Research on single-pixel imaging method in the complex environment

Abstract

Aiming at the problem that the network models of traditional intelligent single-pixel imaging methods have poor suppression ability in complex environments (e.g., smoke, fog, haze, etc.), a single-pixel imaging method based on random discarding mechanism (DSPINet) is proposed. The method integrates compressed sensing and deep learning. It compresses and samples the image directly, discards part of the information, and reconstructs the image with a small number of the one-dimensional detection signal. In view of the fact that most of the data of traditional single-pixel imaging methods are obtained in a controlled environment and do not have good generalization ability, this paper introduces the random discarding mechanism into the network model to simulate the light scattering loss and system noise in the complex environment. The test results show that when the simulation test’s sampling rate is 10.00 %, the proposed DSPINet model is compared with the DLGI (improved), DLBGI, Bsr2-Net, and TVAL3, the structural similarity (SSIM) of reconstructed images is improved by 2.20 %, 4.49 %, 5.68 %, and 63.16 %, respectively. In the actual test of introducing the scattering medium, when the sampling rate is only 3.00–10.00 %, the reconstructed images generated by the DSPINet model have a good ability to suppress the interference and show a high reconstruction accuracy in the complex environment.