M
Meng Lyu
Researcher at Chinese Academy of Sciences
Publications - 9
Citations - 837
Meng Lyu is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Digital holography & Deep learning. The author has an hindex of 7, co-authored 8 publications receiving 453 citations.
Papers
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Journal ArticleDOI
Deep-learning-based ghost imaging.
TL;DR: 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.
Journal ArticleDOI
Phase imaging with an untrained neural network.
Fei Wang,Yaoming Bian,Haichao Wang,Meng Lyu,Giancarlo Pedrini,Wolfgang Osten,George Barbastathis,Guohai Situ +7 more
TL;DR: It is experimentally shown that one needs only to feed PhysenNet a single diffraction pattern of a phase object, and it can automatically optimize the network and eventually produce the object phase through the interplay between the neural network and the physical model.
Journal ArticleDOI
eHoloNet: a learning-based end-to-end approach for in-line digital holographic reconstruction.
Hao Wang,Meng Lyu,Guohai Situ +2 more
TL;DR: The eHoloNet is proposed, which can reconstruct the object wavefront directly from a single-shot in-line digital hologram and has strong robustness to the change of optical path difference between reference beam and object light and does not require the reference beam to be a plane or spherical wave.
Journal ArticleDOI
Learning-based lensless imaging through optically thick scattering media
TL;DR: In this article, a hybrid neural network was proposed for computational imaging through a scattering medium with an optical thickness of over 10 times the scattering mean free path, and the reconstruction of image information from various targets hidden behind a white polystyrene slab of 3mm in thickness.
Journal ArticleDOI
Fast autofocusing in digital holography using the magnitude differential.
TL;DR: The results suggest that the proposed digital holographic autofocusing method performs better than other existing methods, in terms of applicability and computation efficiency, with potential applications in industrial and biomedical inspections where automatic focus tracking is necessary.