Efficient Multi-focus Image Fusion Using Parameter Adaptive Pulse Coupled Neural Network Based Consistency Verification

Yang, Bin; Chen, Qiang

doi:10.1007/s11220-022-00391-5

Cited by 1 publication

(1 citation statement)

References 34 publications

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“…Different from the parameter and level set active contour model, the C-V model does not rely on the gradient information of the image for visual perception. Yang 23 believed that the C-V model could also obtain good segmentation results for images with meaningless gradients and fuzzy boundaries, and it successfully solved the problem of boundary dependent segmentation of level set active contour model based on boundaries. Since the C-V model is evolved on the basis of the Mumford-Shah model 24 , the Mumford-Shah model is briefly introduced before the introduction of the C-V model.…”

Section: Related Workmentioning

confidence: 99%

Simulation analysis of visual perception model based on pulse coupled neural network

Li¹

2023

Sci Rep

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Pulse-coupled neural networks perform well in many fields such as information retrieval, depth estimation and object detection. Based on pulse coupled neural network (PCNN) theory, this paper constructs a visual perception model framework and builds a real image reproduction platform. The model firstly analyzes the structure and generalization ability of neural network multi-class classifier, uses the minimax criterion of feature space as the splitting criterion of visual perception decision node, which solves the generalization problem of neural network learning algorithm. In the simulation process, the initial threshold is optimized by the two-dimensional maximum inter-class variance method, and in order to improve the real-time performance of the algorithm, the fast recurrence formula of neural network is derived and given. The PCNN image segmentation method based on genetic algorithm is analyzed. The genetic algorithm improves the loop termination condition and the adaptive setting of model parameters of PCNN image segmentation algorithm, but the PCNN image segmentation algorithm still has the problem of complexity. In order to solve this problem, this paper proposed an IGA-PCNN image segmentation method combining the improved algorithm and PCNN model. Firstly, it used the improved immune genetic algorithm to adaptively obtain the optimal threshold, and then replaced the dynamic threshold in PCNN model with the optimal threshold, and finally used the pulse coupling characteristics of PCNN model to complete the image segmentation. From the coupling characteristics of PCNN, junction close space of image and gray level characteristics, it determined the local gray mean square error of image connection strength coefficient. The feature extraction and object segmentation properties of PCNN come from the spike frequency of neurons, and the number of neurons in PCNN is equal to the number of pixels in the input image. In addition, the spatial and gray value differences of pixels should be considered comprehensively to determine their connection matrix. Digital experiments show that the multi-scale multi-task pulse coupled neural network model can shorten the total training time by 17 h, improve the comprehensive accuracy of the task test data set by 1.04%, and shorten the detection time of each image by 4.8 s compared with the series network model of multiple single tasks. Compared with the traditional PCNN algorithm, it has the advantages of fast visual perception and clear target contour segmentation, and effectively improves the anti-interference performance of the model.

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Section: Related Workmentioning

confidence: 99%

Simulation analysis of visual perception model based on pulse coupled neural network

Li¹

2023

Sci Rep

View full text Add to dashboard Cite

show abstract

Efficient Multi-focus Image Fusion Using Parameter Adaptive Pulse Coupled Neural Network Based Consistency Verification

Cited by 1 publication

References 34 publications

Simulation analysis of visual perception model based on pulse coupled neural network

Simulation analysis of visual perception model based on pulse coupled neural network

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