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Computational ghost imaging based on array sampling
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DOI：10.1364/OE.445000.png)
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Abstract
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High-quality computational ghost imaging under low sampling has always attracted much attention and is an important step in the practical application of computational ghost imaging. However, as far as we know, most studies focus on achieving high-quality computational ghost imaging with one single pixel detector. The high efficiency computational ghost imaging method using multiple single pixel detectors for array measurement is rarely mentioned. In this work, a new computational ghost imaging method based on deep learning technology and array detector measurement has been proposed, which can achieve fast and high-quality imaging. This method can resolve the problem of misalignment and overlap of some pixels in the reconstructed image due to the incomplete correspondence between the array detector and the light field area. At the same time, the problem of partial information loss in the reconstructed image because of the gap between the detection units of the array detector has also been solved. Simulation and experiment results show that our method can obtain high computational ghost imaging quality, even at the low sampling rate of 0.03, and as the detection unit of the array detector increases, the number of sampling is further reduced. This method improves the applicability of computational ghost imaging and can be applied to many fields such as real-time detection and biomedical imaging. (C) 2021 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
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