高红霞, 魏涛. 改进PCNN与平均能量对比度的图像融合算法[J]. 红外与激光工程, 2022, 51(4): 20210996. DOI: 10.3788/IRLA20210996
引用本文: 高红霞, 魏涛. 改进PCNN与平均能量对比度的图像融合算法[J]. 红外与激光工程, 2022, 51(4): 20210996. DOI: 10.3788/IRLA20210996
Gao Hongxia, Wei Tao. Image fusion algorithm based on improved PCNN and average energy contrast[J]. Infrared and Laser Engineering, 2022, 51(4): 20210996. DOI: 10.3788/IRLA20210996
Citation: Gao Hongxia, Wei Tao. Image fusion algorithm based on improved PCNN and average energy contrast[J]. Infrared and Laser Engineering, 2022, 51(4): 20210996. DOI: 10.3788/IRLA20210996

改进PCNN与平均能量对比度的图像融合算法

Image fusion algorithm based on improved PCNN and average energy contrast

  • 摘要: 为了改善红外光和可见光图像融合的视觉效果和运算时效性,借助有限离散剪切波变换(Finite discrete shearlet transform, FDST)将源图像分解一系列大小相同尺度不同的高低频子带;然后,在低频子带的融合过程中采用改进的空间频率作为脉冲耦合人工神经网络(Pulse Coupled Neural Network,PCNN)的输入激励,动态调节链接强度的大小,以便根据图像的特征自适应变化,充分保留了图像轮廓和边缘等特征信息。在高频子带的融合中,采用区域平均能量对比度的策略进行融合,尽可能突出了纹理和细节等信息;最后,对处理得到的高低频子带采取FDST逆变换,重构得到背景清晰和目标突出的图像。实验结果表明:提出的改进融合方法能够更加清晰和全面地呈现出图像中的背景和目标,与其他几种算法相比,主观视觉与客观指标均表现的最优,且具有更高的运算效率。

     

    Abstract: To improve the visual effect and time efficiency of infrared and visible image fusion, the source images were decomposed into a series of high and low frequency sub-bands with the same size and different scales by Finite Discrete Shearlet Transform (FDST). Then, in the fusion process of low frequency sub-bands, the improved spatial frequency was used as the input excitation of Pulse Coupled Neural Network(PCNN), and the link strength was dynamically adjusted to change adaptively according to the image features, which fully preserved the feature information of image contour and edge. In the fusion of high frequency sub-band, the strategy of regional average energy contrast was used to fuse, which highlighted the information such as texture and details as much as possible. Finally, the image with clear background and prominent target was reconstructed with the processed high and low frequency sub-bands by using FDST inverse transform. The experimental results show that the improved fusion method can present the background and target in the image more clearly and comprehensively, compared with other algorithms, and performs the best subjective and objective indicators with the highest operation efficiency.

     

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