Polarization angle information enhancement method based on polarimetric array imaging.

Polarization imaging, based on the measurement of polarization parameters containing specific physical information, has found extensive applications across various domains. Among these parameters, polarization angle information plays a crucial role in revealing texture details. However, in practical scenarios, noise during image acquisition can lead to significant degradation of polarization angle information. To address this issue, we introduce a novel, to the best of our knowledge, polarization angle information enhancement method based on polarimetric array imaging. Our proposed method utilizes the principles of polarimetric array imaging to effectively restore texture information embedded within polarization angle images. Through the deployment of a self-designed polarimetric array imaging system, we conducted experiments in diverse scenes to validate the efficacy of our approach. The acquired polarization angle data were subjected to our method for enhancement. The experimental outcomes distinctly illustrate the noise suppression capabilities of our method, showcasing its ability to faithfully reconstruct intricate details obscured by substantial noise interference.

Computational imaging and occluded objects perception method based on polarization camera array.

Traditional optical imaging relies on light intensity information from light reflected or transmitted by an object, while polarization imaging utilizes polarization information of light. Camera array imaging is a potent computational imaging technique that enables computational imaging at any depth. However, conventional imaging methods mainly focus on removing occlusions in the foreground and targeting, with limited attention to imaging and analyzing polarization characteristics at specific depths. Conventional camera arrays cannot be used for polarization layered computational imaging. Thus, to study polarization layered imaging at various depths, we devised a flexible polarization camera array system and proposed a depth-parallax relationship model to achieve computational imaging of polarization arrays and polarization information reconstruction under varying conditions and depths. A series of experiments were conducted under diverse occlusion environments. We analyzed the distinctive characteristics of the imaging results obtained from the polarization array, employing a range of array distribution methods, materials, occlusion density, and depths. Our research successfully achieved computational imaging that incorporates a layered perception of objects. Finally, we evaluated the object region's polarization information using the gray level co-occurrence matrix feature method.

High-similarity analytical model of skylight polarization pattern based on position variations of neutral points.

The skylight polarization pattern contains rich information for navigation, meteorological monitoring, and remote sensing. In this paper, we propose a high-similarity analytical model by considering the influence of the solar altitude angle on the neutral point position variations for the distribution pattern of the polarized skylight. A novel function is built to determine the relationship between the neutral point position and solar elevation angle based on a large number of measured data. The experimental results show that the proposed analytical model achieves a higher similarity to measured data compared with existing models. Furthermore, data from several consecutive months verifies the universality, effectiveness, and accuracy of this model.

Adaptive method for estimating information from a polarized skylight.

The acquisition and processing of skylight polarization information forms the cornerstone in modern navigation systems that are developed by imitating certain biological mechanisms. The accuracy of skylight polarization mode information plays a major part in improving the accuracy of polarized light navigation. This paper mainly focuses on developing a methodology that can avoid the error caused by the inaccurate rotation of the polarizer and manual readings from non-electrical equipment, when the time-sequence polarization measurement system is used to obtain the skylight polarization mode information. We propose an adaptive algorithm that can obtain the pictures of angle of polarization and degree of polarization with sets of random rotation angles with no need for precise readings for the rotation angle of the polarizer. By allocating initial random values to rotation angles, a simple iterative estimation method like the Gaussian-Newton method can be used to converge calculated angle of polarization and degree of polarization values to their respective real values. The experiment results show that the proposed method can be used to estimate polarization information with high accuracy and universality under various experiment settings including both sunny and cloudy weathers. Meanwhile, the time efficiency of the proposed method is comparable to traditional methods.