一种邻域一致性的NSCT域多传感器图像融合算法

针对同一场景多传感器图像融合问题，提出了一种基于邻域特性的非采样Contourlet变换域融合新算法．首先对待融合图像进行非采样Contourlet变换分解，由邻域平均能量与方差构造各点的能量方差决策值，基于决策值最大原则选择低频子带系数，从而在保持图像亮度的同时融合更多的边缘细节;基于邻域能量最大原则选择带通方向子带系数，以保留更多的边缘．最后反变换得到融合图像．采用多聚焦图像及红外与可见光图像进行仿真实验，并对融合结果进行了主客观评价．实验结果表明，该算法较好地融合了亮度及边缘细节，避免了引入人为噪声，得到了具有更好的视觉效果和量化指标的融合图像．

Multi-sensor image fusion algorithm considering neighborhood consistency in the nonsubsampled contourlet transform domain

For the fusion problem of the multi-sensor images of the same scene, a new algorithm is proposed based on neighbor energy and variance in the nonsubsampled Contourlet transform (NSCT) domain. Source images are firstly decomposed in the NSCT domain. For low frequency sub-band coefficients selection, the decision value of variance and energy based on neighbor variance and average neighbor energy is constructed for each pixel, and the rule based on the maximum of the decision value is adopted, so as to keep both image luminance and image details. For band-pass directional sub-band coefficients selection, the rule of maximum neighbor energy is used to keep more edge information. Finally the fused image is obtained through inverse transform. The algorithm has been used to merge multi-focus images and also infrared and visible light images. Experimental results indicate that the proposed method avoids the introduction of artifacts, with better edge details and luminance information, so that the fused image has a better subjective visual effect and objective evaluation criteria.