双通道量子脉冲耦合神经网络

Dual Channel Quantum Pulse Coupled Neural Network

脉冲耦合神经网络(PCNN)在图像处理领域应用广泛,改进的双通道脉冲耦合神经网络(DPCNN)也在图像融合领域具有优异性能。为了将量子计算的优异并行性能与双通道脉冲耦合神经网络相结合,降低其算法复杂度,提出了双通道量子脉冲耦合神经网络(DQPCNN)。该模型使用量子逻辑门构建量子模块,如量子全加器、量子乘法器和量子比较器,构建了一个适用于DQPCNN的量子图像卷积模块,并采用这些模块完成DQPCNN所需的计算。通过仿真实验证明了DQPCNN的有效性,DQPCNN的复杂度与其他模型相比具有明显优势。

Pulse coupled neural networks have been proposed for a variety of applications in the field of image processing.Its improved version,the dual channel pulse coupled neural network,also has excellent performance in the field of image fusion.In order to combine the excellent parallel performance of quantum computing with dual channel pulse coupled neural networks and reduce their algorithmic complexity,the dual channel quantum pulse coupled neural network(DQPCNN)is proposed.In this model,quantum logic gates are used to construct quantum modules,such as quantum full adder,quantum multiplier,quantum comparator and a quantum image convolution module for DQPCNN.And these modules are employed to perform the required calculations for DQPCNN.The effectiveness of the DQPCNN is demonstrated by simulation experiments,and the complexity of the DQPCNN is lower than other models.