2-D impulse noise suppression by recursive gaussian maximum likelihood estimation

Yang Chen; Jian Yang; Huazhong Shu; Luyao Shi; Jiasong Wu; Limin Luo; Jean-Louis Coatrieux; Christine Toumoulin

doi:10.1371/journal.pone.0096386

2-D impulse noise suppression by recursive gaussian maximum likelihood estimation

PLoS One. 2014 May 16;9(5):e96386. doi: 10.1371/journal.pone.0096386. eCollection 2014.

Authors

Yang Chen¹, Jian Yang², Huazhong Shu¹, Luyao Shi¹, Jiasong Wu¹, Limin Luo¹, Jean-Louis Coatrieux³, Christine Toumoulin³

Affiliations

¹ Laboratory of Image Science and Technology, Southeast University, Nanjing, China; The Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Beijing, China; Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), Rennes, France.
² Research Center of Mixed Reality and Advanced Display, Beijing Institute of Technology, Beijing, China.
³ Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), Rennes, France; INSERM, U1099, Rennes, France; Université de Rennes 1, LTSI, Rennes, France.

Abstract

An effective approach termed Recursive Gaussian Maximum Likelihood Estimation (RGMLE) is developed in this paper to suppress 2-D impulse noise. And two algorithms termed RGMLE-C and RGMLE-CS are derived by using spatially-adaptive variances, which are respectively estimated based on certainty and joint certainty & similarity information. To give reliable implementation of RGMLE-C and RGMLE-CS algorithms, a novel recursion stopping strategy is proposed by evaluating the estimation error of uncorrupted pixels. Numerical experiments on different noise densities show that the proposed two algorithms can lead to significantly better results than some typical median type filters. Efficient implementation is also realized via GPU (Graphic Processing Unit)-based parallelization techniques.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms*
Image Processing, Computer-Assisted / methods*
Likelihood Functions
Models, Theoretical*
Signal-To-Noise Ratio*

Grants and funding

This research was supported by National Basic Research Program of China under grant (2010CB732503), National Natural Science Foundation under grants (81000636, 81370040), and the Project supported by Natural Science Foundations of Jiangsu Province (BK2011593). This work was also supported by the Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.