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.
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.