Deep learning for efficiently imaging through the localized speckle field of a multimode fiber

Abstract

Due to the occurrence of redundant speckle, multimode fiber (MMF) imaging is extremely challenging. Our work studies the relationship between the effective feature distribution of the speckle field and the local spatial position and area, and proves that the information distribution of the speckle is highly redundant. The effective feature refers to the phase and amplitude information of the optical field carrying the image point information and the co-exciting very redundant information due to mode dispersion, interference, coupling, and entrained noise through transmission. The neural network Swin-Unet can well learn the association information between global and local features, greatly simplifies the fitting of the MMF end-to-end global mapping relationship, and achieves high-fidelity reconstruction from the local speckle field to the global image. This work will contribute to the realization of MMF real-time large-field endoscopic imaging.