Infrared Polarization and Intensity Image Fusion Algorithm Based on the Feature Transfer

Abstract

The features of infrared polarization and intensity images are not finely transferred to the fused image by using traditional fusion algorithms, which leads to a severe blur of the fused image. This study proposes a new infrared polarization and intensity image fusion algorithm based on the feature transfer. First, the contrast features of the infrared polarization image are extracted by the multiscale average filter decomposition with help of standard deviation constraint. The texture features of infrared polarization images are retrieved via non-subsample-shearlet transform at the same time. Second, the difference of the features is measured using the similarity index, which is used as the transfer weight for the infrared polarization feature images during the later phase of the image fusion. Finally, the fused image is obtained by the superimposition of the infrared intensity image and feature images, which are created from the infrared polarization image. The experimental results demonstrated that the proposed method is able to transfer the features of both the infrared intensity image and the polarization image into the fused images. It performs well on both subjective and objective image quality.