NSCT-PCNN image fusion based on image gradient motivation

Pulse coupled neural network (PCNN) is widely used in image processing because of its unique biological characteristics, which is suitable for image fusion. When combining PCNN with non-subsampled contourlet (NSCT) model, it is applied in overcoming the difficulty of coefficients selection for subband of the NSCT model. However in the original model, only the grey values of image pixels are used as input, without considering that the subjective vision of human eyes lacks the sensitivity to the local factors of the image. In this study, the improved pulse-coupled neural network model has replaced the grey-scale value of the image and introduced the weighted product of the strength of the gradient of the image and the local phase coherence as the model input. Finally, compared with other multi-scale decompositions-based image fusion and other improved NSCT-PCNN algorithms, the algorithm presented in this study outperforms them in terms of objective criteria and visual appearance.