Parameter adaptive unit-linking dual-channel PCNN based infrared and visible image fusion

The properties like pulse synchronization of neurons and global coupling greatly motivated researchers to apply pulse coupled neural network (PCNN) models in accomplishing image fusion. However, manual adjustment of its parameters negatively affects the fusion performance. Moreover, it can process one image at a time. A new parameter adaptive unit-linking dual-channel PCNN model that exhibits all the properties of PCNN and processes two images simultaneously is used in this work to implement a novel fusion algorithm in the non-subsampled contourlet transform (NSCT) domain for the integration of infra-red and visible images. At the same time, all the parameters of the proposed model are automatically esti-mated from the source images. The infrared and visible images are first decomposed using NSCT to provide a sequence of band-pass directional sub-bands and a low-pass sub-band, respectively. The band-pass directional sub-bands are fused using fractal dimension-based linking strength, while the low-pass sub-bands are combined using a new linking strength based on the multi-scale morphological gradient of coefficients. Lastly, the fused image is constructed from the fused sub-bands by applying inverse NSCT. Fourteen state-of-the-art methods are adopted for comparing the performance of the pro-posed method. The qualitative comparison is done using the human visual system, whereas six objective metrics are considered for the quantitative evaluation. The proposed method is competitive and outper-forms some of the existing methods, according to the results of the experiments.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This study proposes a novel fusion algorithm for integrating infrared and visible images using a parameter adaptive unit-linking dual-channel pulse coupled neural network (PCNN) model in the non-subsampled contourlet transform (NSCT) domain. The proposed model exhibits all the properties of PCNN and can process two images simultaneously. By automatically estimating all the parameters from the source images, the proposed method achieves competitive performance compared to existing methods. The fusion process involves fractal dimension-based linking strength and multi-scale morphological gradient-based linking strength. The experimental results demonstrate the effectiveness of the proposed method.