Single-arm ghost imaging via conditional generative adversarial network

In this study, we develop a single-arm ghost imaging (GI) framework based on a conditional generative adversarial network (cGAN) to improve the image quality and extend the application scenarios of GI. A set of one-dimensional (1D) bucket signals generated by a single-arm GI system and their corresponding ground-truth counterparts is employed to train the cGAN. This allows us to reconstruct a low-noise image from a new 1D bucket signal, while the sequence of random patterns is unnecessary. The results show that the proposed method significantly improves the image quality relative to the conventional GI methods. (letter)