Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China.
National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China.
Sensors (Basel). 2020 May 12;20(10):2764. doi: 10.3390/s20102764.
Pulse-coupled neural network (PCNN) and its modified models are suitable for dealing with multi-focus and medical image fusion tasks. Unfortunately, PCNNs are difficult to directly apply to multispectral image fusion, especially when the spectral fidelity is considered. A key problem is that most fusion methods using PCNNs usually focus on the selection mechanism either in the space domain or in the transform domain, rather than a details injection mechanism, which is of utmost importance in multispectral image fusion. Thus, a novel pansharpening PCNN model for multispectral image fusion is proposed. The new model is designed to acquire the spectral fidelity in terms of human visual perception for the fusion tasks. The experimental results, examined by different kinds of datasets, show the suitability of the proposed model for pansharpening.
脉冲耦合神经网络（PCNN）及其改进模型适用于处理多聚焦和医学图像融合任务。不幸的是，PCNN 很难直接应用于多光谱图像融合，特别是在考虑光谱保真度的情况下。一个关键问题是，大多数使用 PCNN 的融合方法通常侧重于空间域或变换域中的选择机制，而不是多光谱图像融合中至关重要的细节注入机制。因此，提出了一种新的用于多光谱图像融合的 PCNN 模型。该新模型旨在根据人类视觉感知来获取融合任务的光谱保真度。通过不同数据集进行的实验结果表明，该模型非常适合于图像融合。
