Ptychographic modulation engine (PME): a low-cost DIY microscope add-on for coherent super-resolution imaging

Guoan Zheng; He Zhang; Kazunori Hoshino; Pengming Song; Pouria Hoveida; Shaowei Jiang; Zichao Bian

REVIEW 3 major objections 5 minor 49 references

Ptychographic modulation engine (PME): a low-cost DIY microscope add-on for coherent super-resolution imaging

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper reports that a $5 vibrating-diffuser add-on can quadruple the resolution of an ordinary low-NA microscope, recover quantitative phase, and refocus images after capture.

desk verdict A useful low-cost diffuser-modulation microscope add-on with a genuine simplification (unknown shift recovery), but the central 4x resolution claim needs independent verification and diffuser-motion metrology. read the letter →

arxiv 1908.05761 v2 pith:7X5EO22E submitted 2019-08-13 physics.ins-det eess.IVphysics.optics

Zichao Bian , Shaowei Jiang , Pengming Song , He Zhang , Pouria Hoveida , Kazunori Hoshino , Guoan Zheng This is my paper · ORCID

classification physics.ins-deteess.IVphysics.optics

keywords super-resolutionmicroscopyquantitativephaseimagingptychographyretrievalthindiffuservibration-basedscanninglabel-freebioimagingcomputationalrefocusing

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

The paper reports that a roughly $5, 3D-printed add-on can convert an ordinary low-NA microscope into a super-resolution quantitative phase microscope. The add-on, named the Ptychographic Modulation Engine (PME), puts a thin diffuser between the specimen and the objective and uses two vibration motors to jostle it to random x-y positions, so each captured frame is a differently shifted speckle-modulated image. A ptychographic phase-retrieval loop then jointly recovers the complex object wavefront, the unknown complex diffuser profile, and the unknown positional shifts, eliminating the need for calibrated mechanical scanning. On a 2X, 0.055 NA objective the authors demonstrate a 4-fold resolution gain over the diffraction limit, and on live yeast cells they show that focus can be adjusted numerically after acquisition. If these claims hold, label-free quantitative phase imaging with improved resolution becomes a low-cost, turnkey retrofit for existing microscopes.

What carries the argument

The load-bearing mechanism is the shifted-diffuser product in the forward model: the propagated object wavefront $O(x,y)*h_d(x,y)$ is multiplied pointwise by the shifted diffuser profile $D(x-x_i,y-y_i)$, and the low-NA pupil then low-pass filters the product. Each vibration-induced in-plane shift maps a different high-spatial-frequency portion of the object into the measured spectrum, creating a ptychographic dataset from random motion. The reconstruction procedure uses the ePIE update (the standard ptychographic iterative engine update) for the exit wave, the rPIE update for the object and diffuser, and the cross-correlation of Eq. (6) to estimate the unknown shifts before each update cycle. The design principle that makes a 4-fold gain possible is that the final resolution is set by the smallest features of the diffuser rather than by the objective pupil.

What would settle it

Record the diffuser motion independently while the PME dataset is captured (for example, with a high-speed camera aimed at the flexure or a separate interferometric channel) and test whether the recovered rigid-translation shifts explain the raw frames to the shot-noise level. A cleaner test is to drive the same diffuser with a calibrated piezo stage in known in-plane steps and compare the reconstruction with the vibration-driven result; a large discrepancy would show that additional motion components matter. If the held-out residual between the forward model and measured intensities is well above noise, the central resolution claim is not supported.

Watch

Extended reading notes

Core claim

The central claim is that placing a thin vibrating diffuser in the detection path of a low-NA microscope encodes the high-angle object information that the objective would otherwise discard, and that this information can be decoded by a joint recovery of the object, diffuser, and shifts. The forward model is $I_i(x,y)=|\mathcal{F}^{-1}\{\mathrm{CTF}\cdot\mathcal{F}\{[O(x,y)*h_d(x,y)]\cdot D(x-x_i,y-y_i)\}\}|^2$, with $O$ the complex exit wavefront, $D$ the complex diffuser profile, $h_d$ the free-space propagator over distance $d$, and the objective pupil expressed as the coherent transfer function CTF. The update loop combines an ePIE-style Fourier-domain correction of the exit wave with rPIE-style updates of the object and shifted diffuser, while image cross-correlation recovers the unknown shifts. The authors report a 4-fold resolution gain over the diffraction limit of the 2X, 0.055 NA objective, resolving group 8, element 6 of a USAF target, and state that the remaining resolution limit is the diffuser's feature size rather than the objective NA. They also recover quantitative phase in agreement with a calibrated phase target and refocus live-yeast phase images by digital propagation after acquisition.

Load-bearing premise

The load-bearing premise is that the vibrating diffuser behaves as a single thin, rigid complex mask that only translates in the x-y plane, with one fixed profile across all frames; if rotation, tilt, axial motion, or deformation is present, the model has no free parameter to capture it and the recovered resolution gain would likely be an artifact.

Editorial extensions

If this is right

A low-NA, large-field-of-view objective can reach a resolution normally associated with higher-NA optics, since the stated ceiling is the diffuser feature size.
Quantitative phase and amplitude are recovered together, giving label-free morphology; because the recovered wavefront can be propagated, post-acquisition refocusing replaces mechanical focus adjustment in time-lapse imaging.
No calibrated scanning stage is needed for ptychography with this module: the random vibration shifts are recovered computationally from the speckle images themselves.
The thin-sample requirement of Fourier ptychography is replaced by a thin-diffuser requirement, so thicker specimens such as tissue sections and live cells can be imaged.
At a parts cost below $5 and with 3D-printed hardware, the module is intended as a turnkey retrofit for existing microscope platforms.

Reading between the lines

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

Because the paper ties resolution to the diffuser feature size, a smaller-pitch diffuser (for example, a monolayer of sub-wavelength beads or an engineered random phase mask) should push the gain further, but the paper does not demonstrate this.
The same unknown-shift cross-correlation recovery could be adapted to other speckle-illumination setups that currently rely on calibrated stages, although that transfer is not shown here.
A head-to-head comparison against a conventional objective with the same effective numerical aperture on the same biological sample would cleanly separate genuine synthetic-aperture gain from algorithmic artifacts.
Because refocusing is a digital parameter, the approach could in principle support autofocus and three-dimensional tracking of moving cells in time-lapse experiments; the yeast experiment only hints at this.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: this is a legitimate incremental hardware contribution—a $5 add-on that turns a regular microscope into a diffuser-modulated ptychographic imager with the diffuser shifts treated as unknowns. The experimental claims, especially the 4× resolution gain, are plausible but not demonstrated as rigorously as they need to be.

What is actually new: replacing the precise mechanical scanning of the diffuser in the group’s prior ptychographic structured modulation work with two vibration motors, and recovering the unknown shifts jointly with object and diffuser. That is a genuine simplification. The simulation in Fig. 3 shows the shift refinement works: mean error drops from ~14 pixels to ~0.19 pixels. The forward model is clearly written and the recovery steps are explicit enough to be reimplemented. The demonstrations on USAF target, phase target, fixed tissue, and live yeast are useful, and the digital refocusing on yeast is a nice extra.

The soft spot is the load-bearing assumption that each camera frame sees one fixed rigid in-plane translation of the diffuser (Eqs. 1 and 8). The paper never measures the actual diffuser motion or reports the exposure time. At 75 fps, each frame integrates about 13 ms; if the diffuser moves by many microns during that window, the measured intensity is a time average over a range of shifts, which is not the model in Eq. (1). The flexure design is intended to suppress rotation and axial motion, but no metrology verifies it. The 4× claim rests on a single USAF target image with no independent high-NA ground-truth comparison, no error bars, and no shared code or data. A control experiment with a stationary diffuser or a cross-check against a higher-NA objective would settle this. These concerns are real but not necessarily fatal—the fact that raw speckle is visible suggests the modulation is not completely washed out—but they remain unresolved.

The citation pattern is fine; the authors appropriately draw on their own prior work. This is a short methods report, not a definitive study. It will be of value to labs looking for a cheap super-resolution module for label-free imaging, and it deserves a serious referee. I would recommend major revision to add diffuser-motion metrology, exposure-time details, and a more rigorous resolution calibration before publication.

Referee Report

3 major / 5 minor

Summary. The paper reports a low-cost add-on module (PME) for existing microscopes that aims to achieve super-resolution quantitative phase imaging without precise mechanical scanning. A thin diffuser is placed between the sample and the objective and is vibrated by two motors to produce random in-plane positional shifts. The forward model in Eq. (1) assumes each captured intensity corresponds to a single rigid in-plane translation of a fixed diffuser profile. The proposed phase retrieval procedure jointly recovers the complex object wavefront, the complex diffuser profile, and the unknown positional shifts. The authors validate the shift-recovery procedure with a simulation (reducing mean shift error from ~14 pixels to 0.19 pixels) and demonstrate imaging on a USAF resolution target, a phase target, fixed tissue slides, and in-vitro yeast cells. The central experimental claim is a 4-fold resolution gain over the diffraction limit of a 2X, 0.055 NA objective lens, based on resolving group 8, element 6 of the USAF target.

Significance. If the claims hold, the PME add-on could provide a very inexpensive (<$5) and turnkey route to super-resolution phase imaging on standard microscopes, avoiding precise scanning mechanisms. The simulation gives a machine-checkable validation of the shift-refinement step, and the reconstruction algorithm is specified in enough detail to be reproducible. The paper also demonstrates computational refocusing, which is a useful practical feature for live-cell imaging. However, the central experimental evidence for the 4-fold resolution gain is a single USAF target observation with no independent ground-truth comparison or quantitative resolution metric, and the physical forward model relies on an assumption about the diffuser motion that is not directly verified. These limitations currently prevent the strong resolution claim from being fully supported.

major comments (3)

[Section 3, Eq. (1)] See above.
[Section 4, Fig. 4] This comment concerns the experimental validation of the main resolution claim.
[Section 3, Eqs. (6)-(15)] This comment concerns the practical reliability of the algorithm.

minor comments (5)

[Introduction] Typographical error.
[Introduction] Spelling of the author name.
[Section 4, Fig. 5] Missing experimental detail.
[Section 3, processing time statement] Incomplete reporting of computational parameters.
[Section 2, diffuser description] Clarification needed for a central design claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reconstruction is self-contained and the resolution claim is benchmarked against external targets and simulations.

full rationale

The paper's derivation chain is self-contained. The forward model (Eq. 1) and the iterative ePIE/rPIE updates (Eqs. 10-17, citing Maiden et al. [47,48]) form an inverse problem whose inputs are the measured intensity images, the assumed propagation model, and the objective CTF. The resolution claim is not a renamed fit: it is evaluated by resolving known features of a USAF target (Fig. 4) and by a simulation with ground-truth positions (Fig. 3). The recovered positional shifts and diffuser profile are jointly estimated unknowns, but the central '4-fold resolution gain' is measured against an external resolution standard, not against a parameter fitted to that standard. The authors do cite their own prior work [19,33,41,46] for the detection-path-modulation concept, but the present result is not derived from those citations; it is demonstrated with a new hardware prototype and independently validated data. Physical assumptions such as rigid in-plane diffuser translation and the absence of time-averaging during the ~13 ms camera exposure are correctness risks, not circularity: no equation or fitted parameter is shown to reduce to its own input. Therefore the paper warrants a circularity score of 0.

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

The paper introduces no new physical entities. Its free parameters are experimental and algorithmic choices. The main load-bearing assumptions are the thin rigid-diffuser translation model and the thin-object exit-wavefront model, both essential to Eqs. (1)-(17). The reliance on the authors' own prior algorithms (ePIE, rPIE, and ptychographic structured modulation) is acknowledged in the references.

free parameters (5)

Algorithm step sizes alpha (ePIE) and beta (rPIE) = Not specified
Tuning constants in Eqs. (12), (14), (15) chosen by hand for convergence; no values or selection criteria are given.
Sample-to-diffuser distance d = ~0.5 mm
Used in the forward model and defocused CTF; chosen for the experiment, affects the propagation kernel and reconstruction.
Refocus propagation distances = 54 µm and 78 µm
Selected post hoc by maximizing image contrast (Fig. 8), not predicted by a model.
Number of images and iterations = 750 images, 3 iterations
Empirically chosen; the paper notes more images improve quality, indicating a data-dependent setting.
Diffuser feature size = ~1 µm microspheres
Design choice; the claimed resolution limit is attributed to this feature size without a quantitative model.

assumptions (5)

domain assumption The diffuser is a thin, rigid complex mask that undergoes pure in-plane translations, describable by a single shift vector per capture.
Eq. (1) and Eq. (8) model the diffuser as D(x - s_x, y - s_y); any rotation, tilt, or deformation invalidates the model.
domain assumption The object is a thin complex transmission function O(x,y) whose exit wavefront is well defined at a single plane.
Eq. (1) models the sample as a complex exit wavefront; thick samples would violate this and break the forward model.
standard math Free-space propagation over distance d is accurately modeled by convolution with a PSF h_d, and the objective lens by a coherent transfer function low-pass filter.
Section 3, Eqs. (1), (7), (10); this is a standard scalar diffraction model, but its accuracy at the stated geometry is assumed.
domain assumption The ptychographic iterative engine (ePIE/rPIE) converges to the true object given sufficient overlap and diversity of illuminations.
The algorithm relies on the validity of ePIE (ref [47]) and rPIE (ref [48]) for this specific modulation geometry; no convergence proof is given for the joint object-diffuser-shift estimation.
domain assumption The diffuser speckle features are resolvable in the captured images, so cross-correlation can provide good initial shift estimates.
Section 5 argues this as a design advantage; if the speckle is undersampled or blurred, the initialization in Eq. (3) and refinement in Eq. (6) may fail.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Ptychographic modulation engine (PME): a low-cost DIY microscope add-on for coherent super-resolution imaging." pith.science (2026). https://pith.science/paper/7X5EO22E

@misc{pith2026190805761,
  author       = {Pith},
  title        = {Pith review of: Ptychographic modulation engine (PME): a low-cost DIY microscope add-on for coherent super-resolution imaging},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7X5EO22E}},
  note         = {Machine review of arXiv:1908.05761}
}

read the original abstract

Imaging of biological cells and tissues often relies on fluorescent labels, which offer high contrast with molecular specificity. The use of exogenous labeling agents, however, may alter the normal physiology of the bio-specimens. Complementary to the established fluorescence microscopy, label-free quantitative phase imaging provides an objective morphological measurement tool for bio-specimens and is free of variability introduced by contrast agents. Here we report a simple and low-cost microscope add-on, termed Ptychographic Modulation Engine (PME), for super-resolution quantitative phase imaging. In this microscope add-on module, we attach a diffuser to a 3D-printed holder that can be mechanically moved to different x-y positions. We then use two vibrational motors to introduce random positional shifts to the diffuser. The add-on module can be placed between the objective lens and the specimen in most existing microscope platforms. Thanks to the diffuser modulation process, the otherwise inaccessible high-resolution object information can now be encoded into the captured images. In the ptychographic phase retrieval process, we jointly recover the complex object wavefront, the complex diffuser profile, and the unknown positional shifts of the diffuser. We demonstrate a 4-fold resolution gain over the diffraction limit of the employed 2X objective lens. We also test our approach for in-vivo cell imaging, where we are able to adjust the focus after the data has been captured. The reported microscope add-on provides a turnkey solution for super-resolution quantitative phase imaging. It may find applications in label-free bio-imaging where both large field-of-view and high resolution are needed.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

49 extracted references · 49 canonical work pages

[1]

Freudiger C W, Min W, Saar B G, Lu S, Holtom G R, He C, Tsai J C, Kang J X and Xie X S 2008 Label - free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy Science 322 1857 - 61

work page 2008
[2]

Wang H - W, Le T T and Cheng J - X 2008 Label - free imaging of arterial cells and extracellular matrix using a multimodal CARS microscope Optics communications 281 1813 - 22

work page 2008
[3]

Le T T, Langohr I M, Locker M J, Sturek M and Cheng J - X 2007 Label - free molecular imaging of atherosclerotic lesions using multimod al nonlinear optical microscopy Journal of biomedical optics 12 054007

work page 2007
[4]

Coskun A F, Cetin A E, Galarreta B C, Alvarez D A, Altug H and Ozcan A 2014 Lensfree optofluidic plasmonic sensor for real - time and label - free monitoring of molecular binding events over a wide field - of - view Scientific reports 4 6789

work page 2014
[5]

McLeod E, Dincer T U, Veli M, Ertas Y N, Nguyen C, Luo W, Greenbaum A, Feizi A and Ozcan A 2015 High - throughput and label - free single nanoparticle sizing based on time - resolved on - chip microscopy ACS nano 9 3265 - 73

work page 2015
[6]

Rivenson Y, Liu T, Wei Z, Zhang Y, de Haan K and Ozcan A 2019 PhaseStain: the digital staining of label - free quantitative phase microscopy images using deep learning Light: Science & Applications 8 23

work page 2019
[7]

Pham H V, Pantanowitz L and Liu Y 2016 Quantitative phase imaging to improve the diagnostic accuracy of urine cytology Cancer cytopathology 124 641 - 50

work page 2016
[8]

Wang P, Bista R, Bhargava R, Brand R E and Liu Y 2010 Spatial - domain low - coherence quantitative phase microscopy for cancer diagnosi s Opt. Lett. 35 2840 - 2

work page 2010

Show all 49 references

[9]

Kim T, Zhou R, Mir M, Babacan S D, Carney P S, Goddard L L and Popescu G 2014 White - light diffraction Journal XX (XXXX) XXXXXX Bian et al 8 tomography of unlabelled live cells Nature Photonics 8 256

2014
[10]

Lu L, Sun J, Zhang J, Fan Y, Chen Q and Zuo C 2019 Quantitative Phase Imaging Camera With a Weak Diffuser Frontiers in Physics 7

2019
[11]

Kasprowicz R, Suman R and O’Toole P 2017 Characterising live cell behaviour: Traditional label - free and quantitative phase imaging approaches The International Journal of Biochemistry & Cell Biology 84 89 - 95

2017
[12]

Marrison J, Räty L, Marriott P and O'toole P 2013 Ptychography – a label free, high - contrast imaging technique for live cells using quantitative phase information Scientific reports 3 2369

2013
[13]

Maiden A M, Rodenburg J M and Humphr y M J 2010 Optical ptychography: a practical implementation with useful resolution Opt. Lett. 35 2585 - 7

2010
[14]

Lee K, Kim K, Jung J, Heo J, Cho S, Lee S, Chang G, Jo Y, Park H and Park Y 2013 Quantitative phase imaging techniques for the study of cell pathop hysiology: from principles to applications Sensors 13 4170 - 91

2013
[15]

Choi W, Fang - Yen C, Badizadegan K, Oh S, Lue N, Dasari R R and Feld M S 2007 Tomographic phase microscopy Nature methods 4 717

2007
[16]

Shin S, Kim K, Yoon J and Park Y 2015 Active illumination using a digital micromirror device for quantitative phase imaging Opt. Lett. 40 5407 - 10

2015
[17]

Mandracchia B, Gennari O, Marchesano V, Paturzo M and Ferraro P 2017 Label free imaging of cell - substrate contacts by holographic total internal reflection micros copy Journal of Biophotonics 10 1163 - 70

2017
[18]

Kastl L, Isbach M, Dirksen D, Schnekenburger J and Kemper B 2017 Quantitative phase imaging for cell culture quality control Cytometry Part A 91 470 - 81

2017
[19]

Zheng G, Horstmeyer R and Yang C 2013 Wide - field, high - resolution Fourier ptychographic microscopy Nature photonics 7 739

2013
[20]

Zhang H, Bian Z, Jiang S, Liu J, Song P and Zheng G 2019 Field - portable quantitative lensless microscopy based on translated speckle illumination and sub - sampled ptychographic phase retrieval Opt. Lett. 44 1976 - 9

2019
[21]

Express 27 7498 - 512

Zhang H, Jiang S, Liao J, Deng J, Liu J, Zhang Y and Zheng G 2019 Near - field Fourier ptychography: super - resolution phase retrieval via speckle illumination Opt. Express 27 7498 - 512

2019
[22]

Park Y, Depeursinge C and Popescu G 2018 Quantitative phase imaging in biomedicine Nature Photonics 12 578

2018
[23]

Express 26 32888 - 99

Chen M, Phillips Z F and Waller L 2018 Quantitative differential phase contrast (DPC) microscopy with computational aberration correction Opt. Express 26 32888 - 99

2018
[24]

Faulkner H and Rodenburg J 2004 Movable aperture lensless transmission microscopy: a novel phase retrieval algorithm Physical review letters 93 023903

2004
[25]

Gerchberg R W 1972 A practical algorithm for the determination of phase from image and diffraction plane pictures Optik 35 237

1972
[26]

Thibault P and Menzel A 2013 Reconstructing state m ixtures from diffraction measurements Nature 494 68 - 71

2013
[27]

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

2012
[28]

Thibault P, Dierolf M, Menzel A, Bunk O, David C and Pfeiffer F 2008 High - Resolution Scanning X - ray Diffraction Microscopy Science 321 379 - 82

2008
[29]

Dierolf M, Menzel A, Thibault P, Schneider P, Kewish C M, Wepf R, Bunk O and Pfeiffer F 2010 Ptychograp hic X - ray computed tomography at the nanoscale Nature 467 436 - 9

2010
[30]

Godard P, Carbone G, Allain M, Mastropietro F, Chen G, Capello L, Diaz A, Metzger T, Stangl J and Chamard V 2011 Three - dimensional high - resolution quantitative microscopy of extended crys tals Nature communications 2 568

2011
[31]

Maiden A, Morrison G, Kaulich B, Gianoncelli A and Rodenburg J 2013 Soft X - ray spectromicroscopy using ptychography with randomly phased illumination Nature communications 4 1669

2013
[32]

Jiang Y, Chen Z, Han Y, Deb P, Gao H, Xie S, Purohit P, Tate M W, Park J and Gruner S M 2018 Electron ptychography of 2D materials to deep sub - ångström resolution Nature 559 343

2018
[33]

Guo K, Dong S and Zheng G 2015 Fourier ptychography for brightfield, phase, darkfield, reflective, multi - sl ice, and fluorescence imaging IEEE Journal of Selected Topics in Quantum Electronics 22 77 - 88

2015
[34]

Song P, Jiang S, Zhang H, Huang X, Zhang Y and Zheng G 2019 Full - field Fourier ptychography (FFP): Spatially varying pupil modeling and its application for r apid field - dependent aberration metrology APL Photonics 4 050802

2019
[35]

Zheng G 2014 Breakthroughs in Photonics 2013: Fourier Ptychographic Imaging IEEE Photonics Journal 6 1 - 7

2014
[36]

Express 23 3472 - 91

Ou X, Horstmeyer R, Zheng G and Yang C 2015 High numerical aperture Fourier ptychography: principle, implementation and characterization Opt. Express 23 3472 - 91

2015
[37]

Express 26 23119 - 3 1

Pan A, Zhang Y, Wen K, Zhou M, Min J, Lei M and Yao B 2018 Subwavelength resolution Fourier ptychography with hemispherical digital condensers Opt. Express 26 23119 - 3 1

2018
[38]

Sun J, Zuo C, Zhang L and Chen Q 2017 Resolution - enhanced Fourier ptychographic microscopy based on high - numerical - aperture illuminations Scientific Reports 7 1187

2017
[39]

Tian L, Liu Z, Yeh L - H, Chen M, Zhong J and Waller L 2015 Computational illumina tion for high - speed in vitro Fourier ptychographic microscopy Optica 2 904 - 11

2015
[40]

Ou X, Zheng G and Yang C 2014 Embedded pupil function recovery for Fourier ptychographic microscopy Optics Express 22 4960 - 72

2014
[41]

Express 22 13586 - 99

Dong S, Horstmeyer R, Shiradkar R, Guo K, Ou X, Bian Z, Xin H and Zheng G 2014 Aperture - scanning Fourier Journal XX (XXXX) XXXXXX Bian et al 9 ptychography for 3D refocusing and super - resolution macroscopic imaging Opt. Express 22 13586 - 99

2014
[42]

Horstmeyer R, Chung J, Ou X, Zheng G and Yang C 2016 Diffraction tomography with Fourier pt ychography Optica 3 827 - 35

2016
[43]

Li P, Batey D J, Edo T B and Rodenburg J M 2015 Separation of three - dimensional scattering effects in tilt - series Fourier ptychography Ultramicroscopy 158 1 - 7

2015
[44]

Choi Y, Yang T D, Fang - Yen C, Kang P, Lee K J, Dasari R R, Feld M S and Choi W 2011 Overcomi ng the diffraction limit using multiple light scattering in a highly disordered medium Physical review letters 107 023902

2011
[45]

Choi Y, Kim M, Yoon C, Yang T D, Lee K J and Choi W 2011 Synthetic aperture microscopy for high resolution imaging through a turb id medium Opt. Lett. 36 4263 - 5

2011
[46]

Song P, Jiang S, Zhang H, Bian Z, Guo C, Hoshino K and Zheng G 2019 Super - resolution microscopy via ptychographic structured modulation of a diffuser Opt. Lett. 44 3645 - 8

2019
[47]

Maiden A M and Rodenburg J M 2009 An improve d ptychographical phase retrieval algorithm for diffractive imaging Ultramicroscopy 109 1256 - 62

2009
[48]

Maiden A, Johnson D and Li P 2017 Further improvements to the ptychographical iterative engine Optica 4 736 - 45

2017
[49]

Voelz D G 2011 Computational fourier optics: a MATLAB tutorial : SPIE press Bellingham, WA)

2011

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

Freudiger C W, Min W, Saar B G, Lu S, Holtom G R, He C, Tsai J C, Kang J X and Xie X S 2008 Label - free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy Science 322 1857 - 61

work page 2008

[2] [2]

Wang H - W, Le T T and Cheng J - X 2008 Label - free imaging of arterial cells and extracellular matrix using a multimodal CARS microscope Optics communications 281 1813 - 22

work page 2008

[3] [3]

Le T T, Langohr I M, Locker M J, Sturek M and Cheng J - X 2007 Label - free molecular imaging of atherosclerotic lesions using multimod al nonlinear optical microscopy Journal of biomedical optics 12 054007

work page 2007

[4] [4]

Coskun A F, Cetin A E, Galarreta B C, Alvarez D A, Altug H and Ozcan A 2014 Lensfree optofluidic plasmonic sensor for real - time and label - free monitoring of molecular binding events over a wide field - of - view Scientific reports 4 6789

work page 2014

[5] [5]

McLeod E, Dincer T U, Veli M, Ertas Y N, Nguyen C, Luo W, Greenbaum A, Feizi A and Ozcan A 2015 High - throughput and label - free single nanoparticle sizing based on time - resolved on - chip microscopy ACS nano 9 3265 - 73

work page 2015

[6] [6]

Rivenson Y, Liu T, Wei Z, Zhang Y, de Haan K and Ozcan A 2019 PhaseStain: the digital staining of label - free quantitative phase microscopy images using deep learning Light: Science & Applications 8 23

work page 2019

[7] [7]

Pham H V, Pantanowitz L and Liu Y 2016 Quantitative phase imaging to improve the diagnostic accuracy of urine cytology Cancer cytopathology 124 641 - 50

work page 2016

[8] [8]

Wang P, Bista R, Bhargava R, Brand R E and Liu Y 2010 Spatial - domain low - coherence quantitative phase microscopy for cancer diagnosi s Opt. Lett. 35 2840 - 2

work page 2010