4.4 Article

Computational ghost imaging through a dynamic scattering medium based on a convolutional neural network from simulation

Journal

LASER PHYSICS LETTERS
Volume 20, Issue 5, Pages -

Publisher

IOP Publishing Ltd
DOI: 10.1088/1612-202X/acc245

Keywords

dynamic scattering medium; computational ghost imaging; deep learning; scattering imaging

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This article presents an imaging method through a dynamic scattering medium based on computational ghost imaging and a convolutional neural network. The CNN is used to improve the quality of ghost imaging, and its training set is obtained from numerical simulation rather than actual experiments, reducing the workload significantly. A concise mathematical model is provided to reflect the absorption and scattering effects of the dynamic medium. By adding Gaussian white noise to the detected light intensity sequence, the undulation caused by the dynamic scatterer is simulated, and the network is trained under these conditions. Compared to the dataset without noise, our proposed method demonstrates better performance in imaging through a dynamic scattering medium, for both simple binary objects and complex grayscale ones. The effectiveness of this method has been verified in experiments with scattering medium rotated at different speeds.
An imaging method through a dynamic scattering medium is presented based on computational ghost imaging (CGI) and a convolutional neural network (CNN). The CNN is adopted to improve CGI quality, and its training set is obtained from numerical simulation rather than actual experiments, which greatly reduces the workload. A concise mathematical model is given to reflect the absorption and scattering effects of the dynamic medium. By adding Gaussian white noise with different intensities to the detected light intensity sequence, the undulation caused by dynamic scatterer is simulated, and then the network is trained under these conditions. Compared to the dataset without adding noise, our proposed method leads to a better performance of the trained network in imaging through a dynamic scattering medium, not only for the simple binary objects, but also the complex grayscale ones. The effectiveness of this method has been verified in experiments of scattering medium rotated at different speeds.

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