Deep-learning-based ghost imaging

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.

# Random lasers and scattering media # Physics and Astronomy # Sparse and Compressive Sensing Techniques # Engineering # Optical Imaging and Spectroscopy Techniques # Medicine

Meng Lyu, Wei Wang, Hao Wang, et al. (2017). "Deep-learning-based ghost imaging". Scientific Reports. https://doi.org/10.1038/s41598-017-18171-7