A real-time wavefront sensing method for arbitrary targets is proposed,which provides an effective way for diversified wavefront sensing application scenarios.By using a distorted grating,the positive and negative def...A real-time wavefront sensing method for arbitrary targets is proposed,which provides an effective way for diversified wavefront sensing application scenarios.By using a distorted grating,the positive and negative defocus images are simultaneously acquired on a single detector.A fine feature,which is independent of the target itself but corresponding to the wavefront aberration,is defined.A lightweight and efficient network combined with an attention mechanism[AM-EffNet]is proposed to establish an accurate mapping between the features and the incident wavefronts.Comparison results show that the proposed method has superior performance compared to other methods and can achieve high-accuracy wavefront sensing in varied target scenes only by using the point target dataset to train the network well.展开更多
A cross-scale composite wavefront measurement method based on deep learning is proposed to address local large gradient wavefront distortions from aero-optical effects.Since dynamic range and spatial resolution are us...A cross-scale composite wavefront measurement method based on deep learning is proposed to address local large gradient wavefront distortions from aero-optical effects.Since dynamic range and spatial resolution are usually a trade-off for most wavefront sensors,we propose a hybrid Shack-Hartmann-digital holographic wavefront sensing mechanism that includes a Shack-Hartmann wavefront sensor(SHWFS)and off-axis digital holography(OADH).Using the hybrid wavefront sensing mechanism and the data processing method,the reconstructed wavefront of SHWFS and the wrapped phase of OADH are obtained separately.A multi-input efficient network cal ed the multi-system wavefront measurement-net(MSWM-Net)with an attention mechanism is introduced to map the reconstructed wavefront of SHWFS and the wrapped phase of the OADH to the precise wavefront.Numerical simulations and comparisons with the deep learning phase unwrapping(DLPU)-model-based phase unwrapping method and classical phase unwrapping technique demonstrate that this method resolves the chal enge of mismatched data scales across the two measurement systems,enabling rapid and high-precision wavefront sensing.展开更多
基金supported by the National Natural Science Foundation of China(No.62105336)Sichuan Science and Technology Program(No.2022JDRC0095)。
文摘A real-time wavefront sensing method for arbitrary targets is proposed,which provides an effective way for diversified wavefront sensing application scenarios.By using a distorted grating,the positive and negative defocus images are simultaneously acquired on a single detector.A fine feature,which is independent of the target itself but corresponding to the wavefront aberration,is defined.A lightweight and efficient network combined with an attention mechanism[AM-EffNet]is proposed to establish an accurate mapping between the features and the incident wavefronts.Comparison results show that the proposed method has superior performance compared to other methods and can achieve high-accuracy wavefront sensing in varied target scenes only by using the point target dataset to train the network well.
基金supported by the National Natural Science Foundation of China(No.62305343)the Fund of the National Key Laboratory of Adaptive Optics(No.FNLAO-24-MS-S07)。
文摘A cross-scale composite wavefront measurement method based on deep learning is proposed to address local large gradient wavefront distortions from aero-optical effects.Since dynamic range and spatial resolution are usually a trade-off for most wavefront sensors,we propose a hybrid Shack-Hartmann-digital holographic wavefront sensing mechanism that includes a Shack-Hartmann wavefront sensor(SHWFS)and off-axis digital holography(OADH).Using the hybrid wavefront sensing mechanism and the data processing method,the reconstructed wavefront of SHWFS and the wrapped phase of OADH are obtained separately.A multi-input efficient network cal ed the multi-system wavefront measurement-net(MSWM-Net)with an attention mechanism is introduced to map the reconstructed wavefront of SHWFS and the wrapped phase of the OADH to the precise wavefront.Numerical simulations and comparisons with the deep learning phase unwrapping(DLPU)-model-based phase unwrapping method and classical phase unwrapping technique demonstrate that this method resolves the chal enge of mismatched data scales across the two measurement systems,enabling rapid and high-precision wavefront sensing.
