2-D impulse noise suppression by recursive gaussian maximum likelihood estimation.
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.
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.
Yang
Y
Chen
Research Center of Mixed Reality and Advanced Display, Beijing Institute of Technology, Beijing, China.
Jian
J
Yang
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.
Huazhong
H
Shu
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.
Luyao
L
Shi
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.
Jiasong
J
Wu
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.
Limin
L
Luo
Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), Rennes, France; INSERM, U1099, Rennes, France; Université de Rennes 1, LTSI, Rennes, France.
Jean-Louis
JL
Coatrieux
Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), Rennes, France; INSERM, U1099, Rennes, France; Université de Rennes 1, LTSI, Rennes, France.
Christine
C
Toumoulin
D000465:Algorithms; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D008962:Models, Theoretical; D059629:Signal-To-Noise Ratio