Analytical modeling of optical field propagation in a scattering medium is essential for computational optics such as iterative imaging reconstructions and end-to-end photonic designs. Despite the existence of the beam propagation method framework and numerical electromagnetic simulation tools for scalar scattering processes, there has been a lack of an analytical model to include the full tensor nature of birefringent scattering. Here, we present an analytical tool with a polarization transfer function tensor to model the field propagation in a birefringent scattering medium by including a full set of field tensor elements and multiple-scattering characteristics. Birefringence-controlled field propagation experiments were successfully carried out to validate the proposed model. With the capability to solve the 3D reconstruction of the scattering potential tensor, we believe the proposed model will provide a more accurate and efficient forward analytical tool for iterative reconstruction of birefringence-related scattering processes.

Spin-orbit interaction (SOI) of light has attracted enormous interest for its potential applications in controlling light at nanoscale and holds promise for future optical information technology and optoelectronics. In particular, the transverse spin in evanescent waves is regarded as distinct manifestation of SOI and an emerging candidate for miniaturizing optoelectronic circuits. However, further application has been hampered by the inability to conveniently generate light field with configurable transverse spin. Here, we present a simple method to generate free-space light field with arbitrary transverse spin utilizing vector beam interference and numerically demonstrate the configurable spin orbital coupling in nanophotonic waveguide. Chiral waveguide with excellent extinction ratio up to 42 dB is achieved in our simulation. The propagating direction of guided mode can be fully controlled via tuning the ellipticity of transverse spin. The device is compatible with existing integrated nanophotonic devices and may find valuable applications in future optical information processing and optical communication technology.

Due to the effect of the complex scattering medium, the photons carrying target information will be attenuated when passing through scattering media, and target localization is difficult. The resolution of the target-position information from scattered images is crucial for achieving accurate target localization in environments such as dense fog in military applications. In this paper, a target localization network incorporating an attention mechanism was designed based on the robust feature resolution ability of neural networks and the characteristics of scattering formation. A training dataset with basic elements was constructed to achieve data decoupling, and then realize the position estimation of targets in different domains in complex scattering environments. Experimental validation showed that the target was accurately localized in speckle images with different domain data by the above method. The results will provide ideas for future research on the localization of typical targets in natural scattering environments.

Target recovery through scattering media is an important aspect of optical imaging. Although various algorithms combining deep-learning methods for target recovery through scattering media exist, they have limitations in terms of robustness and generalization. To address these issues, this study proposes a data-decoupled scattering imaging method based on autocorrelation enhancement. This method constructs basic-element datasets, acquires the speckle images corresponding to these elements, and trains a deep-learning model using the autocorrelation images generated from the elements using speckle autocorrelation as prior physical knowledge to achieve the scattering recovery imaging of targets across data domains. To remove noise terms and enhance the signal-to-noise ratio, a deep-learning model based on the encoder–decoder structure was used to recover a speckle autocorrelation image with a high signal-to-noise ratio. Finally, clarity reconstruction of the target is achieved by applying the traditional phase-recovery algorithm. The results demonstrate that this process improves the peak signal-to-noise ratio of the data from 15 to 37.28 dB and the structural similarity from 0.38 to 0.99, allowing a clear target image to be reconstructed. Meanwhile, supplementary experiments on the robustness and generalization of the method were conducted, and the results prove that it performs well on frosted glass plates with different scattering characteristics.