A Novel Image Fusion Method Using Self-adaptive Dual-channel Pulse Coupled Neural Networks Based on PSO Evolutionary Learning

LI Yi; WU Xiao-jun

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A Novel Image Fusion Method Using Self-adaptive Dual-channel Pulse Coupled Neural Networks Based on PSO Evolutionary Learning

更新时间：2025-07-16

- A Novel Image Fusion Method Using Self-adaptive Dual-channel Pulse Coupled Neural Networks Based on PSO Evolutionary Learning
- Acta Electronica Sinica Vol. 42, Issue 2, Pages: 217-222(2014)
- 作者机构：
  
  1. 江南大学物联网工程学院,江苏,无锡,214122
  2. 青岛大学国际学院,山东,青岛,266071
  3. 江南大学物联网工程学院,江苏,无锡,214122
  4. 青岛大学国际学院,山东,青岛,266071
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.60973094, No.61103128);Science and Technology Research Major Program of Ministry of Education of China (No.311024);Project for Discipline Innovation and Intelligence Introduction of Higher Education of China (No.B12018)
- DOI：
  CLC： TP391
- Published Online：25 February 2014，
  
  Published：2014
- 稿件说明：
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LI Yi, WU Xiao-jun. A Novel Image Fusion Method Using Self-adaptive Dual-channel Pulse Coupled Neural Networks Based on PSO Evolutionary Learning[J]. Acta Electronica Sinica, 2014, 42(2): 217-222.
DOI：

LI Yi, WU Xiao-jun. A Novel Image Fusion Method Using Self-adaptive Dual-channel Pulse Coupled Neural Networks Based on PSO Evolutionary Learning[J]. Acta Electronica Sinica, 2014, 42(2): 217-222. DOI：

摘要

针对双通道脉冲耦合神经网络图像融合方法中参数选取不易确定之挑战，提出了一种基于进化学习的自适应双通道脉冲耦合图像融合方法.通过引入自适应学习能力的进化学习算法和构建新的优化目标对双通道脉冲耦合神经网络模型参数来进行优化，提出的新算法能够有效地找到双通道脉冲耦合神经网络模型的近似最优参数，克服了经典双通道脉冲耦合神经网络图像融合方法需要人工交互穷举尝试不同参数来获取较优参数之缺点.实验研究亦表明了上述优点.

Abstract

A novel method for self-adaptive dual-channel pulse coupled neural networks (DC-PCNN) based on PSO evolutionary learning is proposed in order to overcome the difficulty of parameters selection of DC-PCNN.In this study an evolutionary learning algorithm and a new optimization criterion are proposed to optimize the parameters of PCNN for image fusion.In contrast with classical DC-PCNN method that needs to try different parameters settings manually

the proposed method can find the optimal parameters adaptively.Experimental results obtained on benchmark databases verify the above advantages.

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