NSCT域内结合边缘特征和自适应PCNN的红外与可见光图像融合

Fusion of Infrared and Visible Images Based on Edge Feature and Adaptive PCNN in NSCT Domain

针对传统的基于多尺度变换的红外与可见光图像融合,对比度不高,边缘等细节信息保留不充分等问题,结合NSCT变换的多分辨率、多方向特性和PCNN全局耦合、脉冲同步激发等优点,提出一种基于NSCT变换结合边缘特征和自适应PCNN红外与可见光图像融合算法.对于低频子带,采用一种基于边缘的融合方法;对于高频方向子带,采用方向信息自适应调节PCNN的链接强度,使用改进的空间频率特征作为PCNN的外部激励,根据脉冲点火幅度融合子带系数.实验结果验证了该算法的有效性.

To improve the contrast and preserve more image details in the fusion of infrared and visible images,a fusion method for infrared and visible images based on nonsubsampled contourlet transform （NSCT） combined with image edge fea- ture and adaptive pulse coupled neural network （PCNN） is proposed. For the low frequency subband, the fusion is based on edges of images. For the high frequency subbands, the orientation information of each pixel in images is utilized as the linking strength, and a modified spatial frequency is adopted as the input to motivate the adaptive PCNN, and the fire amplitude is em- ployed to determine the coefficients selection. Experimental results indicate the effectiveness of the proposed algorithm.