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.

Imaging in low light is significant but challenging in many applications. Adding the polarization information into the imaging system compromises the drawbacks of the conventional intensity imaging to some extent. However, generally speaking, the qualities of intensity images and polarization images cannot be compatible due to the characteristic differences in polarimetric operators. In this Letter, we collected, to the best of our knowledge, the first polarimetric imaging dataset in low light and present a specially designed neural network to enhance the image qualities of intensity and polarization simultaneously. Both indoor and outdoor experiments demonstrate the effectiveness and superiority of this neural network-based solution, which may find important applications for object detection and vision in photon-starved environments.

Full Stokes imaging can be performed with a continuously rotating retarder in front of a fixed polarizer and a standard camera (RRFP) or a division of a focal plane polarization camera (RRDOFP). We determine the optimal number and duration of intensity measurements through a cycle of the retarder for these two types of setups as a function of instrument and noise parameters. We show that this number mainly depends on the type of noise that corrupts the measurements. We also show that with these setups, the starting angle of the retarder need not be known precisely and can be autocalibrated, which facilitates synchronization of the rotating retarder with the camera. We investigate the precision and feasibility domain of this autocalibration and show the RRDOFP setup has more attractive properties compared with RRFP setup. These results are important to optimize and facilitate the operation of polarization imagers based on a rotating retarder.

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.