针对红外与可见光图像的成像特点及目前红外与可见光图像融合中融合图像信息量不足的问题, 结合复剪切波变换(Complex Shearlet transform, CST)及脉冲发放皮层模型(Spiking cortical model, SCM)的优点, 本文提出了一种新的红外与可见光图像融合算法。首先, 利用红外图像目标与背景灰度的显著差异, 通过区域生长方法从红外图像提取目标区域; 然后用 CST对源图像进行分解, 对源图像的目标区域和背景区域系数分别采用不同的融合规则进行融合, 其中背景区域的高频子带系数利用 SCM进行选择; 最后, 经过 CST逆变换重构融合图像。研究结果表明, 与其它的红外与可见光图像融合算法相比, 本方法在视觉效果和客观评价指标上都得到了提升。

The imaging characteristics of the infrared and visual images and the insufficient information content of the fused images considered and the benefits of complex Shearlet transform(CST) and spiking cortical model(SCM)combined with, a new kind of infrared and visual image fusion algorithm is proposed. First, the object distilled from infrared image are segmented by region growing method. Then, CST is utilized for multiscale decomposition of the source images, and the object regions and background region are fused by different rules. The high frequency sub-band coefficients of background region are selected by using the SCM, and finally the fused image is reconstructed by the inverse CST. Experimental results demonstrate that the proposed fusion algorithm outperforms other fusion algorithms in terms of visual appearance and objective evaluation criteria.