Polarization can provide information largely uncorrelated with the spectrum and intensity. Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields, e.g., ocean observation, remote sensing (RS), biomedical diagnosis, and autonomous vehicles. Recently, with the increasing amount of data and the rapid development of physical models, deep learning (DL) and its related technique have become an irreplaceable solution for solving various tasks and breaking the limitations of traditional methods. PI and DL have been combined successfully to provide brand-new solutions to many practical applications. This review briefly introduces PI and DL’s most relevant concepts and models. It then shows how DL has been applied for PI tasks, including image restoration, object detection, image fusion, scene classification, and resolution improvement. The review covers the state-of-the-art works combining PI with DL algorithms and recommends some potential future research directions. We hope that the present work will be helpful for researchers in the fields of both optical imaging and RS, and that it will stimulate more ideas in this exciting research field.

This study introduces a novel image reconstruction technique based on a diffusion model that is conditioned on the native data domain. Our method is applied to multi-coil MRI and quantitative MRI (qMRI) reconstruction, leveraging the domain-conditioned diffusion model within the frequency and parameter domains. The prior MRI physics are used as embeddings in the diffusion model, enforcing data consistency to guide the training and sampling process, characterizing MRI k-space encoding in MRI reconstruction, and leveraging MR signal modeling for qMRI reconstruction. Furthermore, a gradient descent optimization is incorporated into the diffusion steps, enhancing feature learning and improving denoising. The proposed method demonstrates a significant promise, particularly for reconstructing images at high acceleration factors. Notably, it maintains great reconstruction accuracy for static and quantitative MRI reconstruction across diverse anatomical structures. Beyond its immediate applications, this method provides potential generalization capability, making it adaptable to inverse problems across various domains.

Vertebra segmentation from biplanar whole-spine radiographs is highly demanded in the quantitative assessment of scoliosis and the resultant sagittal deformities. However, automatic vertebra segmentation from the radiographs is extremely challenging due to the low contrast, blended boundaries, and superimposition of many layers, especially in the sagittal plane. To alleviate these problems, we propose a lightweight pyramid attention quick refinement network (LPAQR-Net) for efficient and accurate vertebra segmentation. The LPAQR-Net consists of three components: (1) a lightweight backbone network (LB-Net) to prune network parameters and memory footprints to strike an optimal balance between speed and accuracy, (2) a series of global attention refinement (GAR) modules to selectively reuse low-level features to facilitate the feature refinement, and (3) an attention-based atrous spatial pyramid pooling (A-ASPP) module to extract weighted pyramid contexts to improve the segmentation of blurred vertebrae. Moreover, the multi-class training strategy is employed to alleviate the over-segmentation of adjacent vertebrae. Evaluation results on both frontal and lateral radiographs of 332 AIS patients show our method achieves accurate vertebra segmentation with significant reductions in inference time and computational demands compared to the state-of-the-art. Meanwhile, results on the public AASCE2019 dataset also demonstrate the good generalization ability of our model. It is the first attempt to explore the lightweight network for vertebra segmentation from biplanar whole-spine radiographs. It simulates radiologists gathering nearby contexts for accurate and robust vertebra boundary inference. The method can provide efficient and accurate vertebra segmentation for clinicians to perform a fast and reproducible spinal deformity evaluation.

Polarization characteristics are significantly crucial for tasks in various fields, including the remote sensing of oceans and atmosphere, as well as the polarization LIDAR and polarimetric imaging in scattering media. Many polarimetric metrics (such as the degree of polarization, polarization angle diattenuation, and depolarization) have been proposed to enrich the characterization and improve the task performance in scattering media; yet, their related efficacy is limited, especially in high turbidity conditions. The indices of polarimetric purity (IPPs), including three different depolarization metrics, have been successfully applied to biomedical diagnosis. However, it is still debatable whether IPPs also are excellent metrics for identifying or distinguishing objects in scattering media. In this work, we seek to provide physical insights into the application of distinguishing and identifying different objects via IPPs. Imaging experiments are devised and performed on different objects, e.g., metals and plastics, under different turbidity levels, demonstrating the superiority of IPPs as excellent metrics for object identification in scattering conditions. The experimental results show that the IPPs images can enhance image contrast and improve discriminability, as well as break the limitation of traditional intensity-model imaging techniques when further combined with dehazing or enhancing algorithms. Importantly, as the used Mueller matrix (MM) and the related IPPs can also be obtained via other types of MM polarimeters (e.g., PolSAR and MM microscopy), the proposed solution and idea have potential for such applications as biomedical imaging, photogrammetry, and remote sensing.

Bulk Nb superconducting radio-frequency (SRF) cavities are widely used in accelerators, and their accelerating gradient and general performance are limited by the superheating field ( B _sh ). To push the theoretical limit of the B _sh , new multilayer structures are required. We fabricated FeSe-coated Nb films using pulsed laser deposition, performed structural characterizations, and measured the transport and magnetic properties for this superconductor-superconductor bilayer structure with smooth surface. Additionally, the measured B _c1 of FeSe-coated Nb film is greatly enhanced, while the B _sh of the FeSe layer is expected to be higher than that of bulk Nb, yet the superconducting transition temperature ( T _c ) is less than 5 K. This work presents the first fabrication of a new coating layer: FeSe deposited on Nb, showing the possibility of using iron-based materials for multilayer structures in SRF cavities.