We hereby proposed and experimentally demonstrated an active polarization imaging technique, based on wavelength selection, for seeing through highly turbid water where targets are always visually lost. The method was realized by making use of the dependence of light scattering on wavelength in turbid water. Red light illumination was selected to minimize scattering occurring in light propagation and to guarantee accurate estimation of degree of polarization. Experiments demonstrate its contribution to turn targets in highly turbid water from “undetectable” to “detectable.”

Underwater imaging provides human vision system friendly images; however, it often suffers from severe image degradation. This research developed an underwater polarization imaging model, which considers the water scattering effect, as well as absorption effect. It fully explored the polarization information of the target scene that backscattered light is partially polarized and target light is unpolarized. Then backscattered light is first estimated and removed. The target scene’s distance information is derived based upon the polarization information, and then applied to develop a distance-based Lambertian model. This model enables estimation of the intensity loss caused by water absorption and accurate target radiance recovery. Furthermore, real-world experiments show that the developed model handled the underwater image degradation well. In particular, it enables effective color cast correction resulting from water absorption, which traditional imaging methods always ignore.

This study reviews the recent advances in data-driven polarimetric imaging technologies based on a wide range of practical applications. The widespread international research and activity in polarimetric imaging techniques demonstrate their broad applications and interest. Polarization information is increasingly incorporated into convolutional neural networks (CNN) as a supplemental feature of objects to improve performance in computer vision task applications. Polarimetric imaging and deep learning can extract abundant information to address various challenges. Therefore, this article briefly reviews recent developments in data-driven polarimetric imaging, including polarimetric descattering, 3D imaging, reflection removal, target detection, and biomedical imaging. Furthermore, we synthetically analyze the input, datasets, and loss functions and list the existing datasets and loss functions with an evaluation of their advantages and disadvantages. We also highlight the significance of data-driven polarimetric imaging in future research and development.

Underwater imaging method based on polarization information is extremely popular due to its ability to effectively remove the backscattered light. The Stokes vector contains the information of both the degree and angle of polarization of the light wave. However, this aspect has been rarely utilized in image reconstruction. In this study, an underwater polarimetric imaging model is established by fully exploiting this feature of Stokes vectors. The transmission of light wave is described in terms of the polarization information derived from the Stokes vector. Then, an optimization function is designed based on the independent characteristics of target light and backscattered light to estimate the target and backscattered field information. The real-world experiments and mean squared error analysis verify that the proposed method can remove the backscattered light and recover the target information accurately.

This paper presents a near-infrared (NIR) monocular 3D computational polarization imaging method to directly reconstruct the shape of surfaces exhibiting nonuniform reflectance. A reference gradient field is introduced to the weight constraints for globally correcting the ambiguity of the surface normal for a target with nonuniform reflectance. We experimentally demonstrated that our method can reconstruct the shape of surfaces exhibiting nonuniform reflectance in not only the near field but also the far field. Moreover, with the proposed method, the axial resolution can be kept constant even under different object distances as long as the ratio of the focal length to the object distance is fixed. The simplicity and robustness of the proposed method make it an attractive tool for the fast modeling of 3D scenes.

Speckle correlation imaging (SCI) has found tremendous versatility compared with other scattering imaging approaches due to its single-shot data acquisition strategy, relatively simple optical setup, and high-fidelity reconstruction performance. However, this simplicity requires SCI experiments to be performed strictly in a darkroom condition. As background noise increases, the speckle contrast rapidly decreases, making precise interpretation of the data extremely difficult. Here, we demonstrate a method by refining the speckle in the autocorrelation domain to achieve high-performance single-shot imaging. Experiment results prove that our method is adapted to estimate objects in a low signal-to-background ratio (SBR) circumstance even if the SBR is about − 23 d B . Laboratory and outdoor SCI experiments are performed.