A novel conflict reassignment method based on grey relational analysis (GRA)
Pattern Recognition Letters
Multiresolution Image Fusion Algorithm Based on Block Modeling and Probabilistic Model
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
A compact cooperative recurrent neural network for computing general constrained L1norm estimators
IEEE Transactions on Signal Processing
Supervised restoration of degraded medical images using multiple-point geostatistics
Computer Methods and Programs in Biomedicine
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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