Passive Polarization Imaging Research Articles

Multi-polarization fusion generative adversarial networks for clear underwater imaging

Polarization imaging has become a promising way for clear underwater vision, which depends on the difference of polarization characteristics between backscattered light and target signal. In this paper, to achieve clear underwater color polarization imaging, we propose a learning-based method that uses multi-polarization fusion adversarial generative networks to learn the relationship between polarization information and object radiance. The proposed method is especially designed for multiple polarimetric images, which can effectively extract the polarization correlations of images with different polarization states. Moreover, to train the proposed network, we build, for the first time to our knowledge, a color polarization image dataset from natural underwater environments through passive polarization imaging. The experimental results in laboratory and natural underwater environments show that it is feasible to introduce polarization information into learning-based image recovery, and deep learning technology is conducive to the extraction of polarization information. Comparing with other methods, the proposed method can effectively remove the backscattered light and recover the object radiance.

Review of visualization methods for passive polarization imaging

Collected sources for the different types of visualizations used in the field of polarization imaging are not extensive. Here, we survey and review the different visualization techniques in passive polarimetric imaging. Analysis of the methods is done by applying various concepts from the field of visualization. We provide recommendations for choosing a visualization based on the data structure, spatial frequency, and analysis goals.

Fundamental limits of target detection performance in passive polarization imaging.

We quantitatively determine the target detection performance of different passive polarization imaging architectures perturbed by signal-independent detection noise or signal-dependent Poisson shot noise. We compare the fully adaptive polarimetric imager and the best channel of a static polarimetric imager, and in each case, we compare the use of a polarizer and a polarizing beam splitter as the polarization analyzing device. For all these configurations, we derive a closed-form expression of the target/background separability and quantify the performance gain brought by polarization imaging compared to standard intensity imaging. We show in particular that all the considered polarimetric imaging configurations but one require a minimum value of the polarimetric contrast in order to outperform intensity imaging. The only configuration that always performs better than intensity imaging uses a polarizing beam splitter in the presence of background shot noise. These results are useful in evaluating the fundamental limits of the gain brought by polarization imaging and determining, in practice, which type of imaging architecture is preferable for a given application.