Enhanced Neural Architecture for Real-Time Deep Learning Wavefront Sensing
To achieve real-time deep learning wavefront sensing (DLWFS) of dynamic random wavefront distortions induced by atmospheric turbulence, this study proposes an enhanced wavefront sensing neural network (WFSNet) based on convolutional neural networks (CNN). We introduce a novel multi-objective neural...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/480 |
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| author | Jianyi Li Qingfeng Liu Liying Tan Jing Ma Nanxing Chen |
| author_facet | Jianyi Li Qingfeng Liu Liying Tan Jing Ma Nanxing Chen |
| author_sort | Jianyi Li |
| collection | DOAJ |
| description | To achieve real-time deep learning wavefront sensing (DLWFS) of dynamic random wavefront distortions induced by atmospheric turbulence, this study proposes an enhanced wavefront sensing neural network (WFSNet) based on convolutional neural networks (CNN). We introduce a novel multi-objective neural architecture search (MNAS) method designed to attain Pareto optimality in terms of error and floating-point operations (FLOPs) for the WFSNet. Utilizing EfficientNet-B0 prototypes, we propose a WFSNet with enhanced neural architecture which significantly reduces computational costs by 80% while improving wavefront sensing accuracy by 22%. Indoor experiments substantiate this effectiveness. This study offers a novel approach to real-time DLWFS and proposes a potential solution for high-speed, cost-effective wavefront sensing in the adaptive optical systems of satellite-to-ground laser communication (SGLC) terminals. |
| format | Article |
| id | doaj-art-edb8f4d1214b40389f0b5bab4d104ef6 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-edb8f4d1214b40389f0b5bab4d104ef62025-01-24T13:49:05ZengMDPI AGSensors1424-82202025-01-0125248010.3390/s25020480Enhanced Neural Architecture for Real-Time Deep Learning Wavefront SensingJianyi Li0Qingfeng Liu1Liying Tan2Jing Ma3Nanxing Chen4Free-Space Optical Communication Technology Research Center, Harbin Institute of Technology, Harbin 150001, ChinaFree-Space Optical Communication Technology Research Center, Harbin Institute of Technology, Harbin 150001, ChinaFree-Space Optical Communication Technology Research Center, Harbin Institute of Technology, Harbin 150001, ChinaFree-Space Optical Communication Technology Research Center, Harbin Institute of Technology, Harbin 150001, ChinaFree-Space Optical Communication Technology Research Center, Harbin Institute of Technology, Harbin 150001, ChinaTo achieve real-time deep learning wavefront sensing (DLWFS) of dynamic random wavefront distortions induced by atmospheric turbulence, this study proposes an enhanced wavefront sensing neural network (WFSNet) based on convolutional neural networks (CNN). We introduce a novel multi-objective neural architecture search (MNAS) method designed to attain Pareto optimality in terms of error and floating-point operations (FLOPs) for the WFSNet. Utilizing EfficientNet-B0 prototypes, we propose a WFSNet with enhanced neural architecture which significantly reduces computational costs by 80% while improving wavefront sensing accuracy by 22%. Indoor experiments substantiate this effectiveness. This study offers a novel approach to real-time DLWFS and proposes a potential solution for high-speed, cost-effective wavefront sensing in the adaptive optical systems of satellite-to-ground laser communication (SGLC) terminals.https://www.mdpi.com/1424-8220/25/2/480real-time wavefront sensingdeep learningCNNmulti-objective neural architecture searchatmospheric turbulence |
| spellingShingle | Jianyi Li Qingfeng Liu Liying Tan Jing Ma Nanxing Chen Enhanced Neural Architecture for Real-Time Deep Learning Wavefront Sensing Sensors real-time wavefront sensing deep learning CNN multi-objective neural architecture search atmospheric turbulence |
| title | Enhanced Neural Architecture for Real-Time Deep Learning Wavefront Sensing |
| title_full | Enhanced Neural Architecture for Real-Time Deep Learning Wavefront Sensing |
| title_fullStr | Enhanced Neural Architecture for Real-Time Deep Learning Wavefront Sensing |
| title_full_unstemmed | Enhanced Neural Architecture for Real-Time Deep Learning Wavefront Sensing |
| title_short | Enhanced Neural Architecture for Real-Time Deep Learning Wavefront Sensing |
| title_sort | enhanced neural architecture for real time deep learning wavefront sensing |
| topic | real-time wavefront sensing deep learning CNN multi-objective neural architecture search atmospheric turbulence |
| url | https://www.mdpi.com/1424-8220/25/2/480 |
| work_keys_str_mv | AT jianyili enhancedneuralarchitectureforrealtimedeeplearningwavefrontsensing AT qingfengliu enhancedneuralarchitectureforrealtimedeeplearningwavefrontsensing AT liyingtan enhancedneuralarchitectureforrealtimedeeplearningwavefrontsensing AT jingma enhancedneuralarchitectureforrealtimedeeplearningwavefrontsensing AT nanxingchen enhancedneuralarchitectureforrealtimedeeplearningwavefrontsensing |