多传感器图像融合技术作为信息融合的重要分支和研究热点，已广泛应用在机器视觉、医疗诊断、军事遥感等领域。为了更好地进行多传感器图像融合，将在图像分割、目标识别等领域具有独特优势的脉冲耦合神经网络(pulse coupled neural network, PCNN)引入到多传感器图像融合领域中来，提出了一种基于修正PCNN的多源图像融合方法，该方法在区域分割的基础上，先提取区域特征，然后由特征指导融合过程；同时，从目标区域相对于背景的显著性出发，提出了一种反映目标区域突出性的新特征，并针对传统PCNN参数无法自动设定的难题，提出了基于修正PCNN的参数自动设定方案。实验结果表明，该方法无论在主观视觉效果，还是客观评价参数上均优于基于多分辨分析的融合算法，且克服了传统像素级融合方法中融合图像模糊、对噪声敏感等不足,尤其适用于图像不能严格配准的应用场合。这对于拓宽PCNN的理论研究和实际应用具有一定价值。

Being an efficient method of information fusion, multisensor image fusion has been used in many fields such as machine vision, medical diagnosis, military applications and remote sensing. In this paper, PCNN is introduced in this research field for its interesting properties in image processing, including segmentation, target recognition et al., and a multisensor image fusion scheme based on modified PCNN is proposed. The basic idea of the scheme is to segment all different input images by PCNN and to use this segmentation to guide the fusion process. At the same time, a new region feature, which emphasized the salience of target regions and its neighbors is proposed. Focusing on the famous difficult problem of PCNN, how to determine PCNN parameters adaptively, an adaptive PCNN parameters determination algorithm is also presented in this paper. Experimental results demonstrate that the proposed fusion scheme outperforms the multiscale decomposition based fusion approaches, both in visual effect and objective evaluation criteria. It avoids some of the well-known problems in pixel-level fusion such as blurring effects and high sensitivity to noise, particularly when there is mis-registration of the source images. The research fruits have certain value on the theory research and practical application of PCNN.