Multisensor image fusion using the wavelet transform
Graphical Models and Image Processing
Evaluation of focus measures in multi-focus image fusion
Pattern Recognition Letters
Pulse coupled neural network based image fusion
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Frame representations for texture segmentation
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks
Physiologically motivated image fusion for object detection using a pulse coupled neural network
IEEE Transactions on Neural Networks
Perfect image segmentation using pulse coupled neural networks
IEEE Transactions on Neural Networks
A novel conflict reassignment method based on grey relational analysis (GRA)
Pattern Recognition Letters
Image Fusion Algorithm Using RBF Neural Networks
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Review article: Review of pulse-coupled neural networks
Image and Vision Computing
Multi-focus image fusion using PCNN
Pattern Recognition
Fusion of multi-focus images using differential evolution algorithm
Expert Systems with Applications: An International Journal
Multisensor image fusion using a pulse coupled neural network
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Multispectral and panchromatic images fusion by adaptive PCNN
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Multifocus image fusion and denoising: A variational approach
Pattern Recognition Letters
Multi-focus thermal image fusion
Pattern Recognition Letters
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Expert Systems with Applications: An International Journal
Spiking cortical model for multifocus image fusion
Neurocomputing
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This paper presents a method for multi-focus image fusion by using pulse coupled neural network (PCNN). The registered source images are first decomposed into blocks and the size of the image blocks is 8x8 pixels. Feature maps are obtained by computing the energy of image Laplacian of each block. Input the feature maps into PCNN as external stimulus. The final fused image can be constructed by selecting the image blocks from the source images based on the comparison of the outputs of the PCNN. Experimental results show that the proposed method outperforms some previous fusion methods, both in visual effect and objective evaluation criteria.