Research on Infrared Image Segmentation Algorithm Based on Pulse-Coupled Neural Network
Infrared images are noisy and complex in structure,and it is difficult for traditional segmentation methods to achieve complete segmentation of infrared images.Pulse-coupled neural network(PCNN)has numerous parameters,and manual adjustment of network parameters is time-consuming and labor-intensive.In order to avoid the manual adjustment of network parameters while achieving a more complete segmentation of infrared images,a new infrared image segmentation algorithm is proposed by combining the particle swarm optimization(PSO)algorithm and the improved PCNN.Firstly,the infrared images are preprocessed.Secondly,the PCNN is simplified in order to reduce the network parameters.Then,the preprocessed images are input to the network for cyclic iterations,and the PSO algorithm is used to calculate the adaptation function values of the segmentation results for each iteration,so as to determine the population and the individual optimal parameters.Finally,the segmentation results are obtained by the population optimal parameters.In order to verify the segmentation performance of the algorithm,different natural landscape images and infrared images are used in the experiments.The experimental results show that the proposed algorithm can achieve a more complete segmentation of infrared and natural landscape images;the proposed algorithm is better than the traditional algorithms in both segmentation effect and qualitative analysis.
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