Paper Info
Title
Novel cooperative neural fusion algorithms for image restoration and image fusion.
Abstract
To deal with the problem of restoring degraded images with non-Gaussian noise, this paper proposes a novel cooperative neural fusion regularization (CNFR) algorithm for image restoration. Compared with conventional regularization algorithms for image restoration, the proposed CNFR algorithm can relax need of the optimal regularization parameter to be estimated. Furthermore, to enhance the quality of restored images, this paper presents a cooperative neural fusion (CNF) algorithm for image fusion. Compared with existing signal-level image fusion algorithms, the proposed CNF algorithm can greatly reduce the loss of contrast information under blind Gaussian noise environments. The performance analysis shows that the proposed two neural fusion algorithms can converge globally to the robust and optimal image estimate. Simulation results confirm that in different noise environments, the proposed two neural fusion algorithms can obtain a better image estimate than several well known image restoration and image fusion methods.
Year
DOI
Venue
2007
10.1109/TIP.2006.888340
IEEE Transactions on Image Processing
Keywords
Field
DocType
cooperative neural fusion,degraded image,novel cooperative neural fusion,image fusion,image fusion method,neural fusion algorithm,image restoration,signal-level image fusion algorithm,better image estimate,optimal image estimate,gaussian noise
Background noise,Image fusion,Computer science,Image quality,Image processing,Artificial intelligence,Image restoration,Artificial neural network,Computer vision,Pattern recognition,Algorithm,Sensor fusion,Gaussian noise
Journal
Volume
Issue
ISSN
16
2
1057-7149
Citations 
PageRank 
References 
20
0.83
24
Authors
2
Name
Order
Citations
PageRank
Youshen Xia11795123.60
M. S. Kamel220330.22