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.

通过多模光纤（MMF）成像是一项艰巨的任务。已经开发了一些方法，例如传输矩阵或数字相位共轭，以通过MMF实现成像。但是，所有这些方法似乎都对外部环境和MMF的状态敏感，例如MMF的弯曲状态和运动。在本文中，我们通过实验证明了通过常规神经网络（CNN）通过弯曲的MMF进行的高保真成像。两种方法（准确性和Pearson相关系数）用于评估重建的图像保真度。我们专注于研究MMF条件对重建图像保真度的影响，在该图像中，用于成像的MMF卷曲到不同的直径。发现当物体通过MMF的较小弯曲直径时，该物体的信息可能会丢失，导致重建的图像保真度几乎没有降低。我们表明，即使MMF卷曲到很小的直径（例如5厘米），重建的图像保真度仍然很好。这种新颖的成像系统可能会在内窥镜等领域找到应用。