Paper Citation Record · LEDGER

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging

As of 13 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2412.06806.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.06806 v2

Coverage vector

measured 43 of 43 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

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External citation measurements

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Outbound references

Observation a5072c05-99c0-41a6-bca8-3cfc4e7e3816 · outbound

This paper cites Dynamische Theorie der Kristallstrukturanal- yse durch Elektronenbeugung im inhomogenen Prim ¨arstrahlwellenfeld,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Dynamische Theorie der Kristallstrukturanal- yse durch Elektronenbeugung im inhomogenen Prim ¨arstrahlwellenfeld,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.312284Z

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Observation 5ef81465-af77-4192-a66d-2016248c17ec · outbound

This paper cites Electron ptychography of 2D materials to deep sub-˚angstr¨om resolution,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Electron ptychography of 2D materials to deep sub-˚angstr¨om resolution,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.299748Z

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Observation e5abde7c-8608-46a9-add9-529ea43f0155 · outbound

This paper cites Local-orbital ptychography for ultrahigh-resolution imaging,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Local-orbital ptychography for ultrahigh-resolution imaging,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.287942Z

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Observation 751baf0c-1c4f-4713-8d07-258c65294185 · outbound

This paper cites Improving organic tandem solar cells based on water-processed nanoparticles by quantitative 3D nanoimaging,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Improving organic tandem solar cells based on water-processed nanoparticles by quantitative 3D nanoimaging,

Reference 4

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Observation ce80b755-29d1-47ad-bda6-f0e139569b6d · outbound

This paper cites Characterising live cell behaviour: Traditional label-free and quantitative phase imaging ap- proaches,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Characterising live cell behaviour: Traditional label-free and quantitative phase imaging ap- proaches,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.263094Z

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Observation 41aeb3ad-0686-455d-b076-e58258331b8e · outbound

This paper cites High-resolution non-destructive three-dimensional imaging of integrated circuits,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging High-resolution non-destructive three-dimensional imaging of integrated circuits,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.250151Z

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Observation d920e0a4-bbb0-4887-bbf4-b934d9062110 · outbound

This paper cites Deep learning at the edge enables real-time streaming ptychographic imaging,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Deep learning at the edge enables real-time streaming ptychographic imaging,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.237985Z

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Observation 9f8d08c1-5ea5-41bf-a724-43955b4236e3 · outbound

This paper cites Sampling in x-ray ptychography,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Sampling in x-ray ptychography,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.222470Z

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Observation 742a5f17-2a68-48b8-8f1b-39a32a520d76 · outbound

This paper cites AI-enabled high-resolution scanning coherent diffraction imaging,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging AI-enabled high-resolution scanning coherent diffraction imaging,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.210475Z

Source-reported events for the cited work

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Observation c5e308d4-8e76-4aa4-a20d-465ecc9fdd91 · outbound

This paper cites Deep-Learning Electron Diffractive Imaging,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Deep-Learning Electron Diffractive Imaging,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.197265Z

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Observation a69bf0a7-47ab-4ad5-ba7f-74ffc5df423f · outbound

This paper cites An efficient ptychography reconstruction strategy through fine-tuning of large pre-trained deep learning model,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging An efficient ptychography reconstruction strategy through fine-tuning of large pre-trained deep learning model,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.184421Z

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Observation de19f29c-8e3c-4cd1-b45c-a075bea51373 · outbound

This paper cites Learning to synthesize: Robust phase retrieval at low photon counts,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Learning to synthesize: Robust phase retrieval at low photon counts,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.167201Z

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Observation 774f2839-edee-4353-b283-897842ad24da · outbound

This paper cites Phase retrieval based on deep learning with bandpass filtering in holographic data storage,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Phase retrieval based on deep learning with bandpass filtering in holographic data storage,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.153370Z

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Observation e2a6f422-e56b-481a-a43b-29116437bed6 · outbound

This paper cites On-the-fly scans for X-ray ptychography,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging On-the-fly scans for X-ray ptychography,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.141330Z

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Observation 41b41048-4420-4f6b-b02a-1f85eba25689 · outbound

This paper cites Noise-robust latent vector re- construction in ptychography using deep generative models,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Noise-robust latent vector re- construction in ptychography using deep generative models,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.129399Z

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Observation c8833fe0-ba1c-485e-936b-6d8efa711e42 · outbound

This paper cites Using a modified double deep image prior for crosstalk mitigation in multislice ptychography,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Using a modified double deep image prior for crosstalk mitigation in multislice ptychography,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.116482Z

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Observation f7fcc8ce-0d59-4c43-ae6a-672bf407330f · outbound

This paper cites PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.104039Z

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Observation c7da7646-5372-4991-9e8d-772de7329a7b · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Very Deep Convolutional Networks for Large-Scale Image Recognition,

Reference 18

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Observation e1791707-b221-4a1f-99b6-4fd3aeed9638 · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Imagenet classification with deep convolutional neural networks,

Reference 19

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Observation 9481c0d0-7143-4f57-b202-a76a3089ce21 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,

Reference 20

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Observation 70e70bc3-bf95-4311-b6f6-b58f448fc694 · outbound

This paper cites Mvitv2: Improved multiscale vision transformers for classification and detection,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Mvitv2: Improved multiscale vision transformers for classification and detection,

Reference 21

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Observation aafc28dd-ee39-4937-9e9c-50cce0d3d12f · outbound

This paper cites A survey on vision transformer,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging A survey on vision transformer,

Reference 22

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Observation 7db6d84e-deeb-49fc-86ef-2081a40aedcd · outbound

This paper cites Medsegdiff-v2: Diffusion- based medical image segmentation with transformer,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Medsegdiff-v2: Diffusion- based medical image segmentation with transformer,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.039884Z

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Observation cb55cf44-1426-44a4-9df4-7b1459739a39 · outbound

This paper cites Climatelearn: Benchmarking machine learning for weather and climate modeling,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Climatelearn: Benchmarking machine learning for weather and climate modeling,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.027408Z

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Observation f9e45be5-e55d-4ef8-8c42-e7afd9cf67d6 · outbound

This paper cites Bidirectional generation of structure and properties through a single molecular foundation model,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Bidirectional generation of structure and properties through a single molecular foundation model,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.015932Z

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Observation 910678c2-8b5c-40a4-9bae-b8cea6965cd5 · outbound

This paper cites PtychoFormer: A Transformer-based Model for Ptychographic Phase Retrieval,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging PtychoFormer: A Transformer-based Model for Ptychographic Phase Retrieval,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:03.004088Z

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Observation 82a3f8e4-e26a-4b18-82b6-e45f44e2a55d · outbound

This paper cites On the Fraunhofer (Far Field) Diffraction Patterns of Opaque and Transparent Objects with Coherent Background,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging On the Fraunhofer (Far Field) Diffraction Patterns of Opaque and Transparent Objects with Coherent Background,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:02.991451Z

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Observation 563dc421-9b23-4f51-8646-410c1878a705 · outbound

This paper cites Kirchhoff’s theory for optical diffrac- tion, its predecessor and subsequent development: The resilience of an inconsistent theory,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Kirchhoff’s theory for optical diffrac- tion, its predecessor and subsequent development: The resilience of an inconsistent theory,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:02.964849Z

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Observation fc2c83f4-b9b1-4787-9a79-e74f80e6cbe4 · outbound

This paper cites An improved ptychographical phase retrieval algorithm for diffractive imaging,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging An improved ptychographical phase retrieval algorithm for diffractive imaging,

Reference 29

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Observation 6ab2d6b2-7178-451a-97a0-fa4bd1e7f2de · outbound

This paper cites Differential programming enabled functional imaging with Lorentz transmission electron mi- croscopy,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Differential programming enabled functional imaging with Lorentz transmission electron mi- croscopy,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:02.943942Z

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Observation 050c085b-7bbf-46cf-9e14-8ebd0f9a314e · outbound

This paper cites X-ray Ptychography Imaging of Hu- man Chromosomes After Low-dose Irradiation,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging X-ray Ptychography Imaging of Hu- man Chromosomes After Low-dose Irradiation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:02.932564Z

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Observation 4319761e-dd9c-4283-9022-4601b9d1bdaa · outbound

This paper cites In situ X-ray-based imaging of nano materials,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging In situ X-ray-based imaging of nano materials,

Reference 32

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Observation 9fa7a902-02a2-431b-9e9b-c705444d9822 · outbound

This paper cites Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency informa- tion,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency informa- tion,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:02.909556Z

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Observation f4ac8626-6f57-4714-9acc-f899219fa3a6 · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation,

Reference 34

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Observation 89a6a773-1a2f-4a51-9d2f-9f56d1da581d · outbound

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A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging SegFormer: Simple and efficient design for semantic segmentation with transformers,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:02.885801Z

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Observation ec1f8174-a9eb-4ac9-a361-8b9eb8d38c59 · outbound

This paper cites Cellpose3: One-click image restoration for improved cellular segmentation,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Cellpose3: One-click image restoration for improved cellular segmentation,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:02.874557Z

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Observation 81f95939-c407-451b-b482-976927c8b9d7 · outbound

This paper cites PIXART-$$ \Sigma $$: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging PIXART-$$ \Sigma $$: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:02.862015Z

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Observation 9489e77f-24cf-4b40-9efa-f2984c35d578 · outbound

This paper cites Medical image segmentation review: The success of u-net,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Medical image segmentation review: The success of u-net,

Reference 38

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This paper cites A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:02.843493Z

Source-reported events for the cited work

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Observation 3857277e-005f-40b4-9997-9fef73f463b0 · outbound

This paper cites Robust Estimation of a Location Parameter,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Robust Estimation of a Location Parameter,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:02.830563Z

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Observation a307cfcd-4a48-4dcb-8273-6a5ed667c053 · outbound

This paper cites VII. Note on regression and inheritance in the case of two parents,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging VII. Note on regression and inheritance in the case of two parents,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:02.818389Z

Source-reported events for the cited work

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Observation 42bf409f-fea3-43b0-9639-2eabcb8c2bc9 · outbound

This paper cites Loss functions for image restoration with neural networks,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Loss functions for image restoration with neural networks,

Reference 42

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Observation e8c92ea8-2b98-4673-897f-8adbbdd72cd1 · outbound

This paper cites Image quality assessment: From error visibility to structural similarity,.

A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging Image quality assessment: From error visibility to structural similarity,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:36:02.797516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.