Learning to see speckle in the weak laser field through multimode fiber

作     者：JI Yunqi SONG Binbin LI Xueqing LI Yonghui 

作者机构：The Engineering Research Center of Learning-Based Intelligent System (Ministry of Education)， the Key Laboratory of Computer Vision and Systems (Ministry of Education)， and the School of Computer Science and Engineering， Tianjin University of Technology 

出 版 物：《Optoelectronics Letters》 (光电子快报(英文))

年 卷 期：2025年

学科分类：080901[工学-物理电子学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080401[工学-精密仪器及机械] 080203[工学-机械设计及理论] 0804[工学-仪器科学与技术] 0802[工学-机械工程] 0803[工学-光学工程] 

基　　金：Opening Foundation of Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems (2019LODTS004) Tianjin Key Laboratory of Micro-scale Optical Information Science and Technology 

摘      要：Multimode fibers (MMFs) have great potential for endoscopic imaging due to the high number of modes and a small core diameter. Deep learning based on neural networks has received increasing attention in the field of scattering image reconstruction. However， most studies focus on designing complex network architectures to improve reconstruction， but these network models struggle to reconstruct images in a weak laser field. In the paper， a lightweight generative adversarial network model combined with a histogram specification algorithm is designed to reconstruct speckles in the weak laser field through MMF. Experimental results show that the reconstruction results of our algorithm have better metrics. Moreover， the model demonstrates excellent cross-domain generalization ability with regards to the Fashion-MNIST dataset. It is worth mentioning that we found that the speckles after inactivation still retain the ability to be reconstructed， which enhances the robustness of the model