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.

Underwater images always suffer from low contrast and inaccurate colors due to scattering and absorption by particles when the target light propagates through turbid water. In this paper, we first found that a lot of intensity space is occupied by fewer pixels, called ‘tails’, on both sides of the histograms for the red, green and blue channels of the image. Based on this histogram attenuation prior and taking account of the advantage of a polarization filter we proposed an effective polarimetric recovery method to enhance the underwater image quality, which includes a specially designed histogram processing method, named ‘cut-tail histogram stretching’. This processing overcomes the limitation of traditional histogram-based methods and can further improve the restoration performance. The experimental results corresponding to underwater scenes with different turbidities and colors show that the proposed method can simultaneously enhance the image contrast and reduce the color distortion to some extent, and thus realize clear underwater vision.

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.