Deep-learning-based ghost imaging

By Meng Lyu, Wei Wang, Hao Wang, Haichao Wang, Guowei Li, Ni Chen, Guohai Situ. Scientific Reports, 2017

365 citations recorded by the source corpus.

In this manuscript, we propose a novel framework of computational ghost imaging, i.e., ghost imaging using deep learning (GIDL). With a set of images reconstructed using traditional GI and the corresponding ground-truth counterparts, a deep neural network was trained so that it can learn the sensing model and increase the quality image reconstruction. Moreover, detailed comparisons between the image reconstructed using deep learning and compressive sensing shows that the proposed GIDL has a much better performance in extremely low sampling rate. Numerical simulations and optical experiments were carried out for the demonstration of the proposed GIDL.

Subfields: Acoustics and Ultrasonics, Biomedical Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/b28f75371e04c39090c17c2caee2d98b758b2104

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In Acoustics and Ultrasonics

In Biomedical Engineering

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Citations in the corpus

Learning-based lensless imaging through optically thick scattering media (2019, background): “25,33 Third, the trained network is hard to generalize.”
Deep Learning Techniques for Inverse Problems in Imaging (2020, background): “ing [45], holography [46], [47], ghost imaging [48], imaging through scattering media [49]–[51], and non-line-of-sight imaging [52], which is illustrated in Figure 5.”
Far-field super-resolution ghost imaging with a deep neural network constraint (2022, background): “Speci ﬁ cally, it has shown that it allows robust GI reconstruction of high-quality images even when β 10% with high computational ef ﬁ ciency 28 – 31 .”