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Image Fusion Technology is widely applied in various fields with the continuous development of image sensor technology. This study puts forward an image fusion algorithm based on Pulse Coupled Neural Network (PCNN) and Non Subsampled Contourlet Transform (NSCT). The algorithm does not make simultaneous use of PCNN on high frequency sub-band and low frequency sub-band. The fusion algorithm applied in fusion of low frequency sub-band coefficients is specifically designed for the high requirement on image definition of low frequency sub-band which can realize the fusion of local definition and marginal area energy. While the algorithm applied in fusion of high frequency sub-band coefficients takes bigger absolute values of coefficients, which is obtained from the highest level sub-band that can completely preserve marginal information showing images. For other hierarchies, PCNN fusion method which takes the spatial frequency as coefficient of connection is selected, so as to improve the visual features of fusion results. Introduction The concept of image fusion was put forward by scholars in the late 1970s (1). It is a high technology subject of artificial intelligence, image processing, sensors and computer technology. TONG Tao, et al. came up with an image fusion algorithm based on Non Subsampled Contourlet Transform (NSCT) so as to obtain excellent subjective visual effect and objective quantitative indicators (2). X Wang, et al. applied Multi-objective Particle Swarm Optimization (MOPSO) to find the optimal fusion parameters and solved collocation optimization problem of fusion parameters (3). A, et al. proposed a multi-source image fusion method based on revised Pulse Coupled Neural Network (PCNN) which overcame the shortages of image blurring and inadequate noise sensitivity which were commonly seen in traditional pixel level fusion methods and thus was especially suitable for situations where images could not be strictly matched (4). This study puts forward a multisensor image fusion algorithm based on NSCT and PCNN transform, aiming to achieve better visual effect and quantitative indicators and therefore realize a better fusion of details of the source image. Image fusion algorithm based on combination of nsct and pcnn As shown in figure 1, fusion rule of NSCT multifocus image based on definition and PCNN is as follows: after NSCT multi-scale and multi-directional decomposition of the image, different rules was used for fusion processing on each sub-band coefficient. The final fusion image was obtained through NSCT inverse transformation. 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer (MMEBC 2016) © 2016. The authors Published by Atlantis Press 399 Fig. 1. Text algorithm flow chart The specific fusion process is as follows: Step 1: J level NSCT decomposition was carried out on image A and B and the all-order sub-band coefficients obtained were respectively { } ) ( ) , ( ), , ( 0 , 0 j j n m C n m C l j A j ≥ and { } ) ( ) , ( ), , ( 0 , 0 j j n m C n m C l j j ≥ 和 , of which, was the low frequency sub-band coefficient, was the band-pass sub-band coefficient on direction 1 under j dimension. Step 2: for low frequency sub-band, selection scheme based on edge and clarity was applied and for high frequency sub-band, improved PCNN simplified model was applied. The coefficient of F obtained was ) , ( ) * ( ) , ( ) * ( ) , ( ) * ( ) , ( 2 0 3 2 0 2 2 0 1 n m C F n m C F n m C F n m E A j A j A j A + + = . Step 3: NSCT inverse transformation was carried out and the final fusion image F was obtained. Fusion Rule of Low Frequency Sub-band Coefficients. The main energy of the image is focused on the low-frequency part of the image. Low frequency sub-band fusion rules can lower image resolution and contrast. Therefore, this study puts forward a fusion rule based on a combination of reference (5) and (6) as follows: Define a variable EA for low-frequency sub-band coefficients of source image A and it is called edge region energy: ) , ( ) * ( ) , ( ) * ( ) , ( ) * ( ) , ( 2 0 3 2 0 2 2 0 1 n m C F n m C F n m C F n m E A j A j A j A + + = (1) Of which, * represents convolution,

Digital Signal Transformation and Computer Image Processing and Analysis Based on Multi-sensor Image Fusion