The polarization imaging detector combines polarization imaging with compressed sensing to obtain four polarization angles of information at the same time. By gaining information from a more dimensional dimension, this increases the contrast of the image and improves detection and recognition capabilities. However, the special structure of the polarization imaging detector reduces the imaging resolution. To solve this problem, we propose a combined imaging method that combines polarization imaging and compressed sensing. We compress the polarization information using digital micromirror array encoding, analyze the influence of the DMD on polarization image errors, and reconstruct the high-resolution polarization information image using deep learning networks. Compared to traditional compressed sensing reconstruction methods, our network achieves better reconstruction results and has higher peak signal-to-noise ratio (PSNR).
Aiming at the problems such as low efficiency of initial structure optimization design of traditional refractive optical system and overreliance on experience in structure selection. In this paper, an initial structure automatic optimization design method of refractive optical system based on deep learning is proposed. The structural characteristic data of the reference lens in the optical lens library are learned through supervised training. Unsupervised training model based on ray tracing is constructed to improve the generalization ability of deep neural network model. Through the network model generated by training, the optical system structure parameters including real glass are output, and the automatic optimal design of the initial structure of the refractive optical system is realized. The design results show that the initial structure spot radius of optical system in full field and full spectrum optimized by network model are close to the reference lens. The initial structure of the optical system can be designed according to different focal length requirements. The success rate of one million initial structures designed in this paper is greater than 96.403%, which indicates that the network model has good generalization ability. The method proposed in this paper contributes to automatically generate the initial structure of the refractive optical system rapidly and provides a new solution for the optimization of complex optical system.
In DPSK communication system, the traditional way for bias voltage is loading a fixed bias voltage on the electro-optic modulator. For the influence of the temperature changes, the half-wave voltage of the electro-optic modulator may change and the DC bias supply voltage will have a certain degree of random fluctuations at the meantime which will cause the DC bias point of the modulator drift and consequently the communication systems are affected. To enhance the stability of the DPSK optical communication system and control the bias of Mach-Zehnder electro-optic modulators automatically, a PID control method has been proposed in this paper. After the actual operation, a DPSK signal transmission with transfer rate of 5Gb/s is built. Using the complex spectrum analyzer, stable signal and the constellation can be received. The automatic control system basically meets the needs of the DPSK transmission system of high stability, high accuracy and capacity of resisting disturbance.
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