The division of focal plane (DoFP) polarization imaging sensors, which can simultaneously acquire the target’s two-dimensional spatial information and polarization information, improves the detection resolution and recognition capability by capturing the difference in polarization characteristics between the target and the background. In this paper, we propose a novel polarization imaging method based on deep compressed sensing (DCS) by adding digital micromirror devices (DMD) to an optical system and simulating the polarization transmission model of the optical system to reconstruct high-resolution images under low sampling rate conditions. By building a simulated dataset, training a polarization super-resolution imaging network, and showing excellent reconstructions on real shooting scenes, compared to current algorithms, our model has a higher peak signal-to-noise ratio (PSNR), which validates the feasibility of our approach.

Aiming at the problem that traditional design methods make it difficult to control the polarization aberration distribution of optical systems quickly and accurately, this study proposes an automatic optimization design method for polarization optical systems based on deep learning. The unsupervised training model based on ray tracing and polarized ray tracing was constructed by learning the reference lens structural feature data from the optical lens library, and the generalization ability of the deep neural network model was improved to achieve the automatic optimization design of the polarized optical system. The design results show that the optical system structure optimized by the network model is close to the reference lens in the full field of view and the full spectrum and that the optical system structure can be designed for different focal length requirements. The success rate of 1 million groups of initial structures designed is better than 96.403%, and the polarization effect of the optical system is effectively controlled. The proposed deep learning approach to optical design provides a new solution for future complex optical systems and also provides an effective way to improve the design accuracy of special optical systems such as polarization optical systems.

Superresolution (SR) imaging technology based on compression coding has always been considered as the key to break through the geometric resolution of the detector. In addition to factors such as the reconstruction algorithm and mounting platform vibrations, the impact of inherent errors in the optical system itself on the reconstruction results of SR imaging is also obvious. To address this issue, a study on the design of the SR optical system and the influence of optical alignment errors on SR imaging was conducted. The design of the SR optical system based on digital micro-mirror device (DMD) for long-wave infrared wavelength was completed, and an athermal analysis of the system was carried out. The design results showed that the SR optical system has good imaging quality in the operating temperature range. The imaging model of the DMD SR imaging optical system is established according to the designed SR optical system. We investigated the influence of various alignment errors, including decenter, tilt, lens interval error and defocus, on the imaging properties of the SR optical system. Various random combinations of alignment errors were introduced into the optical system, respectively, and the SR reconstructed image quality of the imaging system was analyzed using the inverse sensitivity method to obtain the tolerance limits when the system was assembled. Finally, the effectiveness of the method to obtain the alignment tolerance limit of the compression coding SR imaging optical system was verified through a desktop demonstration experiment.