Advanced Holographical and Physics Inspired Deep Learning Approaches for Image Transmission through Multimode Optical Fiber

被引：0

作者：

Kazemzadeh, Mohammadrahim ^{[1
]}

Collard, Liam ^{[1
,2
]}

Piscopoa, Linda ^{[1
,3
]}

Pisanoa, Filippo ^{[1
,4
]}

Ciraci, Cristian ^{[1
]}

De Vittorio, Massimo ^{[1
,2
,3
]}

Pisanello, Ferruccio ^{[1
,2
]}

机构：

[1] Ist Italiano Tecnol, Ctr Biomol Nanotechnol, I-73010 Arnesano, LE, Italy

[2] RAISE Ecosyst, Genoa, Italy

[3] Univ Salento, Dipartimento Ingn Innovaz, I-73100 Lecce, Italy

[4] Univ Padua, Dept Phys & Astron G Galilei, Via Marzolo 8, I-35131 Padua, Italy

来源：

DATA SCIENCE FOR PHOTONICS AND BIOPHOTONICS | 2024年 / 13011卷

关键词：

D O I：

10.1117/12.3017089

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recent strides in data-driven and deep learning methods have empowered image and wavefront reconstruction in such environments. This breakthrough finds promising roles in biomedical applications like image transmission and holography. Yet, the reconstructed image quality relies on deep learning model effectiveness in understanding transmission mechanisms. In our presentation, we propose two enhancements. First, employs a novel deep learning architecture inspired by light physics, showcasing enhanced image reconstruction quality and broad problem generalization. The second one is an optical method which boosts data variance through holographic encoding, enabling multi-channel image transmission and improved data fusion via deep learning.

引用

页数：2