基于高斯模糊逻辑和ADCSCM的红外与可见光图像融合算法

为了克服当前的红外与可见光图像融合算法存在着目标不够突出、纹理细节丢失等现象，本文提出了一种基于高斯模糊逻辑和自适应双通道脉冲发放皮层模型（Adaptive Dual-Channel Spiking Cortical Model, ADCSCM）的红外与可见光图像融合算法。首先，使用非下采样剪切波变换（Non-Subsampled Sheartlet Transform, NSST）将源图像分解为低频和高频部分。其次，结合新拉普拉斯能量和（New Sum of Laplacian, NSL）与高斯模糊逻辑，设定双阈值来指导低频部分进行融合；同时，采用基于ADCSCM的融合规则来指导高频部分进行融合。最后，使用NSST逆变换进行重构来获取融合图像。实验结果表明，本文算法主观视觉效果最佳，并在互信息、信息熵和标准差3项指标上高于其他7种融合算法，能够有效突出红外目标、保留较多纹理细节，提高融合图像的质量。

Infrared and Visible Image Fusion Algorithm Based on Gaussian Fuzzy Logic and Adaptive Dual-Channel Spiking Cortical Model

To overcome the shortcomings of current infrared and visible image fusion algorithms, such as non-prominent targets and the loss of many textural details, a novel infrared and visible image fusion algorithm based on Gaussian fuzzy logic and the adaptive dual-channel spiking cortical model (ADCSCM) is proposed in this paper. First, the source infrared and visible images are decomposed into low- and high-frequency parts by non-subsampled shearlet transform (NSST). Then, these are combined with the new sum of the Laplacian and Gaussian fuzzy logic, and dual thresholds are set to guide the fusion of the low-frequency part; simultaneously, the fusion rule based on the ADCSCM is used to guide the fusion of the high-frequency part. Finally, the fused low- and high-frequency parts are reconstructed using inverse NSST to obtain the fused image. The experimental results show that the proposed algorithm has the best subjective visual effect and is better than the other seven fusion algorithms in terms of mutual information, information entropy, and standard deviation. Furthermore, the proposed algorithm can effectively highlight the infrared target, retain more textural details, and improve the quality of the fused image.