提出了一种基于离散小波变换 DWT(discrete wavelet transform)的红外偏振与光强的融合方法。该算法采用 DWT对源图像进行多尺度分解, 然后对低频子带系数采用局部能量融合, 而对高频子带系数采用消除高频噪声与局部均方差准则结合进行融合, 最终通过逆变换得到重构的融合图像。实验结果表明, 与加权平均融合和绝对值较大法的融合规则相比, 局部标准偏差、粗糙度、对比度、局部熵分别提高了 1.1%, 3.0%, 15.2%, 2.6%, 从而可以看出文中的融合方法具有一定的优势和现实意义。

A fusion algorithm of infrared polarization and intensity based on discrete wavelet transform (DWT) is put forward. DWT is adopted in the algorithm to decompose a source image into multi-scale images. Then the low frequency sub-band coefficient is fused by the local energy. And the high frequency sub-band coefficient is fused by eliminating high frequency noise and incorporating with local mean square error rule. The reconstructed fusion images are got finally through inverse transformation. Experimental results show that local standard deviation, roughness, contrast and local entropy are increased by 1.1%, 3.0%, 15.2%, 2.6% respectively comparing with the fusion rules of weighted average and the largest absolute value. So the fusion method has a certain advantage and reality.