We explore an end-to-end wavefront sensing approach based on deep learning,which aims to deal with the high-order turbulence and the discontinuous aberration caused by optical system obstructions commonly encountered ...We explore an end-to-end wavefront sensing approach based on deep learning,which aims to deal with the high-order turbulence and the discontinuous aberration caused by optical system obstructions commonly encountered in real-world ground-based telescope observations.We have considered factors such as the entrance pupil wavefront containing high-order turbulence and discontinuous aberrations due to obstruction by the secondary mirror and spider,realistically simulating the observation conditions of ground-based telescopes.By comparing with the Marechal criterion(0.075λ),we validate the effectiveness of the proposed approach.Experimental results show that the deep learning wavefront sensing approach can correct the distorted wavefront affect by high-order turbulence to close to the diffraction limit.We also analyze the limitations of this approach,using the direct zonal phase output method,where the residual wavefront stems from the fitting error.Furthermore,we have explored the wavefront reconstruction accuracy of different noise intensities and the central obstruction ratios.Within a noise intensity range of 1%–1.9%,the root mean square error(RMSE)of the residual wavefront is less than Marechal criterion.In the range of central obstruction ratios from 0.0 to 0.3 commonly used in ground-based telescopes,the RMSE of the residual wavefront is greater than 0.039λand less than 0.041λ.This research provides an efficient and valid wavefront sensing approach for high-resolution observation with ground-based telescopes.展开更多
In laser systems requiring a flat-top distribution of beam intensity,beam smoothing is a critical technology for enhancing laser energy deposition onto the focal spot.The continuous phase modulator(CPM)is a key compon...In laser systems requiring a flat-top distribution of beam intensity,beam smoothing is a critical technology for enhancing laser energy deposition onto the focal spot.The continuous phase modulator(CPM)is a key component in beam smoothing,as it introduces high-frequency continuous phase modulation across the laser beam profile.However,the presence of the CPM makes it challenging to measure and correct the wavefront aberration of the input laser beam effectively,leading to unwanted beam intensity distribution and bringing difficulty to the design of the CPM.To address this issue,we propose a deep learning enabled robust wavefront sensing(DLWS)method to achieve effective wavefront measurement and active aberration correction,thereby facilitating active beam smoothing using the CPM.The experimental results show that the average wavefront reconstruction error of the DLWS method is 0.04μm in the root mean square,while the Shack–Hartmann wavefront sensor reconstruction error is 0.17μm.展开更多
Virtual Shack-Hartmann wavefront sensing(vSHWS)has some significant advantages and is promising for aberration measurement in the field of biomedical optical imaging.The illumination sources used in vSHWS are almost b...Virtual Shack-Hartmann wavefront sensing(vSHWS)has some significant advantages and is promising for aberration measurement in the field of biomedical optical imaging.The illumination sources used in vSHWS are almost broadband,but are treated as monochromatic sources(only using center wavelength)in current data processing,which may cause errors.This work proposed a data processing method to take into account the multiple wavelengths of the broadband spectrum,named multiple-wavelength centroid-weighting method.Its feasibility was demonstrated through a series of simulations.A wavefront generated with a set of statistical human ocular aberrations was used as the target wavefront to evaluate the performance of the proposed and current methods.The results showed that their performance was very close when used for the symmetrical,but the wavefront error of the proposed method was much smaller than that of the current method when used for the asymmetrical spectrum,especially for the broader spectrum.These results were also validated by using 20 sets of clinical human ocular aberrations including normal and diseased eyes.The proposed method and the obtained conclusions have important implications for the application of vSHWS.展开更多
Deep learning neural networks are used for wavefront sensing and aberration correction in atmospheric turbulence without any wavefront sensor(i.e.reconstruction of the wavefront aberration phase from the distorted ima...Deep learning neural networks are used for wavefront sensing and aberration correction in atmospheric turbulence without any wavefront sensor(i.e.reconstruction of the wavefront aberration phase from the distorted image of the object).We compared and found the characteristics of the direct and indirect reconstruction ways:(i)directly reconstructing the aberration phase;(ii)reconstructing the Zernike coefficients and then calculating the aberration phase.We verified the generalization ability and performance of the network for a single object and multiple objects.What’s more,we verified the correction effect for a turbulence pool and the feasibility for a real atmospheric turbulence environment.展开更多
Baseline algorithm, as a tool in wavefront sensing （WFS）, incorporates the phase-diverse phase retrieval （PDPR） method with hybrid-unwrapping approach to ensure a unique pupil phase estimate with high WFS accuracy...Baseline algorithm, as a tool in wavefront sensing （WFS）, incorporates the phase-diverse phase retrieval （PDPR） method with hybrid-unwrapping approach to ensure a unique pupil phase estimate with high WFS accuracy even in the case of high dynamic range aberration, as long as the pupil shape is of a convex set. However, for a complicated pupil, such as that in obstructed pupil optics, the said unwrapping approach would fail owing to the fake values at points located in obstructed areas of the pupil. Thus a modified unwrapping approach that can minimize the negative effects of the obstructed areas is proposed. Simulations have shown the validity of this unwrapping approach when it is embedded in Baseline algorithm.展开更多
The Shack–Hartmann wavefront sensor(SHWFS)is commonly used for its high speed and precision in adaptive optics.However,its performance is limited in low light conditions,particularly when observing faint objects in a...The Shack–Hartmann wavefront sensor(SHWFS)is commonly used for its high speed and precision in adaptive optics.However,its performance is limited in low light conditions,particularly when observing faint objects in astronomical applications.Instead of a pixelated detector,we present a new approach for wavefront sensing using a single-pixel detector,which is able to code the spatial position of a light spot array into the polarization dimension and decode the polarization state in the polar coordinate.We propose validation experiments with simple and complex wavefront distortions to demonstrate our approach as a promising alternative to traditional SHWFS systems,with potential applications in a wide range of fields.展开更多
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
The Shack-Hartmann wavefront sensor(SHWS)is widely used for high-speed,precise,and stable wavefront measurements.However,conventional SHWSs encounter a limitation in that the focused spot from each microlens is restri...The Shack-Hartmann wavefront sensor(SHWS)is widely used for high-speed,precise,and stable wavefront measurements.However,conventional SHWSs encounter a limitation in that the focused spot from each microlens is restricted to a single microlens,leading to a limited dynamic range.Herein,we propose an adaptive spot matching(ASM)-based SHWS to extend the dynamic range.This approach involves seeking an incident wavefront that best matches the detected spot distribution by employing a Hausdorff-distance-based nearest-distance matching strategy.The ASM-SHWS enables comprehensive spot matching across the entire imaging plane without requiring initial spot correspondences.Furthermore,due to its global matching capability,ASM-SHWS can maintain its capacity even if a portion of the spots are missing.Experiments showed that the ASM-SHWS could measure a high-curvature spherical wavefront with a local slope of 204.97 mrad,despite a 12.5%absence of spots.This value exceeds that of the conventional SHWS by a factor of 14.81.展开更多
基金supported by the National Natural Science Foundation of China(NSFC)(U2031140).
文摘We explore an end-to-end wavefront sensing approach based on deep learning,which aims to deal with the high-order turbulence and the discontinuous aberration caused by optical system obstructions commonly encountered in real-world ground-based telescope observations.We have considered factors such as the entrance pupil wavefront containing high-order turbulence and discontinuous aberrations due to obstruction by the secondary mirror and spider,realistically simulating the observation conditions of ground-based telescopes.By comparing with the Marechal criterion(0.075λ),we validate the effectiveness of the proposed approach.Experimental results show that the deep learning wavefront sensing approach can correct the distorted wavefront affect by high-order turbulence to close to the diffraction limit.We also analyze the limitations of this approach,using the direct zonal phase output method,where the residual wavefront stems from the fitting error.Furthermore,we have explored the wavefront reconstruction accuracy of different noise intensities and the central obstruction ratios.Within a noise intensity range of 1%–1.9%,the root mean square error(RMSE)of the residual wavefront is less than Marechal criterion.In the range of central obstruction ratios from 0.0 to 0.3 commonly used in ground-based telescopes,the RMSE of the residual wavefront is greater than 0.039λand less than 0.041λ.This research provides an efficient and valid wavefront sensing approach for high-resolution observation with ground-based telescopes.
基金supported by the National Natural Science Foundation of China(Grant No.61775112).
文摘In laser systems requiring a flat-top distribution of beam intensity,beam smoothing is a critical technology for enhancing laser energy deposition onto the focal spot.The continuous phase modulator(CPM)is a key component in beam smoothing,as it introduces high-frequency continuous phase modulation across the laser beam profile.However,the presence of the CPM makes it challenging to measure and correct the wavefront aberration of the input laser beam effectively,leading to unwanted beam intensity distribution and bringing difficulty to the design of the CPM.To address this issue,we propose a deep learning enabled robust wavefront sensing(DLWS)method to achieve effective wavefront measurement and active aberration correction,thereby facilitating active beam smoothing using the CPM.The experimental results show that the average wavefront reconstruction error of the DLWS method is 0.04μm in the root mean square,while the Shack–Hartmann wavefront sensor reconstruction error is 0.17μm.
基金This work is supported by the National Natural Science Foundation of China(Grant No.61575205).The authors would like to thank the team of Professor Fan Lüat the Eye Hospital of Wenzhou Medical University for providing clinical human ocular aberrations.
文摘Virtual Shack-Hartmann wavefront sensing(vSHWS)has some significant advantages and is promising for aberration measurement in the field of biomedical optical imaging.The illumination sources used in vSHWS are almost broadband,but are treated as monochromatic sources(only using center wavelength)in current data processing,which may cause errors.This work proposed a data processing method to take into account the multiple wavelengths of the broadband spectrum,named multiple-wavelength centroid-weighting method.Its feasibility was demonstrated through a series of simulations.A wavefront generated with a set of statistical human ocular aberrations was used as the target wavefront to evaluate the performance of the proposed and current methods.The results showed that their performance was very close when used for the symmetrical,but the wavefront error of the proposed method was much smaller than that of the current method when used for the asymmetrical spectrum,especially for the broader spectrum.These results were also validated by using 20 sets of clinical human ocular aberrations including normal and diseased eyes.The proposed method and the obtained conclusions have important implications for the application of vSHWS.
基金National Natural Science Foundation of China(61927810,62075183).
文摘Deep learning neural networks are used for wavefront sensing and aberration correction in atmospheric turbulence without any wavefront sensor(i.e.reconstruction of the wavefront aberration phase from the distorted image of the object).We compared and found the characteristics of the direct and indirect reconstruction ways:(i)directly reconstructing the aberration phase;(ii)reconstructing the Zernike coefficients and then calculating the aberration phase.We verified the generalization ability and performance of the network for a single object and multiple objects.What’s more,we verified the correction effect for a turbulence pool and the feasibility for a real atmospheric turbulence environment.
文摘Baseline algorithm, as a tool in wavefront sensing （WFS）, incorporates the phase-diverse phase retrieval （PDPR） method with hybrid-unwrapping approach to ensure a unique pupil phase estimate with high WFS accuracy even in the case of high dynamic range aberration, as long as the pupil shape is of a convex set. However, for a complicated pupil, such as that in obstructed pupil optics, the said unwrapping approach would fail owing to the fake values at points located in obstructed areas of the pupil. Thus a modified unwrapping approach that can minimize the negative effects of the obstructed areas is proposed. Simulations have shown the validity of this unwrapping approach when it is embedded in Baseline algorithm.
基金supported by the Natural Science Foundation of Shandong Province(No.ZR201911090294)。
文摘The Shack–Hartmann wavefront sensor(SHWFS)is commonly used for its high speed and precision in adaptive optics.However,its performance is limited in low light conditions,particularly when observing faint objects in astronomical applications.Instead of a pixelated detector,we present a new approach for wavefront sensing using a single-pixel detector,which is able to code the spatial position of a light spot array into the polarization dimension and decode the polarization state in the polar coordinate.We propose validation experiments with simple and complex wavefront distortions to demonstrate our approach as a promising alternative to traditional SHWFS systems,with potential applications in a wide range of fields.
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 Fundamental Research Funds for the Central Universities of Shanghai Jiao Tong University and the Shanghai Jiao Tong University 2030 Initiative(No.WH510363001-10)the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University(No.SL2022ZD205)+1 种基金the Science Foundation of the Donghai Laboratory(No.DH-2022KF01001)National Natural Science Foundation of China(No.62205189).
文摘The Shack-Hartmann wavefront sensor(SHWS)is widely used for high-speed,precise,and stable wavefront measurements.However,conventional SHWSs encounter a limitation in that the focused spot from each microlens is restricted to a single microlens,leading to a limited dynamic range.Herein,we propose an adaptive spot matching(ASM)-based SHWS to extend the dynamic range.This approach involves seeking an incident wavefront that best matches the detected spot distribution by employing a Hausdorff-distance-based nearest-distance matching strategy.The ASM-SHWS enables comprehensive spot matching across the entire imaging plane without requiring initial spot correspondences.Furthermore,due to its global matching capability,ASM-SHWS can maintain its capacity even if a portion of the spots are missing.Experiments showed that the ASM-SHWS could measure a high-curvature spherical wavefront with a local slope of 204.97 mrad,despite a 12.5%absence of spots.This value exceeds that of the conventional SHWS by a factor of 14.81.
