Every PCNN neuron is consist of three parts: the receptive field, modulation field and pulse generator.
When applied to image processing, PCNN is a partial linked network (one layer and two dimensions).
In [14, 16, 17], the theory of PCNN is to decide which pixel at the position of (i, j) (visible image or infrared thermal image) performs best in capturing targets.
In low-pass sub-band images, SML which represents clarity of an image is chosen as input of PCNN.
In order to eliminate the weakness of the rule of PCNN such blocky effect resulted from the difference between the gray-level of source images, we divide this sub-band image into three parts by comparing the times of pulsing signals: [P.
What is more, the clarity, spatial resolution and visual effect of the fused image using proposed method are obviously enhanced compared with the results of wavelet transform, PCA and PCNN.
Even, from figure 5(e) and figure 5(f) we can conclude that the weakness of PCNN especially the blocky effect is eliminated without decreasing the fusion effect.
In this paper an improved image fusion algorithm based on Contourlet and PCNN was proposed to detect obstacles in forests.
A novel algorithm of remote sensing image fusion based on Shearlets and PCNN, unpublished.
Besides, the link cost function using the PCNN improves the delay by about 16% and 12%, compared to those using the FCNN with varying [[sigma].
The link cost function using the PCNN improves the energy efficiency about 10% and 47%, compared to those using the FCNN and conventional methods.
We derived the cost function using the concept of PCNN structured by the input types (correlated input type and uncorrelated input type).