inverse filtering, Wiener filtering, restoration of an image

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