IEEE Transactions on Pattern Analysis and Machine Intelligence
Multifocus image fusion using artificial neural networks
Pattern Recognition Letters
Evaluation of focus measures in multi-focus image fusion
Pattern Recognition Letters
Multi-focus image fusion using pulse coupled neural network
Pattern Recognition Letters
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Medical image fusion using m-PCNN
Information Fusion
Review article: Review of pulse-coupled neural networks
Image and Vision Computing
Pulse coupled neural network based image fusion
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
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
New Spiking Cortical Model for Invariant Texture Retrieval and Image Processing
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
Spiking Cortical Model (SCM) is derived from primate visual cortex. It has a high sensitivity for low intensities of stimulus, but low sensitivity for high intensities, and is suitable for image processing. This paper adopts an improved SCM for multifocus image fusion. Firstly we analyze and compare various image clarity measures, and then we propose a new SCM fusion method based on a composite image clarity criterion which synthesizes virtues of two classic clarity criteria. As to the iteration number of SCM model for image processing, we introduce time matrix as an adaptive setting method instead of using fixed constant, which can automatically and adaptively calculate iteration number for each image accurately. Besides, we optimize pulsing output matrix of source image according to natural optical focus principle before forming and outputting the final fused image. In order to verify the effectiveness of the proposed method, we compare it with other ten methods under four fusion effect evaluation indices. The experimental results show that the proposed approach can obtain better fusion results than others, and is an effective multifocus image fusion method.