Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction | Paper Citation Record

Observation 216e3262-e3f0-4df3-b55f-199cc3799064 · outbound

This paper cites Digital holography,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Digital holography,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.833886Z

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Observation e2480a0c-f5a7-4642-9e3a-2d3cfa435ab3 · outbound

This paper cites Real-time 3-d sensing, visualization and recognition of dynamic biological microorganisms,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Real-time 3-d sensing, visualization and recognition of dynamic biological microorganisms,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.906155Z

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Observation 15742304-a549-4970-87ce-df16504f6022 · outbound

This paper cites Optical tomographic image reconstruction based on beam propagation and sparse regularization,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Optical tomographic image reconstruction based on beam propagation and sparse regularization,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.941638Z

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Observation ab203df0-bb56-465e-a223-6f2bfaa43f74 · outbound

This paper cites Fringe pattern improvement and super-resolution using deep learning in digital holography,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Fringe pattern improvement and super-resolution using deep learning in digital holography,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.945172Z

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Observation 0aeaf3d0-206e-4803-8fbb-a8991918ff54 · outbound

This paper cites Neuroblastoma cells classification through learning approaches by direct analysis of digital holograms,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Neuroblastoma cells classification through learning approaches by direct analysis of digital holograms,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.835665Z

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Observation b3e6f8f2-9386-4e78-a354-3b6e1b29afb7 · outbound

This paper cites Coherent x-ray diffraction imaging,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Coherent x-ray diffraction imaging,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.832052Z

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Observation 89c52db9-eb71-434e-9c5e-4c68d5cd529f · outbound

This paper cites Classical holography in the terahertz range: Recording and reconstruction techniques,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Classical holography in the terahertz range: Recording and reconstruction techniques,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.898004Z

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Observation 64c68c80-a4a5-4cc5-b30e-d6e2711d3fad · outbound

This paper cites A new microscopic principle.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction A new microscopic principle

Reference 8

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Observation 1260b8a9-f0b1-40d7-9ed1-5e7dcee2ec6d · outbound

This paper cites Quantitative scanning transmission electron microscopy for materials science: Imaging, diffraction, spectroscopy, and tomography,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Quantitative scanning transmission electron microscopy for materials science: Imaging, diffraction, spectroscopy, and tomography,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.936089Z

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Observation 2da7109c-aa6c-4d04-a385-53aba63ef814 · outbound

This paper cites Phase imaging methods in the scanning transmission electron microscope,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Phase imaging methods in the scanning transmission electron microscope,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.937905Z

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Observation 9203cf15-db63-46bd-8ee5-6c8d6d2bdc89 · outbound

This paper cites an unresolved cited work.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-07-05T12:01:02.939825Z

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Observation 439d4718-9a2b-4930-a470-599553637434 · outbound

This paper cites A practical algorithm for the determination of plane from image and diffraction pictures,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction A practical algorithm for the determination of plane from image and diffraction pictures,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.934044Z

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Observation 376e82a2-1de5-4815-9b41-c21309d42831 · outbound

This paper cites Reconstruction of an object from the modulus of its fourier transform,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Reconstruction of an object from the modulus of its fourier transform,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.943412Z

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Observation abffaa5b-1e42-436e-b729-989ffb472a13 · outbound

This paper cites Phase retrieval algorithms: a comparison,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Phase retrieval algorithms: a comparison,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.932208Z

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Observation 470382c7-141d-4a1e-a648-1b6d793ec3df · outbound

This paper cites Understanding the twin-image problem in phase retrieval,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Understanding the twin-image problem in phase retrieval,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.930491Z

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Observation 6cb26f4a-8971-4412-a117-9485213598d7 · outbound

This paper cites Solution to the twin image problem in holography,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Solution to the twin image problem in holography,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.828774Z

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Observation 1de12ff3-7d4d-4400-977b-710c6b8f2b83 · outbound

This paper cites From fienup’s phase retrieval techniques to regularized inversion for in-line holography: tutorial,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction From fienup’s phase retrieval techniques to regularized inversion for in-line holography: tutorial,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.889927Z

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Observation 538896e1-be66-463b-84b2-31f39b8804c8 · outbound

This paper cites Penalized-likelihood image reconstruction for digital holography,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Penalized-likelihood image reconstruction for digital holography,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.891946Z

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Observation 6731ce81-5650-46c2-b886-07cf1ee8c73f · outbound

This paper cites Inline hologram reconstruction with sparsity constraints,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Inline hologram reconstruction with sparsity constraints,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.893826Z

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Observation 849fd5bc-d23b-4888-b85d-f7392cccada2 · outbound

This paper cites Characterizing and tracking single colloidal particles with video holographic microscopy,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Characterizing and tracking single colloidal particles with video holographic microscopy,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.904387Z

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Observation a23b3e18-b877-46cf-a771-e10a96d56774 · outbound

This paper cites Inverse-problem approach for particle digital holography: accurate location based on local optimization,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Inverse-problem approach for particle digital holography: accurate location based on local optimization,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.830472Z

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Observation eac4699d-984a-4987-8edd-9674d6db44bc · outbound

This paper cites Multispectral in-line hologram reconstruction with aberration compensation applied to Gram-stained bacteria microscopy,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Multispectral in-line hologram reconstruction with aberration compensation applied to Gram-stained bacteria microscopy,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.908252Z

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Observation 6dd14702-dc54-43bd-b380-5bf89d3d61fc · outbound

This paper cites Plug-and-play priors for model based reconstruction.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Plug-and-play priors for model based reconstruction

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.845321Z

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Observation bd2694c9-5009-4313-979e-1699d472b634 · outbound

This paper cites Plug-and-play admm for image restoration: Fixed-point convergence and applications,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Plug-and-play admm for image restoration: Fixed-point convergence and applications,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.881422Z

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Observation f27f97b2-dbb5-4590-8fcd-77e98c78fdf4 · outbound

This paper cites The little engine that could: Regularization by denoising (red),.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction The little engine that could: Regularization by denoising (red),

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.883119Z

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Observation 790c30dc-ad08-439d-a217-6275183848fa · outbound

This paper cites Deep learning techniques for inverse problems in imaging,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Deep learning techniques for inverse problems in imaging,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.899810Z

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Observation ac23eec0-03fe-4f0c-a55d-f3c127d90b11 · outbound

This paper cites Deep holography,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Deep holography,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.843233Z

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Observation 30614ef4-d798-483b-b36d-431181e78dc7 · outbound

This paper cites On the use of deep learning for phase recovery,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction On the use of deep learning for phase recovery,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.841451Z

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Observation 67c6e122-6fa3-4bb3-95d7-c541176ddef9 · outbound

This paper cites Lensless computational imaging through deep learning,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Lensless computational imaging through deep learning,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.902412Z

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Observation 6adbfe51-6ec9-4ac3-a61b-730edd5b414d · outbound

This paper cites eHoloNet: a learning-based end-to-end approach for in-line digital holographic reconstruction,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction eHoloNet: a learning-based end-to-end approach for in-line digital holographic reconstruction,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.895976Z

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Observation 8a2ff570-8336-4a48-897a-209401031007 · outbound

This paper cites Learning fast approximations of sparse coding,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Learning fast approximations of sparse coding,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.921260Z

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Observation e23e4cad-ec74-4128-8f2c-419b76e04de6 · outbound

This paper cites prdeep: Robust phase retrieval with a flexible deep network,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction prdeep: Robust phase retrieval with a flexible deep network,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.917697Z

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Observation 26a451a2-60e0-46f4-ae73-9746b1d359d1 · outbound

This paper cites Physics-based iterative projection complex neural network for phase retrieval in lensless microscopy imaging,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Physics-based iterative projection complex neural network for phase retrieval in lensless microscopy imaging,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.925218Z

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Observation 75a3aeee-7260-4592-bcab-ec7048e61b87 · outbound

This paper cites Dualprnet: Deep shrinkage dual frame network for deep unrolled phase retrieval,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Dualprnet: Deep shrinkage dual frame network for deep unrolled phase retrieval,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.923337Z

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Observation cfa38700-bc7c-4339-8b29-dbdbbf42c1f5 · outbound

This paper cites Hionet: deep priors based deep unfolded network for phase retrieval,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Hionet: deep priors based deep unfolded network for phase retrieval,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.910208Z

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Observation dc0dfcf6-a502-4ea8-8d03-998391c5acdc · outbound

This paper cites Digital holographic reconstruction based on deep learning framework with unpaired data,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Digital holographic reconstruction based on deep learning framework with unpaired data,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.912008Z

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Observation 0adcfa68-f9a8-44a3-b3f7-6823fa6a4f65 · outbound

This paper cites PhaseGAN: a deep-learning phase-retrieval approach for unpaired datasets,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction PhaseGAN: a deep-learning phase-retrieval approach for unpaired datasets,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.913900Z

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Observation 14b5002f-6063-4a59-a3e6-2dd0aa97db13 · outbound

This paper cites Phase recovery and holographic image reconstruction using deep learning in neural networks,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Phase recovery and holographic image reconstruction using deep learning in neural networks,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.915714Z

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Observation 860aa324-739a-452a-b766-7318267d8d43 · outbound

This paper cites Y-net: a one-to-two deep learning framework for digital holographic reconstruction,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Y-net: a one-to-two deep learning framework for digital holographic reconstruction,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.927035Z

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Observation 9a597541-434b-4033-8a63-cdb74cf0d466 · outbound

This paper cites Noise-free quantitative phase imaging in gabor holography with conditional generative adversarial network,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Noise-free quantitative phase imaging in gabor holography with conditional generative adversarial network,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.949090Z

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Observation 379d453f-29e6-463c-abc6-d6e3dc14adc3 · outbound

This paper cites Fourier imager network (FIN): A deep neural network for hologram reconstruction with superior external generalization,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Fourier imager network (FIN): A deep neural network for hologram reconstruction with superior external generalization,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.877085Z

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Observation 6a968086-ad4c-4467-8f29-41ab6722226c · outbound

This paper cites Deep DIH: Single-shot digital in-line holography reconstruction by deep learning,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Deep DIH: Single-shot digital in-line holography reconstruction by deep learning,

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-07-03T07:17:43.357038Z

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Observation 3a99ae86-cd06-4326-9afb-729e38c9f3e1 · outbound

This paper cites Holographic optical field recovery using a regularized untrained deep decoder network,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Holographic optical field recovery using a regularized untrained deep decoder network,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.839684Z

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Observation ff7abd41-7a79-4639-ba11-1479b70ca51d · outbound

This paper cites Untrained deep network powered with explicit denoiser for phase recovery in inline holography,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Untrained deep network powered with explicit denoiser for phase recovery in inline holography,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.837602Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1bb4dcba-5af7-424e-a114-bc6733da624a · outbound

This paper cites Phase imaging with an untrained neural network,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Phase imaging with an untrained neural network,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.874677Z

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Observation d07b51d9-b36a-4af9-b6db-d0dcd8666c1e · outbound

This paper cites Physics-enhanced neural network for phase retrieval from two diffraction patterns,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Physics-enhanced neural network for phase retrieval from two diffraction patterns,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.879296Z

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Observation 5dab3908-6b6f-4ae8-90f0-38e8c822a00e · outbound

This paper cites Self-supervised learning of hologram reconstruction using physics consistency,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Self-supervised learning of hologram reconstruction using physics consistency,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.885337Z

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Observation ab42428e-03a0-44fb-a2d0-d35ac7766a67 · outbound

This paper cites Convolutional neural networks for inverse problems in imaging: A review,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Convolutional neural networks for inverse problems in imaging: A review,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.852824Z

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Observation 5f3ccdad-b2ae-4f52-8d24-f4c8182be596 · outbound

This paper cites Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networks,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networks,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.854677Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c0cd3fd3-03ba-450e-8c7b-8eeac32dad16 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction U-net: Convolutional networks for biomedical image segmentation,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.919371Z

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Observation eb9c4993-8c83-4383-9f68-370da6ed375e · outbound

This paper cites Sar2sar: A semi-supervised despeckling algorithm for sar images,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Sar2sar: A semi-supervised despeckling algorithm for sar images,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.850766Z

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Observation 8a4966d6-bb28-43bc-b28f-288559a4ccec · outbound

This paper cites Near-field lorenz-mie theory and its application to microholography,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Near-field lorenz-mie theory and its application to microholography,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.928767Z

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Observation 3ad91879-afe2-470f-b236-7f96743502f5 · outbound

This paper cites On the convergence of adam and beyond.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction On the convergence of adam and beyond

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.847080Z

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Observation ec5095cf-faf4-4b86-b0bd-86a1081a92a1 · outbound

This paper cites Autofocusing in digital holographic microscopy,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Autofocusing in digital holographic microscopy,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.849004Z

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Observation b492c857-f60c-4935-88da-eeeac327d365 · outbound

This paper cites Fresnelets: new multiresolution wavelet bases for digital holography,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Fresnelets: new multiresolution wavelet bases for digital holography,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.856549Z

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Observation 4bf67dcc-37cb-44de-8a22-b65cda862867 · outbound

This paper cites Computer control of microscopes using µManager,.

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction Computer control of microscopes using µManager,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T12:01:02.887641Z

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