TSMPN-PSI: high-performance polarization scattering imaging based on three-stage multi-pipeline networks

Dynamically manipulating long-wave infrared polarized thermal radiation by a vanadium dioxide metasurface

Dynamically manipulating the spectra and polarization properties of thermal radiation is the key to counter an infrared polarization imaging system (IPIS) under the different background environments. In this Letter, we propose a phase-change metasurface thermal emitter (PCMTE) composed of vanadium dioxide (VO2) dipole antenna arrays to dynamically manipulate polarized radiation spectra in the long-wave infrared (LWIR) region of 8-14 µm. During the thermally induced and reversible insulator-to-metal transition (IMT) in VO2, by simulating the LWIR images at different polarization angles for the PCMTE and background plates, the PCMTE can realize dynamically tunable LWIR camouflage; then, their degree of linear polarization (DoLP) can be calculated, which can demonstrate that the PCMTE can also achieve dynamically tunable LWIR polarization camouflage at the specific radiation angles and backgrounds. Our proposed PCMTE provides an effective scheme for adaptive IR polarization camouflage.

PE-RASP: range image stitching of photon-efficient imaging through reconstruction, alignment, stitching integration network based on intensity image priors

Single photon imaging integrates advanced single photon detection technology with Laser Radar (LiDAR) technology, offering heightened sensitivity and precise time measurement. This approach finds extensive applications in biological imaging, remote sensing, and non-visual field imaging. Nevertheless, current single photon LiDAR systems encounter challenges such as low spatial resolution and a limited field of view in their intensity and range images due to constraints in the imaging detector hardware. To overcome these challenges, this study introduces a novel deep learning image stitching algorithm tailored for single photon imaging. Leveraging the robust feature extraction capabilities of neural networks and the richer feature information present in intensity images, the algorithm stitches range images based on intensity image priors. This innovative approach significantly enhances the spatial resolution and imaging range of single photon LiDAR systems. Simulation and experimental results demonstrate the effectiveness of the proposed method in generating high-quality stitched single-photon intensity images, and the range images exhibit comparable high quality when stitched with prior information from the intensity images.

Dynamic polarization fusion network (DPFN) for imaging in different scattering systems

Deep learning has broad applications in imaging through scattering media. Polarization, as a distinctive characteristic of light, exhibits superior stability compared to light intensity within scattering media. Consequently, the de-scattering network trained using polarization is expected to achieve enhanced performance and generalization. For getting optimal outcomes in diverse scattering conditions, it makes sense to train expert networks tailored for each corresponding condition. Nonetheless, it is often unfeasible to acquire the corresponding data for every possible condition. And, due to the uniqueness of polarization, different polarization information representation methods have different sensitivity to different environments. As another of the most direct approaches, a generalist network can be trained with a range of polarization data from various scattering situations, however, it requires a larger network to capture the diversity of the data and a larger training set to prevent overfitting. Here, in order to achieve flexible adaptation to diverse environmental conditions and facilitate the selection of optimal polarization characteristics, we introduce a dynamic learning framework. This framework dynamically adjusts the weights assigned to different polarization components, thus effectively accommodating a wide range of scattering conditions. The proposed architecture incorporates a Gating Network (GTN) that efficiently integrates multiple polarization features and dynamically determines the suitable polarization information for various scenarios. Experimental result demonstrates that the network exhibits robust generalization capabilities across continuous scattering conditions.