Abstract
An image fusion algorithm between visible and infrared images is significant task for computer vision applications such as multi-sensor systems. Among them, although a visible image is clear perfectly able to be seen through the naked eyes, it is often suffers with noise; while an infrared image is unclear but it has high anti-noise property. In this paper, we propose a novel image fusion algorithm for visible and infrared images using a non-subsampled contourlet transform (NSCT) and a pulse-coupled neural network (PCNN). First, we decompose two original images above mentioned into low and high frequency coefficients based on the NSCT. Moreover, each low frequency coefficients for both images are duplicated at multiple scales, and are processed by laplacian filter and average filter respectively. Finally, we can fuse the normalized coefficients by using the PCNN. Conversely, we can reconstruct a fused image based on the low and high frequency coefficients, which are fused by using the inverse NSCT. Experimental results show that the proposed image fusion algorithm surpasses the conventional and state-of-art image fusion algorithm.
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