Multifocus image fusion using artificial neural networks
Pattern Recognition Letters
Digital Mammogram Segmentation Algorithm Using Pulse Coupled Neural Networks
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Gradient-based multiresolution image fusion
IEEE Transactions on Image Processing
Perfect image segmentation using pulse coupled neural networks
IEEE Transactions on Neural Networks
Inherent features of wavelets and pulse coupled networks
IEEE Transactions on Neural Networks
Image shadow removal using pulse coupled neural network
IEEE Transactions on Neural Networks
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For most image fusion algorithms split relationship among pixels and treat them more or less independently, this paper proposes a region-based image fusion scheme using pulse-coupled neural network (PCNN), which combines aspects of feature and pixe-level fusion. The basic idea is to segment all different input images by PCNN and to use this segmentation to guide the fusion process. In order to determine PCNN parameters adaptively, this paper brings forward an adaptive segmentation algorithm based on a modified PCNN with the multi-thresholds determined by a novel water region area method. Experimental results demonstrate that the proposed fusion scheme has extensive application scope and it outperforms the multi-scale decomposition based fusion approaches, both in visual effect and objective evaluation criteria, particularly when there is movement in the objects or mis-registration of the source images.