Long-distance imaging in time-varying scattering media, such as atmosphere, is a significant challenge. Light is often heavily diffused while propagating through scattering media, because of which the clear imaging of objects concealed by media becomes difficult. In this study, instead of suppressing diffusion by multiple scattering, we used natural randomness of wave propagation through atmospheric scattering media as an optimal and instantaneous compressive imaging mechanism. A mathematical model of compressive imaging based on the modulation of atmospheric scattering media was established. By using the Monte Carlo method, the atmospheric modulation matrix was obtained, and the numerical simulation of modulation imaging of atmospheric scattering media was performed. Comparative experiments show that the atmospheric matrix can achieve the same modulation effect as the Hadamard and Gaussian random matrices. The effectiveness of the proposed optical imaging approach was demonstrated experimentally by loading the atmospheric measurement matrix onto a digital micromirror device to perform single pixel compressive sensing measurements. Our work provides a new direction to ongoing research in the field of imaging through scattering media.
The problem of long-distance imaging through timevarying scattering media, such as the atmosphere, is encountered in many science fields. Recent studies have demonstrated that random atmospheric variability can be considered a spatial light modulator in compressed sensing imaging. However, the quality of the reconstructed image needs to be further improved. In this paper, we propose a distributed cumulative synthesis method to improve the compressed sensing image reconstruction based on atmospheric modulation. For multiple original images of various types, the compressed sensing imaging simulation experiment with different sampling rates was conducted using the distributed cumulative synthesis method. The simulation results show that, compared with the imaging method using a single light source, the distributed cumulative synthesis method can effectively improve the quality of the reconstructed image, whether it is full sampling or undersampling. In addition, a sparsity impact factor is defined to quantify the reconstruction ability of the measurement matrix obtained by the distributed cumulative synthesis method. This value can be used as an evaluation index for the optimized design of the measurement matrix by the distributed cumulative synthesis method. Noise analysis shows that the proposed method has better anti-noise performance than the single light source imaging method.
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