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Being an efficient method of information fusion, image fusion has been used in many fields such as
machine vision, medical diagnosis, military applications and remote sensing. In this paper, Pulse Coupled Neural
Network (PCNN) is introduced in this research field for its interesting properties in image processing, including
segmentation, target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for
Multi-focus image fusion is proposed. First, the two original images are decomposed by wavelet transform. Then,
based on the PCNN, a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in
each frequency domain as the linking strength, so that its value can be chosen adaptively. Wavelet coefficients
map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then
all pixels of image get the ignition. So, the output of PCNN in each iteration time is ignition wavelet coefficients
of threshold strength in different time. At this moment, the sequences of ignition of wavelet coefficients represent
ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image
gray-scale range, which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are
obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the
firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore, by this
algorithm, the threshold adjusting constant is estimated by appointed iteration number. Furthermore, In order to
sufficient reflect order of the firing time, the threshold adjusting constant αΘ is estimated by appointed iteration
number. So after the iteration achieved, each of the wavelet coefficient is activated. In order to verify the
effectiveness of proposed rules, the experiments upon Multi-focus image are done. Moreover, comparative results
of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the
edge details and improve the spatial resolution of the image.
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