Single‐Pass Wavefront Reconstruction via Depth Heterogeneity Self‐Supervised Neural Operator for Turbulence Correction

IF 10 1区 物理与天体物理 Q1 OPTICS
Haoyu Zhang, Chaoxu Chen, Fujie Li, Jifan Cai, Li Yao, Fang Dong, Yuan Wei, Yinjun Liu, Xinjie Zhang, Yingjun Zhou, Ziwei Li, Junwen Zhang, Jianyang Shi, Nan Chi
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引用次数: 0

Abstract

Turbulence‐induced distortion remains a major bottleneck for high‐fidelity applications such as optical wireless communication and laser‐based remote sensing, as conventional adaptive optics systems struggle to meet the combined demands of bandwidth efficiency, low‐delay and environmental adaptability. Here, a Depth Heterogeneity Self‐supervised Neural Operator (DHSNO) is proposed, a multi‐physics heterogeneous model integrated neural architecture tailored to correct turbulence wavefronts in a single pass without the need for labeled training data. By leveraging dual‐mode intensity detection in a depth‐heterogeneous receiver, DHSNO inherently regularizes the ill‐posed wavefront retrieval problem to deliver robust, high‐accuracy, and low‐latency reconstruction. This capability is validated in both an emulated 50‐meter underwater turbulence channel and a real‐world 5‐meter underwater salinity‐gradient channel, where DHSNO achieves a normalized residual wavefront error below 0.06 with an inference time of 3.6 ms under varying turbulent strengths. Furthermore, this prototype system enabled 12‐Gb/s 4K‐120fps video transmission with near‐perfect fidelity (SSIM > 0.9999) under severe turbulence conditions. These findings not only advance the state‐of‐the‐art in adaptive optics but also provide a scalable framework for next‐generation free‐space and underwater optical systems, underscoring the transformative potential for turbulence correction of integrating physical constraints with data‐driven neural networks.
基于深度非均质自监督神经算子的湍流校正单通波前重建
由于传统的自适应光学系统难以满足带宽效率、低延迟和环境适应性的综合要求,湍流引起的畸变仍然是光通信和激光遥感等高保真应用的主要瓶颈。本文提出了一种深度非均质自监督神经算子(DHSNO),这是一种多物理场非均质模型集成神经结构,可在不需要标记训练数据的情况下在单次通过中校正湍流波前。通过利用深度异构接收器中的双模强度检测,DHSNO固有地正则化了病态波前检索问题,从而提供了鲁棒性、高精度和低延迟的重建。这种能力在模拟的50米水下湍流通道和现实世界的5米水下盐度梯度通道中得到了验证,在不同湍流强度下,DHSNO的归一化残余波前误差低于0.06,推断时间为3.6 ms。此外,该原型系统实现了12 Gb/s 4K - 120fps视频传输,具有近乎完美的保真度(SSIM >;0.9999)在严重的湍流条件下。这些发现不仅推动了自适应光学技术的发展,而且为下一代自由空间和水下光学系统提供了一个可扩展的框架,强调了将物理约束与数据驱动的神经网络相结合的湍流校正的变革潜力。
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来源期刊
CiteScore
14.20
自引率
5.50%
发文量
314
审稿时长
2 months
期刊介绍: Laser & Photonics Reviews is a reputable journal that publishes high-quality Reviews, original Research Articles, and Perspectives in the field of photonics and optics. It covers both theoretical and experimental aspects, including recent groundbreaking research, specific advancements, and innovative applications. As evidence of its impact and recognition, Laser & Photonics Reviews boasts a remarkable 2022 Impact Factor of 11.0, according to the Journal Citation Reports from Clarivate Analytics (2023). Moreover, it holds impressive rankings in the InCites Journal Citation Reports: in 2021, it was ranked 6th out of 101 in the field of Optics, 15th out of 161 in Applied Physics, and 12th out of 69 in Condensed Matter Physics. The journal uses the ISSN numbers 1863-8880 for print and 1863-8899 for online publications.
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