反射(计算机编程)
传输(电信)
光学
光纤
光学成像
人工神经网络
计算机科学
模式(计算机接口)
迭代重建
基质(化学分析)
材料科学
人工智能
物理
电信
复合材料
程序设计语言
操作系统
作者
Yijie Zheng,George S. D. Gordon
摘要
Needle-thin optical fibre imaging systems using multimode fibre show considerable potential for facilitating advanced medical endoscopes that can capture high-resolution images in challenging regions of the body, such as the brain or blood vessels. However, these systems experience significant optical distortion whenever the fibre is disturbed. To address this, it is crucial to calibrate the fibre transmission matrix (TM) in vivo immediately before conducting the imaging process since TM is highly sensitive to temperature variations and bending. We therefore present a reflection-mode TM reconstruction model using U-net based convolutional neural networks with a custom loss function used for arbitrary global phase compensation, which reduced computational time to ~1s. We demonstrated this model by reconstructing 64 × 64 complex-valued fibre TMs through a reflection-mode optical fibre system and tested by reconstructing widefield images with ≤ 9% image error. We anticipate this neural network-based TM reconstruction model with the custom loss function designed will lead to new AI models that deal with phase information, for example in imaging through optical fibre, holographic imaging and projection, where both phase control and speed are required.
科研通智能强力驱动
Strongly Powered by AbleSci AI