How convolutional-neural-network detects optical vortex scattering fields

Hu, Junbao; Guo, Zefeng; Fu, Yang; Gan, Jiaan; Chen, Pengfei; Chen, Guangyong; Min, Changjun; Yuan, Xiaocong; Feng, Fu

doi:10.1016/j.optlaseng.2022.107246

Cited by 8 publications

(2 citation statements)

References 30 publications

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“…Recently, convolutional-neural-networks have been used to recognize scattered scalar vortex beams using statistical invariance and scatterer features, which allowed to identify topological charge values from captured speckle patterns. 16 In this work, we demonstrate a novel technique to assess spatial structure over the transverse section of vector vortex beams (VVBs) of equal but opposite topological charges, using texture statistical measures derived from an a-GLCM. To demonstrate our classification approach we used the set of Laguerre-Gauss vector beams (LGVBs), but this also applies to vector Bessel beams.…”

Section: Introductionmentioning

confidence: 99%

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Texture classification of complex vector vortex beams

Chicangana-Cifuentes,

Rodríguez-Fajardo,

Rosales-Guzmán

et al. 2023

Opt. Eng.

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Section: Introductionmentioning

confidence: 99%

“…Nonetheless, this is a qualitative technique that does not provide information about the mode purity. Recently, convolutional-neural-networks have been used to recognize scattered scalar vortex beams using statistical invariance and scatterer features, which allowed to identify topological charge values from captured speckle patterns 16 …”

Section: Introductionmentioning

confidence: 99%

Texture classification of complex vector vortex beams

Chicangana-Cifuentes,

Rodríguez-Fajardo,

Rosales-Guzmán

et al. 2023

Opt. Eng.

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Learning‐Based Vectorial Reconstruction of Orthogonal Polarization Components in a Structured Vector Optical Field Passing Through Scattering Media

Chen,

Shen,

et al. 2024

Advanced Physics Research

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Optical imaging through scattering media has become important due to its fundamental physics interest and various applications. The reconstruction of a structured optical field with various states of polarization passing through a scattering medium with a speckle pattern behind the scattering medium remains challenging since existing restoring techniques only reconstruct the speckle in a single‐polarization state (scalar optical field). This work proposes a novel approach to simultaneously restore the initial orthogonally polarized components from a speckle pattern behind a scattering medium. The neural network Polarization‐DenseUnet (P‐DenseUnet) based on the vector transfer matrix is constructed to restore the two orthogonally linear (or circular) polarization components of a structured vector optical field from a speckle pattern behind the scattering medium. The generalization and effectiveness of this proposed method are tested for high fidelity with different phase distributions such as vortex, digits, and Fashion‐mnist.

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Roadmap on computational methods in optical imaging and holography [invited]

Rosen,

Alford,

Allan

et al. 2024

Appl. Phys. B

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Computational methods have been established as cornerstones in optical imaging and holography in recent years. Every year, the dependence of optical imaging and holography on computational methods is increasing significantly to the extent that optical methods and components are being completely and efficiently replaced with computational methods at low cost. This roadmap reviews the current scenario in four major areas namely incoherent digital holography, quantitative phase imaging, imaging through scattering layers, and super-resolution imaging. In addition to registering the perspectives of the modern-day architects of the above research areas, the roadmap also reports some of the latest studies on the topic. Computational codes and pseudocodes are presented for computational methods in a plug-and-play fashion for readers to not only read and understand but also practice the latest algorithms with their data. We believe that this roadmap will be a valuable tool for analyzing the current trends in computational methods to predict and prepare the future of computational methods in optical imaging and holography.

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How convolutional-neural-network detects optical vortex scattering fields

Cited by 8 publications

References 30 publications

Texture classification of complex vector vortex beams

Texture classification of complex vector vortex beams

Learning‐Based Vectorial Reconstruction of Orthogonal Polarization Components in a Structured Vector Optical Field Passing Through Scattering Media

Roadmap on computational methods in optical imaging and holography [invited]

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