Texture-oriented image denoising technique for the removal of random-valued impulse noise

Marium Azhar; Hassan Dawood; Hussain Dawood

doi:10.1117/1.JEI.27.3.033028

1 June 2018 Texture-oriented image denoising technique for the removal of random-valued impulse noise

Marium Azhar, Hassan Dawood, Hussain Dawood

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 3, 033028 (June 2018). https://doi.org/10.1117/1.JEI.27.3.033028

Abstract

An iterative texture-oriented image denoising technique is proposed for the restoration of images corrupted with random-valued impulse noise (RVIN). The proposed technique opts a switching approach that first identifies the pixels corrupted by RVIN and then estimates their intensity values to restore the images. Textons of distinct orientations conforming to bilateral symmetry are proposed for the identification of corrupted pixels. To estimate the intensity values of the identified corrupted pixels, the textons having local similarity are used. As the textons are fundamental elements of texture perception, the proposed technique preserves the texture information of images, effectively. The performance of the proposed denoising technique is evaluated on standard benchmark test images under various intensities of RVIN by comparing it with state-of-the-art techniques. The simulation results depict the significant performance of the proposed denoising technique for low as well as higher intensities of RVIN.

Citation Download Citation

Marium Azhar, Hassan Dawood, and Hussain Dawood "Texture-oriented image denoising technique for the removal of random-valued impulse noise," Journal of Electronic Imaging 27(3), 033028 (1 June 2018). https://doi.org/10.1117/1.JEI.27.3.033028

Received: 27 March 2018; Accepted: 9 May 2018; Published: 1 June 2018

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available