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REVIEW 4 major objections 6 minor 60 references

Video-rate gigapixel ptychography via space-time neural field representations

T0 review · 4 major / 6 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read The paper demonstrates that a single coded image sensor, translated beneath a dynamic sample and reconstructed through a space-time neural field factorization, can produce 30-fps phase videos with 308-nm resolution over centimeter-scale fie

desk verdict A real advance in dynamic lensless ptychography, but the headline 'gigapixel' claim is not supported by the numbers as written. read the letter →

arxiv 2511.06126 v1 pith:CEQDK4JK submitted 2025-11-08 physics.optics eess.IV

Ruihai Wang , Qianhao Zhao , Zhixuan Hong , Qiong Ma , Tianbo Wang , Lingzhi Jiang , Liming Yang , Shaowei Jiang
show 9 more authors
Feifei Huang Thanh D. Nguyen Leslie Shor Daniel Gage Mary Lipton Christopher Anderton Arunima Bhattacharjee David Brady Guoan Zheng
This is my paper · ORCID
classification physics.opticseess.IV
keywords ptychographygigapixelimagingspace-timeneuralfieldsphaseretrievallenslessvideo-ratecomputationallow-rankfactorization
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Ptychography normally requires many overlapping measurements, so capturing a moving sample in real time seems hopeless. This paper argues that the redundancy is not a burden but a resource: if all frames are reconstructed jointly as a low-rank factorization of a space-time field, each new coded-sensor measurement can improve both spatial resolution and temporal tracking. On a compact lensless setup with a 405-nm laser and a coded sensor moving at 30 fps, it claims to resolve 308-nm linewidths across centimeter-scale fields, and demonstrates the approach on melting snowflakes, crystallization, stem cells, bacterial growth, dissolving microneedles, and time-varying EUV probe beams. The reason a reader should care is that, if right, it turns ptychography from a slow quasi-static technique into a general lensless video-rate imaging tool, and it recasts the space-bandwidth bottleneck as a correlation-extraction problem rather than a hardware-scaling problem.

What carries the argument

The load-bearing object is the space-time neural field factorized as a Hadamard product of hash-encoded spatial features and interpolated temporal feature vectors — a low-rank representation of the complex optical field. Multi-resolution hash encoding compresses the gigapixel spatial coordinates into a shared lookup table (claimed up to roughly 1000-fold compression), and a compact set of temporal vectors (32-dimensional in the implementation) encodes dynamics without explicit motion models. Dual MLPs decode the real and imaginary parts separately to avoid phase-wrap discontinuities, and a gradient-domain loss on spatial derivatives of amplitude supplies the optimization signal. This factori

What would settle it

Use the same hardware and reconstruction approach on a specimen of many independently moving particles with no shared dynamics, such as dense random Brownian motion, and compare recovered frames against ground truth. If the low-rank factorization cannot track independent motions or produces artifacts while a control scene with correlated dynamics reconstructs cleanly, the central scaling claim is falsified. A more quantitative variant: sweep the number of temporal feature vectors on a fixed dataset and show that scenes with N independent moving parts require roughly N temporal vectors for arti

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Extended reading notes

Core claim

At the center of the work is a claim about how to lift the space-bandwidth barrier in ptychography. Instead of reconstructing each frame independently from its own diffraction pattern, the authors factorize the entire space-time volume into multi-resolution hash-encoded spatial features and a small number of learnable temporal feature vectors; every voxel's complex field is formed by the Hadamard product of those features, decoded by two MLPs into real and imaginary parts. A gradient-domain loss on measured versus predicted intensities couples all frames, and the recovered field can be digitally propagated to a chosen focus. The paper reports that this joint optimization converges where fram

Load-bearing premise

The argument rests on the assumption that the full space-time field of any captured scene is well approximated by a low-rank product of shared spatial features and a compact set of temporal feature vectors; if a scene contains many independently moving or rapidly decorrelating regions, this factorization cannot represent it and the reconstruction advantage disappears.

Editorial extensions

If this is right

  • Ptychography can monitor non-repeatable dynamics such as melting, crystallization, cell division, bacterial growth, and drug-device dissolution at video rate, instead of being limited to quasi-static samples.
  • A single sensor with a moving coded surface suffices for gigapixel-scale video, avoiding multi-camera arrays or long sequential acquisitions.
  • Post-measurement digital refocusing becomes a natural byproduct, eliminating real-time autofocus requirements for live-cell and incubator imaging.
  • In EUV, X-ray, and electron ptychography, time-varying illumination or radiation-induced sample evolution can be modeled as temporal features, potentially lowering overlap requirements and radiation dose.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the scaling claim is right, then recovered resolution or space-bandwidth product should improve as temporal correlation is exploited; a direct test is to acquire the same dynamic scene with increasing numbers of frames and see whether SBP grows with correlation rather than with measurement count.
  • The fixed 32-dimensional temporal basis may not scale to scenes with many independent motions; one can test this by imaging several independently moving objects and checking whether artifact-free reconstruction requires a larger temporal dimension or fails entirely.
  • Because the paper treats probe variation as a temporal function, a natural extension is to model other slowly varying systematic errors, such as sample drift, illumination drift, or stage wobble, as additional temporal features in the same framework.
  • The success at very low overlap in the EUV demonstration suggests that ptychographic overlap requirements may be governed more by the strength of the temporal prior than by geometric redundancy; this could be tested on simulated data with controlled dynamics and known ground truth.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The manuscript reports a video-rate ptychographic imaging system based on a translating coded sensor and a space-time neural field representation. The complex optical field at each time is factorized as a Hadamard product of multi-resolution hash-encoded spatial features and compact temporal feature vectors, decoded by dual MLPs that output real and imaginary components, and optimized with a gradient-domain loss against a ptychographic forward model. Experiments demonstrate USAF resolution targets claimed to resolve 308-nm linewidths, snowflake melting, Na2CO3 crystallization, stem-cell wound healing and division, E. coli growth in microfluidics, microneedle dissolution, and EUV time-varying probe reconstruction. The central claim is that single-sensor, video-rate gigapixel SBP imaging with centimeter-scale field coverage is achieved at 30 fps.

Significance. If fully supported, the work would be a significant advance: lensless ptychography with mesoscale field of view, sub-micron resolution, and video-rate temporal sampling, with label-free quantitative phase, post-measurement refocusing, and dynamic EUV probe recovery. Strengths include the availability of open-source code and datasets, external validation against USAF linewidths and a known 700-µm microneedle design height, and the use of a literature-based dry-mass conversion. However, the headline quantitative claim of a gigapixel space-bandwidth product currently lacks a direct SBP accounting, and the central low-rank factorization assumption is not characterized. These issues are load-bearing for the paper's main claim.

major comments (4)
  1. [Abstract; Methods, 'Video-rate gigapixel ptychographic sensing system'] The headline claim of 'gigapixel SBP' and 'centimeter-scale fields' is never supported by an explicit SBP calculation. The sensor is 5120×3840 with 1.4-µm pixels, giving an active area of about 7.2×5.4 mm. At the claimed 308-nm resolution, the number of resolved modes over that static area is roughly 0.4 gigapixels, below the advertised value. If the coded-sensor translation enlarges the effective FOV, the text must state the reconstructed FOV and output grid size; it does not. The Methods list 'reconstruction grid dimensions and magnification' among unspecified parameters. Without this accounting, the central quantitative claim is unverified.
  2. [Methods, 'Space-time neural field representations'; Fig. 1a] The reconstruction rests on the assumption that the entire dynamic scene is well approximated by a Hadamard product of shared spatial hash-encoded features and a compact set of T temporal feature vectors (stated to be in R^32, with T not specified). No analysis is provided of the class of dynamics for which this low-rank factorization holds, how T should scale with scene complexity, or what happens for scenes with many independently moving components. This is load-bearing because the conditioning that turns underdetermined single-frame diffraction data into a tractable problem depends directly on the validity of this factorization.
  3. [Fig. 4h; Methods, 'Dry-mass tracking of bacterial growth'] The quantitative dry-mass growth curve in Fig. 4h and the microneedle dissolution kinetics in Fig. 5d are presented as quantitative results without error bars, replicate counts, or uncertainty propagation. The text claims 'picogram sensitivity,' but no measurement-noise or sensitivity analysis is given. These omissions weaken the biomedical quantitative claims, which are central to the paper's stated versatility beyond the resolution demonstration.
  4. [Discussion] The statement that 'SBP grows with the efficiency of correlation extraction rather than the number of independent measurements' is presented as the main scaling-law conclusion, but no theorem, bound, or information-theoretic argument supports it. As written it is a slogan rather than a result. Resolved information is bounded by measurement diversity plus validity of the prior/factorization; the paper should either formalize this claim or temper it to match what is actually demonstrated.
minor comments (6)
  1. [Results, first paragraph] Typo: 'EVU' should be 'EUV' in the sentence about extreme ultraviolet ptychography.
  2. [Methods, 'Space-time neural field representations'] The hash table capacity is given as '2^26 entries' but the text says 'up to 226 entries' (the superscript is lost). Please fix the formatting.
  3. [Methods, 'Reconstruction via gradient-domain loss'] 'initial learning rate of 10-3' should read '10^-3'.
  4. [Results, paragraph on bacterial growth] The dry-mass formula is referenced as 'In Fig. 4b' but the quantification appears in Fig. 4h; please correct the cross-reference.
  5. [Fig. 5 and Methods, 'Transdermal microneedle patch'] The Methods state 'approximately 100 microneedles per 1×1 cm²' while the Results describe 'hundreds of individual needles.' These numbers should be reconciled.
  6. [Fig. 2 and Supplementary Figs. S3-S4] The 'gigapixel-scale' claim for the crystal dataset should be accompanied by the actual reconstruction array dimensions and pixel count; the main text never specifies the output grid for any dataset.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: central claims are externally benchmarked and self-citations are not load-bearing.

full rationale

The derivation chain is not circular. The coded-surface probe is calibrated against a static blood-smear reference (Methods), so the dynamic-sample reconstructions are not fitting to the quantity they later claim to measure. Resolution is benchmarked by USAF group 10 element 5 (308-nm linewidth); microneedle height is compared with the independent 700-μm fabrication specification; dry-mass quantification uses a literature refractive increment value (Methods, ref 60). These are external checks, not fitted constants renamed as predictions. The space-time low-rank factorization is an explicit modeling assumption (Methods, 'Space-time neural field representations'), not a result derived from the gigapixel claim; it is tested across multiple dynamics and compared against ePIE/mPIE/least-squares. Self-citations (e.g., refs 6, 39) support design choices such as coded ptychography and gradient-domain loss, but the present manuscript supplies its own forward model, calibration, and validation, so no load-bearing step reduces to an unverified self-citation. The absence of an explicit SBP accounting and the slogan-like 'SBP grows with correlation extraction' statement are quantitative-support/correctness concerns, not definitional or fitted-input circularity.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

All load-bearing assumptions are domain/modeling assumptions rather than standard math or invented physical entities. The free parameters are chiefly neural-field hyperparameters and propagation distances that are underreported in the main text; the paper's 'gigapixel' scaling claim depends on the unreported reconstruction grid.

free parameters (7)
  • sample-to-mask propagation distance d1 = 0.2–2 mm (range)
    Forward model uses angular-spectrum propagation over d1; exact value needed for reconstruction but only a range is given, implying per-experiment tuning/calibration.
  • mask-to-detector distance d2 = not stated
    Coded surface is on the sensor, but the model still propagates to the detector over d2; no value or calibration is reported.
  • hash-table capacity = 2^26 entries
    Chosen for memory/compression; affects spatial resolution and reconstruction capacity.
  • hash levels L and features-per-level F = not stated in main text
    Multi-resolution hash encoding hyperparameters; control representational capacity and resolution.
  • temporal feature count T = not stated; features are in R^32
    Number of learnable temporal basis vectors sets the temporal degrees of freedom; not specified.
  • reconstruction grid dimensions and magnification = not stated
    The 'gigapixel' claim depends on the output pixel count; no value or formula is provided.
  • initial learning rate (Adam) = 1e-3
    Optimization hyperparameter; 'typical convergence within 10 epochs' is claimed without schedule details.
assumptions (5)
  • domain assumption Scalar angular-spectrum free-space propagation is an adequate forward model; the sample is thin/single-scattering within the propagation path.
    Methods use angular spectrum over d1 and d2; objects include 700-µm microneedles and cell clusters, so multislice or multiple-scattering effects are neglected without validation.
  • domain assumption The coded-surface transmission function is known exactly from ePIE calibration and remains stable during voice-coil translation.
    Methods: 'coded surface pattern is pre-calibrated using a static blood smear...'; reconstruction treats the mask as a fixed known probe.
  • domain assumption Sensor translations (x_t, y_t) are tracked to sub-pixel accuracy by fiducial cross-correlation and have no unmodeled out-of-plane motion or rotation.
    Methods: 'Cross-correlation... provides sub-pixel registration accuracy'; no sensitivity analysis for tracking errors.
  • domain assumption Dynamic scenes are low-rank in space-time, representable as Hadamard product of shared spatial hash features and compact temporal features.
    Central architecture (Fig. 1a); the claim that each measurement improves all frames depends on this factorization.
  • domain assumption Illumination is spatially coherent across the entire field of view.
    A 405-nm laser diode illuminates without added optics; no partial-coherence model is included.

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Cite this review

Pith. "Pith review of Video-rate gigapixel ptychography via space-time neural field representations." pith.science (2026). https://pith.science/paper/CEQDK4JK

@misc{pith2026251106126,
  author       = {Pith},
  title        = {Pith review of: Video-rate gigapixel ptychography via space-time neural field representations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CEQDK4JK}},
  note         = {Machine review of arXiv:2511.06126}
}
read the original abstract

Achieving gigapixel space-bandwidth products (SBP) at video rates represents a fundamental challenge in imaging science. Here we demonstrate video-rate ptychography that overcomes this barrier by exploiting spatiotemporal correlations through neural field representations. Our approach factorizes the space-time volume into low-rank spatial and temporal features, transforming SBP scaling from sequential measurements to efficient correlation extraction. The architecture employs dual networks for decoding real and imaginary field components, avoiding phase-wrapping discontinuities plagued in amplitude-phase representations. A gradient-domain loss on spatial derivatives ensures robust convergence. We demonstrate video-rate gigapixel imaging with centimeter-scale coverage while resolving 308-nm linewidths. Validations span from monitoring sample dynamics of crystals, bacteria, stem cells, microneedle to characterizing time-varying probes in extreme ultraviolet experiments, demonstrating versatility across wavelengths. By transforming temporal variations from a constraint into exploitable correlations, we establish that gigapixel video is tractable with single-sensor measurements, making ptychography a high-throughput sensing tool for monitoring mesoscale dynamics without lenses.

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Reference graph

Works this paper leans on

60 extracted references

  1. [1]

    Space –bandwidth product of optical signals and systems

    Lohmann AW, Dorsch RG, Mendlovic D, Zalevsky Z, Ferreira C. Space –bandwidth product of optical signals and systems. JOSA A 1996, 13(3): 470-473

    1996

  2. [2]

    Review of bio-optical imaging systems with a high space-bandwidth product

    Park J, Brady DJ, Zheng G, Tian L, Gao L. Review of bio-optical imaging systems with a high space-bandwidth product. Advanced Photonics 2021, 3(4): 044001

    2021

  3. [3]

    Whole slide imaging in pathology: advantages, limitations, and emerging perspectives

    Farahani N, Parwani A V , Pantanowitz L. Whole slide imaging in pathology: advantages, limitations, and emerging perspectives. Pathology and Laboratory Medicine International 2015, 7: 23-33

    2015

  4. [4]

    OpenWSI: a low-cost, high-throughput whole slide imaging system via single-frame autofocusing and open-source hardware

    Guo C, Bian Z, Jiang S, Murphy M, Zhu J, Wang R , et al. OpenWSI: a low-cost, high-throughput whole slide imaging system via single-frame autofocusing and open-source hardware. Optics Letters 2020, 45(1): 260-263

    2020

  5. [5]

    High-throughput digital pathology via a handheld, multiplexed, and AI-powered ptychographic whole slide scanner

    Jiang S, Guo C, Song P, Wang T, Wang R, Zhang T , et al. High-throughput digital pathology via a handheld, multiplexed, and AI-powered ptychographic whole slide scanner. Lab on a Chip 2022, 22(14): 2657-2670

    2022

  6. [6]

    Resolution-Enhanced Parallel Coded Ptychography for High-Throughput Optical Imaging

    Jiang S, Guo C, Song P, Zhou N, Bian Z, Zhu J , et al. Resolution-Enhanced Parallel Coded Ptychography for High-Throughput Optical Imaging. ACS Photonics 2021, 8(11): 3261-3271

    2021

  7. [7]

    Wide -field, high -resolution Fourier ptychographic microscopy

    Zheng G, Horstmeyer R, Yang C. Wide -field, high -resolution Fourier ptychographic microscopy. Nature photonics 2013, 7(9): 739

    2013

  8. [8]

    Concept, implementations and applications of Fourier ptychography

    Zheng G, Shen C, Jiang S, Song P, Yang C. Concept, implementations and applications of Fourier ptychography. Nature Reviews Physics 2021, 3(3): 207-223

    2021

Show all 60 references
  1. [9]

    Coded aperture compressive temporal imaging

    Llull P, Liao X, Y uan X, Yang J, Kittle D, Carin L, et al. Coded aperture compressive temporal imaging. Optics express 2013, 21(9): 10526-10545

    2013

  2. [10]

    Multiscale lens design

    Brady DJ, Hagen N. Multiscale lens design. Optics express 2009, 17(13): 10659-10674

    2009

  3. [11]

    Multiscale gigapixel photography

    Brady DJ, Gehm ME, Stack RA, Marks DL, Kittle DS, Golish DR, et al. Multiscale gigapixel photography. Nature 2012, 486(7403): 386-389

    2012

  4. [12]

    Design and scaling of monocentric multiscale imagers

    Tremblay EJ, Marks DL, Brady DJ, Ford JE. Design and scaling of monocentric multiscale imagers. Applied Optics 2012, 51(20): 4691-4702

    2012

  5. [13]

    Video-rate imaging of biological dynamics at centimetre scale and micrometre resolution

    Fan J, Suo J, Wu J, Xie H, Shen Y , Chen F, et al. Video-rate imaging of biological dynamics at centimetre scale and micrometre resolution. Nature Photonics 2019, 13(11): 809-816

    2019

  6. [14]

    Parallelized computational 3D video microscopy of freely moving organisms at multiple gigapixels per second

    Zhou KC, Harfouche M, Cooke CL, Park J, Konda PC, Kreiss L , et al. Parallelized computational 3D video microscopy of freely moving organisms at multiple gigapixels per second. Nature photonics 2023, 17(5): 442- 450. 15

    2023

  7. [15]

    An array microscope for ultrarapid virtual slide processing and telepathology

    Weinstein RS, Descour MR, Liang C, Barker G, Scott KM, Richter L , et al. An array microscope for ultrarapid virtual slide processing and telepathology. Design, fabrication, and validation study. Human pathology 2004, 35(11): 1303-1314

    2004

  8. [16]

    Nerf: Representing scenes as neural radiance fields for view synthesis

    Mildenhall B, Srinivasan PP, Tancik M, Barron JT, Ramamoorthi R, Ng R. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM 2021, 65(1): 99-106

    2021

  9. [17]

    Fourier features let networks learn high frequency functions in low dimensional domains

    Tancik M, Srinivasan P, Mildenhall B, Fridovich- Keil S, Raghavan N, Singhal U , et al. Fourier features let networks learn high frequency functions in low dimensional domains. Advances in neural information processing systems 2020, 33: 7537-7547

    2020

  10. [18]

    Nerv: Neural representations for videos

    Chen H, He B, Wang H, Ren Y , Lim SN, Shrivastava A. Nerv: Neural representations for videos. Advances in Neural Information Processing Systems 2021, 34: 21557-21568

    2021

  11. [19]

    Instant neural graphics primitives with a multiresolution hash encoding

    Müller T, Evans A, Schied C, Keller A. Instant neural graphics primitives with a multiresolution hash encoding. ACM transactions on graphics (TOG) 2022, 41(4): 1-15

    2022

  12. [20]

    Tensorf: Tensorial radiance fields

    Chen A, Xu Z, Geiger A, Yu J, Su H. Tensorf: Tensorial radiance fields. European conference on computer vision; 2022: Springer; 2022. p. 333-350

    2022

  13. [21]

    Implicit neural representations with periodic activation functions

    Sitzmann V , Martel J, Bergman A, Lindell D, Wetzstein G. Implicit neural representations with periodic activation functions. Advances in neural information processing systems 2020, 33: 7462-7473

    2020

  14. [22]

    K-planes: Explicit radiance fields in space, time, and appearance

    Fridovich-Keil S, Meanti G, Warburg FR, Recht B, Kanazawa A. K-planes: Explicit radiance fields in space, time, and appearance. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2023

    2023

  15. [23]

    FPM -WSI: Fourier ptychographic whole slide imaging via feature-domain backdiffraction

    Zhang S, Wang A, Xu J, Feng T, Zhou J, Pan A. FPM -WSI: Fourier ptychographic whole slide imaging via feature-domain backdiffraction. Optica 2024, 11(5): 634-646

    2024

  16. [24]

    DNF: diffractive neural field for lensless microscopic imaging

    Zhu H, Liu Z, Zhou Y , Ma Z, Cao X. DNF: diffractive neural field for lensless microscopic imaging. Optics express 2022, 30(11): 18168-18178

    2022

  17. [25]

    Recovery of continuous 3D refractive index maps from discrete intensity-only measurements using neural fields

    Liu R, Sun Y , Zhu J, Tian L, Kamilov US. Recovery of continuous 3D refractive index maps from discrete intensity-only measurements using neural fields. Nature Machine Intelligence 2022, 4(9): 781-791

    2022

  18. [26]

    Fourier ptychographic microscopy image stack reconstruction using implicit neural representations

    Zhou H, Feng BY , Guo H, Lin S, Liang M, Metzler CA , et al. Fourier ptychographic microscopy image stack reconstruction using implicit neural representations. Optica 2023, 10(12): 1679-1687

    2023

  19. [27]

    Whole-field, high-resolution Fourier ptychography with neural pupil engineering

    Zhang S, Cao L. Whole-field, high-resolution Fourier ptychography with neural pupil engineering. Optica 2025, 12(10): 1615-1624

    2025

  20. [28]

    Neural space –time model for dynamic multi -shot imaging

    Cao R, Divekar NS, Nunez JK, Upadhyayula S, Waller L. Neural space –time model for dynamic multi -shot imaging. Nature Methods 2024, 21(12): 2336-2341

    2024

  21. [29]

    RHINO: regularizing the hash-based implicit neural representation

    Zhu H, Liu F, Zhang Q, Ma Z, Cao X. RHINO: regularizing the hash-based implicit neural representation. Science China Information Sciences 2025, 69(1): 112101

    2025

  22. [30]

    Neural-field-assisted transport-of-intensity phase microscopy: partially coherent quantitative phase imaging under unknown defocus distance

    Jin Y , Lu L, Zhou S, Zhou J, Fan Y , Zuo C. Neural-field-assisted transport-of-intensity phase microscopy: partially coherent quantitative phase imaging under unknown defocus distance. Photonics Research 2024, 12(7): 1494- 1501

    2024

  23. [31]

    High -speed and wide- field nanoscale table-top ptychographic EUV imaging and beam characterization with a sCMOS detector

    Eschen W, Liu C, Penagos Molina DS, Klas R, Limpert J, Rothhardt J. High -speed and wide- field nanoscale table-top ptychographic EUV imaging and beam characterization with a sCMOS detector. Optics Express 2023, 31(9): 14212-14224

    2023

  24. [32]

    Ptychographic electron microscopy using high- angle dark-field scattering for sub-nanometre resolution imaging

    Humphry M, Kraus B, Hurst A, Maiden A, Rodenburg J. Ptychographic electron microscopy using high- angle dark-field scattering for sub-nanometre resolution imaging. Nature communications 2012, 3(1): 730

    2012

  25. [33]

    Electron ptychography of 2D materials to deep sub-ångström resolution

    Jiang Y , Chen Z, Han Y , Deb P, Gao H, Xie S, et al. Electron ptychography of 2D materials to deep sub-ångström resolution. Nature 2018, 559(7714): 343-349

    2018

  26. [34]

    X-ray ptychography

    Pfeiffer F. X-ray ptychography. Nature Photonics 2018, 12(1): 9-17

    2018

  27. [35]

    Ptychography at all wavelengths

    Wang R, Zhao Q, Loetgering L, Allars F, Hong Z, Pennycook JT, et al. Ptychography at all wavelengths. Nature Reviews Methods Primers 2025, 5

    2025

  28. [36]

    Poisson image editing

    Pérez P, Gangnet M, Blake A. Poisson image editing. Seminal Graphics Papers: Pushing the Boundaries, Volume 2, 2023, pp 577-582

    2023

  29. [37]

    Gradientshop: A gradient -domain optimization framework for image and video filtering

    Bhat P, Zitnick CL, Cohen M, Curless B. Gradientshop: A gradient -domain optimization framework for image and video filtering. ACM Transactions on Graphics (TOG) 2010, 29(2): 1-14

    2010

  30. [38]

    ELFPIE: an error -laxity Fourier ptychographic iterative engine

    Zhang S, Berendschot TT, Zhou J. ELFPIE: an error -laxity Fourier ptychographic iterative engine. Signal Processing 2023, 210: 109088

    2023

  31. [39]

    Deep-ultraviolet Fourier ptychography (DUV-FP) for label- free biochemical imaging via feature-domain optimization

    Zhao Q, Wang R, Zhang S, Wang T, Song P, Zheng G. Deep-ultraviolet Fourier ptychography (DUV-FP) for label- free biochemical imaging via feature-domain optimization. APL Photonics 2024, 9(9). 16

    2024

  32. [40]

    An improved ptychographical phase retrieval algorithm for diffractive imaging

    Maiden AM, Rodenburg JM. An improved ptychographical phase retrieval algorithm for diffractive imaging. Ultramicroscopy 2009, 109(10): 1256-1262

    2009

  33. [41]

    Further improvements to the ptychographical iterative engine

    Maiden A, Johnson D, Li P. Further improvements to the ptychographical iterative engine. Optica 2017, 4(7): 736-745

    2017

  34. [42]

    Multiplexed coded illumination for Fourier Ptychography with an LED array microscope

    Tian L, Li X, Ramchandran K, Waller L. Multiplexed coded illumination for Fourier Ptychography with an LED array microscope. Biomedical optics express 2014, 5(7): 2376-2389

    2014

  35. [43]

    Iterative least -squares solver for generalized maximum -likelihood ptychography

    Odstrčil M, Menzel A, Guizar -Sicairos M. Iterative least -squares solver for generalized maximum -likelihood ptychography. Optics express 2018, 26(3): 3108-3123

    2018

  36. [44]

    Aperture-scanning Fourier ptychography for 3D refocusing and super-resolution macroscopic imaging

    Dong S, Horstmeyer R, Shiradkar R, Guo K, Ou X, Bian Z , et al. Aperture-scanning Fourier ptychography for 3D refocusing and super-resolution macroscopic imaging. Optics express 2014, 22(11): 13586-13599

    2014

  37. [45]

    Optical ptychography for biomedical imaging: recent progress and future directions

    Wang T, Jiang S, Song P , Wang R, Yang L, Zhang T, et al. Optical ptychography for biomedical imaging: recent progress and future directions. Biomedical Optics Express 2023, 14(2): 489-532

    2023

  38. [46]

    Ptychographic coherent diffractive imaging with orthogonal probe relaxation

    Odstrcil M, Baksh P, Boden S, Card R, Chad J, Frey J , et al. Ptychographic coherent diffractive imaging with orthogonal probe relaxation. Optics express 2016, 24(8): 8360-8369

    2016

  39. [47]

    Atomically resolved edges and defects in lead halide perovskites

    Yuan B, Wang Z, Zhang S, Hofer C, Gao C, Chennit T, et al. Atomically resolved edges and defects in lead halide perovskites. Nature 2025

    2025

  40. [48]

    Ptychography

    Rodenburg J, Maiden A. Ptychography. Springer Handbook of Microscopy. Springer, 2019, pp 819-904

    2019

  41. [49]

    Ptychography: A solution to the phase problem

    Guizar-Sicairos M, Thibault P. Ptychography: A solution to the phase problem. Physics Today 2021, 74(9): 42- 48

    2021

  42. [50]

    200 mm optical synthetic aperture imaging over 120 meters distance via macroscopic Fourier ptychography

    Zhang Q, Lu Y , Guo Y , Shang Y , Pu M, Fan Y, et al. 200 mm optical synthetic aperture imaging over 120 meters distance via macroscopic Fourier ptychography. Optics Express 2024, 32(25): 44252-44264

    2024

  43. [51]

    A phase retrieval algorithm for shifting illumination

    Rodenburg JM, Faulkner HM. A phase retrieval algorithm for shifting illumination. Applied physics letters 2004, 85(20): 4795-4797

    2004

  44. [52]

    Incoherent Fourier ptychographic photography using structured light

    Dong S, Nanda P, Guo K, Liao J, Zheng G. Incoherent Fourier ptychographic photography using structured light. Photonics Research 2015, 3(1): 19-23

    2015

  45. [53]

    Near-field Fourier ptychography: super-resolution phase retrieval via speckle illumination

    Zhang H, Jiang S, Liao J, Deng J, Liu J, Zhang Y, et al. Near-field Fourier ptychography: super-resolution phase retrieval via speckle illumination. Optics express 2019, 27(5): 7498-7512

    2019

  46. [54]

    Ptycho-endoscopy on a lensless ultrathin fiber bundle tip

    Song P, Wang R, Loetgering L, Liu J, V ouras P, Lee Y, et al. Ptycho-endoscopy on a lensless ultrathin fiber bundle tip. Light: Science & Applications 2024, 13(1): 168

    2024

  47. [55]

    Ptychographic lensless coherent endomicroscopy through a flexible fiber bundle

    Weinberg G, Kang M, Choi W, Choi W, Katz O. Ptychographic lensless coherent endomicroscopy through a flexible fiber bundle. Optics Express 2024, 32(12): 20421-20431

    2024

  48. [56]

    Deep-ultraviolet ptychographic pocket-scope (DART): mesoscale lensless molecular imaging with label-free spectroscopic contrast

    Wang R, Zhao Q, Quinn J, Yang L, Zhu Y , Huang F, et al. Deep-ultraviolet ptychographic pocket-scope (DART): mesoscale lensless molecular imaging with label-free spectroscopic contrast. eLight 2025, 5

    2025

  49. [57]

    Super-resolved multispectral lensless microscopy via angle-tilted, wavelength-multiplexed ptychographic modulation

    Song P, Wang R, Zhu J, Wang T, Bian Z, Zhang Z , et al. Super-resolved multispectral lensless microscopy via angle-tilted, wavelength-multiplexed ptychographic modulation. Optics Letters 2020, 45(13): 3486-3489

    2020

  50. [58]

    Diffraction tomography with Fourier ptychography

    Horstmeyer R, Chung J, Ou X, Zheng G, Yang C. Diffraction tomography with Fourier ptychography. Optica 2016, 3(8): 827-835

    2016

  51. [59]

    Wide -field high-resolution 3D microscopy with Fourier ptychographic diffraction tomography

    Zuo C, Sun J, Li J, Asundi A, Chen Q. Wide -field high-resolution 3D microscopy with Fourier ptychographic diffraction tomography. Optics and Lasers in Engineering 2020, 128: 106003

    2020

  52. [60]

    Optical measurement of cycle -dependent cell growth

    Mir M, Wang Z, Shen Z, Bednarz M, Bashir R, Golding I , et al. Optical measurement of cycle -dependent cell growth. Proceedings of the National Academy of Sciences 2011, 108(32): 13124-13129

    2011

Pith tools

Reviewed August 3, 2026 · model on record in the stance chip above.