Estimation and removal of backscattered light with nonuniform polarization information in underwater environments.

The nonuniform of polarization information of backscattered light has always been a neglected characteristic in polarization underwater imaging, but its accurate estimation plays an important role in the quality of imaging results. Traditional polarization imaging methods assume that the degree of polarization and angle of polarization of backscattered light are constant. In fact, the polarization information of backscattering light is gradual, this assumption makes traditional methods work only in a small area of the camera's field of view, in which the change of the polarization information of backscattered light can be ignored. In this paper, by analyzing the distribution of backscattered light, it is concluded that its polarization information has the characteristics of low-rank. Then, the degree of polarization and angle of polarization of backscattered light were estimated by low-rank and sparse matrix decomposition, and the clear scene was reconstructed. Experimental results show that the proposed method breaks through the limitation of the assumption of backscattered light in traditional polarization imaging method, and expands the detection field under the same conditions, which makes it possible to develop polarization underwater imaging method to the direction of large field of view detection.

Enhancement of underwater vision by fully exploiting the polarization information from the Stokes vector.

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.

Near-infrared monocular 3D computational polarization imaging of surfaces exhibiting nonuniform reflectance.

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.

Polarization-based exploration for clear underwater vision in natural illumination.

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.

Deeply seeing through highly turbid water by active polarization imaging.

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."

Active underwater descattering and image recovery.

Underwater imaging is a promising but challenging topic due to the scattering particles in water, which result in serious light attenuation. Therefore, underwater images suffer from low-contrast and low-resolution issues. In this study, in order to recover high-quality underwater images, the point spread functions (PSFs) are estimated by a slant-edge method. The experiment modulates the illumination source to deal with backscattering and the imager to take two images in orthogonally polarized states. This imaging method benefits the satisfactory edge extraction. The PSF estimation is performed based on the extracted slant edge to enable recovery of the image. In addition, the modulation transfer function (MTF) is introduced to evaluate the resolution variation with the spatial frequencies. It manifests considerable resolution enhancement in the recovered images. Moreover, the proposed underwater image recovery method also reduces the effect from the scattering as an effective compensation to the polarization imaging approach.

Polarization characteristics of objects in long-wave infrared range.

Research on polarization characteristics of objects has become indispensable in the field of target detection. Though widespread studies on applying polarization to target detection and material identification exist, theoretical descriptions have varied widely in accuracy and completeness. Incomplete descriptions of polarization characteristics invariably result in poor demonstration of changes caused by macroscopic influence factors. For objects that are of finite surface, a comprehensive model is built to analyze the polarization characteristics of their thermal emission. With the Stokes theory and the superposition principle of light waves, the relation between the degree of linear polarization and the spatial geometrical parameters, such as the detection distance and the shape of objects, is discussed in the long-wave infrared range in detail. This model can be applied to analyze the linear polarization characteristics among different materials.

Design of a circular polarization imager for contrast enhancement in rainy conditions.

We present the design of a circular polarization imager for imaging in rainy conditions, which is free from image calibration and correction before obtaining the orthogonal-state contrast image. The system employed a quarter wave plate in front of two Wollaston Prisms (WPs) to capture circularly polarized information and to acquire two orthogonally polarized images simultaneously on the charge coupled device (CCD). Along with the WPs, a reimaging part with multiaperture structure composed of two separate specialized reimaging modules, were implemented to make sure the two orthogonally polarized intensity images are exactly indicating the same scene. Exploiting circularly polarized information provides advantages over a linear polarization imaging system when considering the turbulence of media and illumination. Substantial data have demonstrated the effects of the novel designed polarization imaging system.

Polarimetric dehazing utilizing spatial frequency segregation of images.

A procedure for the detection and removal of haze from dense hazy images has been proposed. It involves the analysis on the content of low-spatial-frequency information of a scene. The image contaminated by haze is decomposed into different spatial frequency layers by the wavelet transform, by which the hazy parts of the image are focused on the low-frequency components. A dehazing method combining both the airlight and direct transmission is employed to specially dehaze the low-frequency parts. The high-frequency parts are processed by a transfer function to enhance the clarity of the hazy image. Finally, a dehazed image with high clarity is obtained by image construction which employs the low- and high-frequency components. Experiments and analyses demonstrate the good performance of the scheme in terms of improving the contrast and clarity of hazy images. Particularly, it works well in improving the visual range of images captured in hazy weather conditions.