基于混合l0l1层分解的红外光强与偏振图像融合算法

红外光强与偏振图像融合能够更全面地描述探测场景的特征,有利于后续的处理工作。本文提出基于混合l0l1层分解的红外光强与偏振图像融合算法,首先,利用混合l0l1层分解对红外偏振与光强图像进行多尺度几何变换;接着,对于低频特征子带图像采用指数局部高斯分布相似度作为红外偏振低频图像融合权重,并将其注入红外光强低频图像中;然后,对于高频子带图像利用局部空间频率和局部能量进行融合,并用主成分分析将两类特征融合图像进行合成,获得高频融合图像;最后,通过重构获得最终融合图像。通过实验对比,本文算法融合结果能够较好地融合两类图像间的互补特征,显著提升融合图像质量。

Fusion Algorithm for Infrared Intensity and Polarization Images Using Hybrid l0l1 Layer Decomposition

A combination of infrared intensity and polarization images can more fully describe the characteristics of a detected scene and facilitate subsequent processing.An algorithm for fusing infrared intensity and polarization images using hybrid l0l1 layer decomposition is proposed.The algorithm consists of the following steps.First,multi-scale geometric transformations are applied to the infrared polarization and intensity images using hybrid l0l1 layer decomposition.Then,in the low-frequency characteristic subband image,the index local Gaussian distribution similarity is adopted as the low-frequency image fusion weight of the infrared polarization image,and the fused infrared polarization image is injected into the low-frequency infrared intensity image.Next,the local spatial frequency and local energy are used to fuse the high-frequency subband image,and the two fused images are combined by principal component analysis to obtain a high-frequency fused image.The final fused image is obtained by reconstruction.An experimental comparison reveals that the algorithm can be used to fuse images of different types with complementary features,and the quality of the fused image is clearly improved.