Image-to-image translation for wavefront and PSF estimation

Smith, Jeffrey C.; Cranney, Jesse; Gretton, Charles; Gratadour, Damien

doi:10.1117/12.2629638

Cited by 3 publications

(7 citation statements)

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“…We are also examining applications of our phase reconstruction in real-time control with impressive early results, 20 , 29 and are encouraged about its applicability given we are measuring an inference time of 0.34 ms on retail desktop GPUs. With optimization and dedicated hardware 30 , 31 and the COSMIC framework platform, 32 wavefront inference from pre-trained networks has potential for hard real-time AO loop control, and not just in postprocessing workflows via PSF reconstruction.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…This research was undertaken with the assistance of resources from the National Computational Infrastructure (NCI Australia), an NCRIS-enabled capability supported by the Australian Government. This paper was originally published in Proceedings – Adaptive Optics Systems VIII, SPIE Astronomical Telescopes and Instrumentation 2022 34 …”

Section: Acknowledgmentsmentioning

confidence: 99%

“…This paper was originally published in Proceedings -Adaptive Optics Systems VIII, SPIE Astronomical Telescopes and Instrumentation 2022. 34…”

Section: Acknowledgmentsmentioning

confidence: 99%

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Image-to-image translation for wavefront and point spread function estimation

Smith

Cranney

Gretton

et al. 2023

J. Astron. Telesc. Instrum. Syst.

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We develop and evaluate a new approach to phase estimation for observational astronomy that can be used for accurate point spread function reconstruction. Phase estimation is required where a terrestrial observatory uses an adaptive optics (AO) system to assist astronomers in acquiring sharp, high-contrast images of faint and distant objects. Our approach is to train a conditional adversarial artificial neural network architecture to predict phase using the wavefront sensor data from a closed-loop AO system. We present a detailed simulation study under different turbulent conditions, using the retrieved residual phase to obtain the point spread function of the simulated instrument. Compared to the state-of-the-art model-based approach in astronomy, our approach is not explicitly limited by modeling assumptions, e.g., independence between terms, such as bandwidth and anisoplanatism-and is conceptually simple and flexible.We use the open-source COMPASS tool for end-to-end simulations. On key quality metrics, specifically the Strehl ratio and Halo distribution in our application domain, our approach achieves results better than the model-based baseline.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Acknowledgmentsmentioning

confidence: 99%

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Image-to-image translation for wavefront and point spread function estimation

Smith

Cranney

Gretton

et al. 2023

J. Astron. Telesc. Instrum. Syst.

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“…the MARL approach). For problem 4, we leverage a non-linear reconstruction (similar to Landman et al, 2 and Smith et al 4 ) to mitigate the non-monotonicity.…”

Section: Figures C) and D)mentioning

confidence: 99%

“…On the one hand, there are methods built around supervised learning (SL). Among these methods, we can differentiate between non-linear reconstructors, [1][2][3][4] predictive models 5,6 or a combination of both. 7,8 In the case of predictive models, while their predictions are usually correct in open-loop, their performance dramatically decreases in closed-loop because there is a distribution mismatch between the training set and operational data.…”

Section: Introductionmentioning

confidence: 99%

Model-free reinforcement learning with a non-linear reconstructor for closed-loop adaptive optics control with a pyramid wavefront sensor

Mulet¹,

Smith

Quiñones

et al. 2022

Adaptive Optics Systems VIII

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We present a model-free reinforcement learning (RL) predictive model with a supervised learning non-linear reconstructor for adaptive optics (AO) control with a pyramid wavefront sensor (P-WFS). First, we analyse the additional problems of training an RL control method with a P-WFS compared to the Shack-Hartmann WFS. From those observations, we propose our solution: a combination of model-free RL for prediction with a non-linear reconstructor based on neural networks with a U-net architecture. We test the proposed method in simulation of closed-loop AO for an 8m telescope equipped with a 32x32 P-WFS and observe that both the predictive and non-linear reconstruction add additional benefits over an optimised integrator.

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Physics-constrained deep-inverse point spread function model: toward non-line-of-sight imaging reconstruction

Wu,

Huang,

Lin

et al. 2024

Adv. Photon. Nexus

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Non-line-of-sight (NLOS) imaging has emerged as a prominent technique for reconstructing obscured objects from images that undergo multiple diffuse reflections. This imaging method has garnered significant attention in diverse domains, including remote sensing, rescue operations, and intelligent driving, due to its wide-ranging potential applications. Nevertheless, accurately modeling the incident light direction, which carries energy and is captured by the detector amidst random diffuse reflection directions, poses a considerable challenge. This challenge hinders the acquisition of precise forward and inverse physical models for NLOS imaging, which are crucial for achieving high-quality reconstructions. In this study, we propose a point spread function (PSF) model for the NLOS imaging system utilizing ray tracing with random angles. Furthermore, we introduce a reconstruction method, termed the physics-constrained inverse network (PCIN), which establishes an accurate PSF model and inverse physical model by leveraging the interplay between PSF constraints and the optimization of a convolutional neural network. The PCIN approach initializes the parameters randomly, guided by the constraints of the forward PSF model, thereby obviating the need for extensive training data sets, as required by traditional deep-learning methods. Through alternating iteration and gradient descent algorithms, we iteratively optimize the diffuse reflection angles in the PSF model and the neural network parameters. The results demonstrate that PCIN achieves efficient data utilization by not necessitating a large number of actual ground data groups. Moreover, the experimental findings confirm that the proposed method effectively restores the hidden object features with high accuracy.

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Image-to-image translation for wavefront and PSF estimation

Cited by 3 publications

References 0 publications

Image-to-image translation for wavefront and point spread function estimation

Image-to-image translation for wavefront and point spread function estimation

Model-free reinforcement learning with a non-linear reconstructor for closed-loop adaptive optics control with a pyramid wavefront sensor

Physics-constrained deep-inverse point spread function model: toward non-line-of-sight imaging reconstruction

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