基于Shearlet变换的红外与可见光图像自适应融合

Shearlet based adaptive fusion of infrared and visible images

提出一种基于Shearlet变换的红外与可见光图像自适应融合算法。算法首先对待融合图像进行Shearlet变换;然而采用粒子群优化算法确定出低频成分的最佳融合权值,自适应地对红外与可见光图像的Shearlet低频系数进行整合,利用Shearlet变换对边缘、轮廓等细节特征的准确定位,采用加权局部能量最大准则对Shearlet高频系数进行融合;最后对融合系数进行逆Shearlet变换得到融合图像。与现有的部分算法进行对比实验,结果表明本文算法获得较好地融合效果。

An adaptive fusion method of infrared and visible images based on shearlet transform is presented.Firstly,the source images are transformed by shearlet transform.And then,the low-frequency coefficients are adaptively fused by the optimal fusion weights,which are selected by particle swarm optimization.With the better representation of Shearlet for edge and contour,the weighted local energy rule is employed to select the better high-frequency coefficients to fusion.Finally,the fused coefficient is transform by inverse Shearlet transform to obtain fused image.Compared to the other existing methods,the experimental results show the performance of the proposed method is much better.