Infrared polarization and intensity image fusion method based on multi-decomposition LatLRR

Abstract

With the development of infrared imaging technology, the study of infrared polarization and infrared intensity image fusion has great potential for application. In this paper, we propose a fusion method based on multi-decomposition latent low-rank representation (LatLRR) and combine it with dual- simplified pulse coupled neural network (D-SPCNN) for the improvement of detail layer fusion. First, the source images are decomposed into the low-rank part and saliency part using LatLRR, and the weight map of the low-rank part is calculated as the final base layer using the luminance weighting strategy. Then, the saliency part is decomposed by LatLRR again to get the detail layers after multiple decompositions, and the saliency part is used as the input of D-SPCNN to output the activated detail weight map. After that, the final detail layer is obtained by summing the results of the multiple times detail weighting strategy. Finally, the detail layer and the base layer are combined and reconstructed to get the fused image. Our method has an excellent effect of improving brightness, while retaining sufficient image details. The experimental results show that our proposed method obtains better performance in both objective evaluation and subjective metrics when compared with other methods.