Improved Phase Diversity Wavefront Sensing with a Deep Learning-Driven Hybrid Optimization Approach
PhotonicsPhase diversity wavefront sensing (PDWS) is a model-based wavefront estimation technique that avoids additional optical components, making it suitable for resource-constrained environments.
Yangchen Wang, Ming Wen, Hongcai Ma
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Open-Loop Wavefront Reconstruction with Pyramidal Sensors Using Convolutional Neural Networks
MathematicsNeural networks have significantly advanced adaptive optics systems for telescopes in recent years. Future adaptive optics systems, especially for extremely large telescopes, are expected to predominantly employ pyramid wavefront sensors, which offer ...
Saúl Pérez-Fernández +7 more
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Wavefront Sensing and Control with the Many Headed Hydra Modal Basis [PDF]
arXiv, 2022 The future of space and ground based telescopes is intimately tied to technology and algorithm development surrounding wavefront sensing and control. Only with cutting edge developments and unusual ideas will we be able to build diffraction limited observatories on the ground that contend with earth-atmosphere, as well as space-based observatories that
arxiv
Wavefront sensing based on the inverted Hartmann sensor [PDF]
arXiv, 2019 The classic Hartmann test consists of an array of holes to reconstruct the wavefront from the local deviation of each focal spot, and Shack-Hartmann sensor improved that with an array of microlenses. This array of microlenses imposes practical limitations when the wavefront is not into of visible wavelengths, e.g., the fabrication of these. Instead, we
arxiv
Demonstration of a photonic lantern focal-plane wavefront sensor: measurement of atmospheric wavefront error modes and low wind effect in the non-linear regime [PDF]
arXiv, 2023 Here we present a laboratory analysis of the use of a 19-core photonic lantern (PL) in combination with neural network (NN) algorithms as an efficient focal plane wavefront sensor (FP-WFS) for adaptive optics (AO), measuring wavefront errors such as low wind effect (LWE), Zernike modes and Kolmogorov phase maps.
arxiv
Single-Shot Wavefront Sensing in Focal Plane Imaging Using Transformer Networks
OpticsWavefront sensing is an essential technique in optical imaging, adaptive optics, and atmospheric turbulence correction. Traditional wavefront reconstruction methods, including the Gerchberg–Saxton (GS) algorithm and phase diversity (PD) techniques, are ...
Hangning Kou +6 more
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Using Spherical-Harmonics Expansions for Optics Surface Reconstruction from Gradients
Sensors, 2017 In this paper, we propose a new algorithm to reconstruct optics surfaces (aka wavefronts) from gradients, defined on a circular domain, by means of the Spherical Harmonics.
Juan Manuel Solano-Altamirano +4 more
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Deep learning bimodal frequency-selective digital holography by using a confocal dual-beam setup
New Journal of PhysicsThis paper proposes a bimodal digital holography technique based on deep learning, marking the first application of neural networks in frequency-selective holographic reconstruction.
Wanbin Zhang +6 more
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Performance analysis of Fourier and Vector Matrix Multiply methods for phase reconstruction from slope measurements [PDF]
XXXIV Symposium of OSI, ICOP, 2009, 2009 The accuracy of wavefront reconstruction from discrete slope measurements depends on the sampling geometry, coherence length of the incoming wavefronts, wavefront sensor specifications and the accuracy of the reconstruction algorithm. Monte Carlo simulations were performed and a comparison of Fourier and Vector Matrix Multiply reconstruction methods ...
arxiv
Deep DIH: Single-Shot Digital In-Line Holography Reconstruction by Deep Learning
IEEE Access, 2020 Digital in-line holography (DIH) is broadly used to reconstruct 3D shapes of microscopic objects from their 2D holograms. One of the technical challenges in the reconstruction stage is eliminating the twin image originating from the phase-conjugate ...
Huayu Li +4 more
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