提出了一种基于非下采样剪切波变换(NSST)和参数自适应简化型脉冲耦合神经网络(SPCNN)相结合的水下偏振图像融合方法。对水下目标物进行图像采集获得目标物的线偏振度图像和偏振光强图像;对两幅图像进行NSST分解获得其多尺度和多方向子带系数,通过参数自适应SPCNN模型融合两幅图像的高频方向子带系数,采用基于区域能量自适应加权的融合方法融合两幅图像的低频子带系数;对融合后的高频方向子带和低频方向子带进行逆NSST重建融合图像。实验结果表明,与其他偏振图像融合方法相比,本文方法可以探测到水下目标物的更多细节和显著特征,主观视觉感受和客观评价方面都有较大的提升。

We propose a method based on nonsubsampled shearlet transform (NSST) and parameter-adaptive simplified pulse coupled neural network (SPCNN) for underwater polarization image fusion. Firstly, the degree of linear polarization and polarized light intensity images of underwater objects are acquired. Then NSST decomposition is performed on the two images to obtain their multi-scale and multi-direction subband coefficients. The high frequency direction subband coefficients of the two images are fused by the parameter adaptive SPCNN model. The low frequency subband coefficients of the two images are fused by an adaptive weighted fusion method based on regional energy. Finally, the fused image is reconstructed by inverting NSST to the high frequency direction subbands and low frequency subbands. Experimental results show that compared with other polarization image fusion methods, the proposed method can detect more details and significant features of underwater objects, and improve subjective visual perception and objective evaluation.