A gradient-based adaptive nonlocal means algorithm for image denoising

Quan Zhang; Limin Luo; Zhiguo Gui; Yuanjin Li

doi:10.1117/12.2030639

19 July 2013 A gradient-based adaptive nonlocal means algorithm for image denoising

Quan Zhang, Limin Luo, Zhiguo Gui, Yuanjin Li

Proceedings Volume 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013); 887807 (2013) https://doi.org/10.1117/12.2030639
Event: Fifth International Conference on Digital Image Processing, 2013, Beijing, China

Abstract

In this paper, a modified adaptive nonlocal means (ANLM) filter is investigated for image denoising by introducing the image gradient into the classical nonlocal means filter. The proposed algorithm takes the orientation of matching neighborhood into consideration and can adaptively select the filtering parameter based on image gradient. Moreover, the symmetry or approximate symmetry of some filtered images is also considered. Therefore, comparing with the classical nonlocal means filter, the new method can exploit much more similar pixels. The proposed approach is applied to several real images corrupted by white Gaussian noise with different standard deviation. The comparative experimental results show that the improved ANLM filter obtains superior denoising performance.

Citation Download Citation

Quan Zhang, Limin Luo, Zhiguo Gui, and Yuanjin Li "A gradient-based adaptive nonlocal means algorithm for image denoising", Proc. SPIE 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013), 887807 (19 July 2013); https://doi.org/10.1117/12.2030639

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