Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Principles and techniques for sensor data fusion
Signal Processing - Intelligent systems for signal and image understanding
Multisensor image fusion using the wavelet transform
Graphical Models and Image Processing
Iterative solution of nonlinear equations in several variables
Iterative solution of nonlinear equations in several variables
Multisensor for Computer Vision
Multisensor for Computer Vision
Mathematical Techniques in Multisensor Data Fusion
Mathematical Techniques in Multisensor Data Fusion
Mathematical Methods for Neural Network Analysis and Design
Mathematical Methods for Neural Network Analysis and Design
Computers & Mathematics with Applications
Neural data fusion algorithms based on a linearly constrained least square method
IEEE Transactions on Neural Networks
A neural network for a class of convex quadratic minimax problems with constraints
IEEE Transactions on Neural Networks
A recurrent neural network for solving nonlinear convex programs subject to linear constraints
IEEE Transactions on Neural Networks
Neural network for quadratic optimization with bound constraints
IEEE Transactions on Neural Networks
A Neural Network Model for Solving Nonlinear Optimization Problems with Real-Time Applications
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
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In this paper, two neural image fusion algorithms for color and gray level images are proposed. These algorithms are based on a linearly constrained least square (LCLS) method and a novel projection recurrent artificial neural network. The theoretical aspects of the model are based on KKT conditions and projection theorem. Compared with the existing fusion methods, the proposed algorithms do not require any analogs multiplier and their structures are simple for implementation. Existence of the unique solution, stability and global convergence of the related projection recurrent artificial neural network model are proved. Seven steps algorithms are described in detail, for implementation. Corresponding block diagram of the entire process verifies the simplicity of these algorithms. The proposed neural network is stable in the sense of Lyapunov and converges to the optimal vector solution in a few iterations. The implementation of these algorithms for both color and gray level images shows that the quality of noisy images can be enhanced efficiently.