Ghost imaging of blurred object based on deep-learning
Zijin Zhang, Chunfang Wang, Wenlin Gong, Dawei Zhang
Abstract
In this paper, a new, to the best of our knowledge, neural network combining a new residual neural network (ResNetV2), the residual dense block (RDB), and eHoloNet is proposed to reconstruct a blurred object. With the theory of ghost imaging, only the bucket signal that passes through the blurred object is necessary for reconstruction. The training sets are ENMNIST, which is used for simulation, and the blurred object is designed by Airy convolution. To test the generalization of the neural network, we use multi-slit as the testing sets. Both simulated and experimental results show that the trained neural network is superior in a generalized reconstruction of the blurred object. In addition, the limitation of the reconstruction is also explained in this work.