Noise-aware infrared polarization image fusion based on salient prior with attention-guided filtering network

Abstract

Infrared polarization image fusion integrates intensity and polarization information, producing a fused image that enhances visibility and captures crucial details. However, in complex environments, polarization imaging is susceptible to noise interference. Existing fusion methods typically use the infrared intensity (S0) and degree of linear polarization (DoLP) images for fusion but fail to consider the noise interference, leading to reduced performance. To cope with this problem, we propose a fusion method based on polarization salient prior, which extends DoLP by angle of polarization (AoP) and introduces polarization distance (PD) to obtain salient target features. Moreover, according to the distribution difference between S0 and DoLP features, we construct a fusion network based on attention-guided filtering, utilizing cross-attention to generate filter kernels for fusion. The quantitative and qualitative experimental results validate the effectiveness of our approach. Compared with other fusion methods, our method can effectively suppress noise interference and preserve salient target features.