Polarization image fusion with self-learned fusion strategy

Polarization image fusion aims to integrate intensity and degree of linear polarization images into one with more details, which is beneficial to improve the ability of targets detection under complex background. The fusion strategies in conventional methods are designed in a hand-crafted way and not robust to different fusion tasks. In this paper, we propose a novel and deep network to address the polarization image fusion issue with self-learned strategy. The network consists of Encoder, Fusion, and Decoder layers. Feature maps extracted by Encoder are fused, then fed into Decoder to generate fused images. Besides, a novel loss function is adopted to train the network in an unsupervised way, without ground truth of fused images. To verify the advantage, the network trained on polarization images is also used to infrared and visible images fusion, and multi-focus image fusion. Experimental results showed that our method outperforms several state-of-the-art methods in terms of visual quality and quantitative measurement. The proposed fused method can be applied in the military and civilian fields such as camouflage and hidden targets detection, medical diagnosis, and environmental monitoring. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The proposed deep network method for polarization image fusion with self-learned strategy and unsupervised training outperforms state-of-the-art methods in visual quality and quantitative measurement. It can be applied in military and civilian fields for camouflage, hidden targets detection, medical diagnosis, and environmental monitoring.