High-fidelity imaging through multimode fibers via deep learning
Jun Zhao, Xuanxuan Ji, Minghai Zhang, Xiaoyan Wang, Ziyang Chen, Yanzhu Zhang, Jixiong Pu
Abstract
Abstract Imaging through multimode fibers (MMFs) is a challenging task. Some approaches, e.g. transmission matrix or digital phase conjugation, have been developed to realize imaging through MMF. However, all these approaches seem sensitive to the external environment and the condition of MMF, such as the bent condition and the movement of the MMF. In this paper, we experimentally demonstrate the high-fidelity imaging through a bent MMF by the conventional neural network (CNN). Two methods (accuracy and Pearson correlation coefficient) are employed to evaluate the reconstructed image fidelity. We focus on studying the influence of MMF conditions on the reconstructed image fidelity, in which MMF for imaging is curled to different diameters. It is found that as an object passes through a small bent diameter of the MMF, the information of the object may loss, resulting in little decrease of the reconstructed image fidelity. We show that even if MMF is curled to a very small diameter (e.g. 5 cm), the reconstructed image fidelity is still good. This novel imaging systems may find applications in endoscopy, etc.