Robust Polarization Orientation Method Based on Task Mutual Enhancement Under Global Noise Environment

The camera-based polarization sensor provide an effective solution for orientation in satellite-denied environments. A key issue in polarization image processing is the solar meridian extraction (SME). However, in the presence of global noise in the polarization image, such as low exposure conditions at night, the performance of existing SME methods are significantly degraded due to the lack of effective polarization information. To alleviate this problem, some methods reduce the noise by increasing the exposure time, but at the expense of dynamics. To improve the environmental adaptability and dynamic performance of the system in the global noise environment, we conduct an unsupervised image restoration (IR) framework and a task mutual enhancement module (TMEM). The IR framework is based on disentangled representation, with each unidirectional subnetwork embedding a loop for noise removal and generation, which can remove the global noise in the angle of polarization (AoP) image. Moreover, TMEM is proposed to promote task-level information interaction between IR and SME, thereby achieving simultaneous improvement in the performance of IR and SME. Specifically, SME constrains the SM in the restored polarization images to be straight, obvious and azimuth-invariant. Conversely, the noise-free polarization images in IR improves the accuracy of SME. Extensive experimental results demonstrate that the proposed method can significantly improve the quality of AoP images and implement accurate and robust orientation under global noise environment. Under 0.2 second (reduced by 25 times) exposure at low-light condition, the dynamic orientation accuracy of the proposed method can achieve 0.74°. Ablation study demonstrate the necessity of jointly learning SME and IR and the effectiveness of TMEM. © 1965-2011 IEEE.