一种基于蚁狮最大熵算法与引导滤波的图像融合算法

传统红外与可见光图像融合算法中易出现目标提取不够充分、细节丢失等问题，导致融合效果不理想，从而无法应用于目标检测、跟踪或识别等领域。因此，该文提出一种基于蚁狮优化算法(ALO)改进的最大香农(Shannon)熵分割法结合引导滤波的红外与可见光图像融合方法。首先，使用蚁狮最大熵分割法(ALO-MES)对红外图像进行目标提取，然后，对红外和可见光图像使用非下采样剪切波变换(NSST)，并对获得的低频和高频分量进行引导滤波。由提取的目标图像与增强后的红外和可见光低频分量通过低频融合规则得到低频融合系数，增强后的高频分量通过双通道脉冲发放皮层模型(DCSCM)得到高频融合系数，最后经NSST逆变换得到融合图像。实验结果表明，所提算法能够得到目标明确、背景信息清晰的融合图像。

An Image Fusion Algorithm Based on Ant Lion Optimized Maximum Entropy Segmentation and Guided Filtering

Traditional fusion algorithms of infrared and visible images often have defects such as insufficient target extraction and loss of details, which lead to unsatisfactory fusion effects, and the fused image can not be applied to target detection, tracking or recognition. Therefore, a fusion method of infrared and visible images based on guided filtering and improved maximum Shannon entropy segmentation method using Ant Lion Optimization algorithm (ALO) is proposed. First, Ant Lion Optimized Maximum Entropy Segmentation (ALO-MES) algorithm is used to extract the target from infrared image. Then, the Non-Subsampled Shearlet Transform (NSST) is performed on the infrared and visible images to obtained the low frequency and high frequency sub-bands, and conduct guided filtering for obtained sub-bands. The low-frequency fusion coefficient is obtained from the extracted target image and the enhanced infrared and visible low-frequency components through the fusion rule based on ALO-MES. And the high-frequency fusion coefficient is obtained by the enhanced high-frequency sub-bands components through Dual-Channel Spiking Cortical Model (DCSCM). Finally, the fusion image is obtained by inverse NSST transform. The experimental results show that the proposed algorithm can get fusion image with clear target and background information.