Large-scale compressive microscopy via diffractive multiplexing across a sensor array

Chaoying Gu; Kevin C. Zhou; Laura Waller; Muneki Ikeda; Nicholas Antipa; Rajesh Menon; Roarke Horstmeyer; Saul Kato; Tina M. Hayward

REVIEW 3 major objections 7 minor 33 references

Large-scale compressive microscopy via diffractive multiplexing across a sensor array

T0 review · 3 major / 7 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read A microscope built from 48 sensors and one diffractive optical element can image a field larger than 5 cm² at about 3-µm resolution and 120 frames per second, computationally filling the gaps between the sensors.

desk verdict Real 48-sensor microscope with diffractive gap-filling; engineering credible, but the 25.2 GP/s throughput is a reconstruction ratio, not a measured pixel rate. read the letter →

arxiv 2507.14437 v1 pith:T47UZF5D submitted 2025-07-19 physics.optics eess.IV

Kevin C. Zhou , Chaoying Gu , Muneki Ikeda , Tina M. Hayward , Nicholas Antipa , Rajesh Menon , Roarke Horstmeyer , Saul Kato

show 1 more author

Laura Waller

This is my paper · ORCID

classification physics.opticseess.IV

keywords compressivemicroscopydiffractiveopticalelementsensorarraypointspreadfunctionengineeringspace-bandwidthproductC.elegansimagingcomputationalhigh-throughput

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 proposes a microscope that breaks the usual trade-off among resolution, field of view, and frame rate by combining a 48-sensor array with a custom diffractive optical element. The array is treated as one large super-sensor with gaps between individual cameras, and the DOE spreads each object point into 15 shifted copies so light landing in the gaps is still detected by at least one sensor. A sparsity-promoting reconstruction algorithm then recovers the full contiguous image, including areas beyond the outer sensors. The authors report about 3-µm resolution over more than 5.2 cm² at up to 120 frames per second, for a total throughput of 25.2 billion pixels per second, and demonstrate both darkfield structural imaging and fluorescence calcium imaging of dozens of freely moving C. elegans.

What carries the argument

The load-bearing component is a 15-impulse, asymmetric point spread function generated by a 64-level diffractive optical element placed in the pupil of a nearly 4f system. The PSF is designed to satisfy five criteria—pan-visibility over the sensor array, minimal translational ambiguity evaluated via a normalized triple correlation with the sensor mask, minimal radial extent to limit chromatic blur, sparsity to limit read noise, and robustness to magnification errors—and it spreads the image across 15 shifted copies that cover the inter-sensor gaps. The forward model is a masked convolution, approximated as direct superposition of shifted object copies with a separable radial-distortion model to account for shift variance; reconstruction is performed by non-stochastic gradient descent on a loss combining measurement MSE with isotropic total-variation and L1 penalties.

What would settle it

Image a dense, non-sparse sample, such as a full-field USAF target without darkfield background subtraction, across the entire FOV and compare the reconstruction with a low-magnification reference; the paper's own simulation predicts reconstruction error grows with object density, so if the dense target cannot be resolved at the claimed 2.76-µm pitch while sparse targets can, the sparsity assumption is what enables the headline numbers.

Watch

Extended reading notes

Core claim

The central claim is that spatial multiplexing across a discontiguous sensor array can increase the spatiotemporal throughput of a microscope beyond what the active sensor area alone allows, without sacrificing resolution or frame rate. Specifically, the paper claims that a 48-sensor, 0.617-gigapixel array coupled with a fabricated 64-level DOE in the Fourier plane achieves about 2.76-µm full-pitch resolution across a 20.0 by 26.4 mm² darkfield FOV and about 4.38-µm resolution across a 9.3 by 9.3 mm² fluorescence FOV, at 120 fps, giving a maximal imaging throughput of approximately 25.2 gigapixels per second. The recovery treats the forward model as a masked, shift-variant convolution and solves an underdetermined inverse problem with total-variation and L1 regularization under a non-negativity constraint, relying on the sample being sparse in some domain. The authors demonstrate that the method can track dozens of freely moving worms over 15-second videos and extract pharyngeal calcium transients from the population.

Load-bearing premise

The recovery algorithm assumes the object is sparse in some domain, and without that assumption the underdetermined reconstructed problem has no unique solution, so the claimed throughput and resolution would not hold for dense scenes.

Editorial extensions

If this is right

If the throughput claim holds, a single snapshot can capture a contiguous video frame over an area roughly five times the active sensor footprint, eliminating the need for scanning or stitching in high-speed population imaging.
The method makes darkfield structural imaging and fluorescence functional imaging of many freely moving organisms practical simultaneously, so behavior and physiology can be followed across a large arena without tracking hardware.
The design is calibration-free in the practical sense that no physical PSF calibration is needed: the DOE is computed from its design criteria and the distortion parameters are co-optimized from the data, reducing experimental overhead.
The demonstrated throughput of about 25.2 gigapixels per second, if correct, would put the system among the fastest microscopes reported, opening applications in rare-cell diagnostics and semiconductor inspection where large-area, high-speed imaging matters.

Reading between the lines

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

The same DOE-based multiplexing should scale to larger sensor arrays: because the PSF already spans the largest inter-sensor gap, adding more sensors at the same pitch would compound the gap-recovery factor and push throughput beyond 25.2 GP/s without redesigning the optics.
If the fixed DOE were replaced by a programmable phase modulator, the PSF could be switched between configurations optimized for darkfield and fluorescence, or adapted to scene density in real time, a capability the fabricated element does not offer.
The reported throughput counts reconstructed pixels, so a fair comparison with other gigapixel imaging systems would count resolved spatial degrees of freedom per second, especially in dense scenes where the sparsity constraint limits the number of independent recoverable points.

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

The paper is a real engineering advance: a 48-sensor array treated as a single super-sensor, with a designed 15-point DOE in the pupil that makes the forward model a masked convolution and lets them solve for image content in the gaps. They built it, characterized it, and demonstrate it on moving C. elegans in both darkfield and fluorescence. The novelty over their own prior multi-camera work is the diffractive gap filling rather than overlapping FOVs; that's a genuine architectural change.

The best parts are the depth of the supplementary: the PSF design criteria, the triple-correlation coherence measure, the scale-robustness search, the shift-variant distortion model with separable objective/tube-lens radial terms, and the direct convolution reconstruction. The resolution characterization across the array with USAF targets is careful, and the field curvature/DOF plots are thorough. The reconstruction is non-circular: they compare against reference images and USAF targets.

The soft spots are in the headline claims, not in the hardware. The 25.2 GP/s number is the product of the reconstruction grid size (210 MP per frame) times 120 fps. The >5.4x factor is the ratio of reconstructed pixels to measured pixels after binning; it is the underdetermination ratio of the forward model, not a directly measured information throughput. That is fine for a compressive system, but it inherits the sparsity assumption, which the paper discloses but never quantifies. Supplementary Fig. S5 shows MSE rising with object density, but it is a simulation on random dots, and all experimental demonstrations are extremely sparse (background-subtracted bright worms, or only fluorescent pharynges). So the '~3 um over >5.2 cm^2 at 120 fps' claim should be qualified as 'for sparse scenes in darkfield mode.' The fluorescence mode only achieves 4.38 um over 9.3x9.3 mm, giving 2.17 GP/s, which is far from the headline.

Also, 'calibration-free' is loose: in Sec. 5.3 they jointly estimate distortion coefficients and DOE orientation from a single frame before reconstructing. That is a per-setup calibration. It is not a major flaw, but the abstract should say 'no separate calibration step' rather than 'calibration-free.'

Data and code are not available; raw data 'upon request,' code 'will be made available.' For a paper whose contribution is in large part empirical, that is a real limitation for verification.

None of this is fatal. The forward model is well posed, the PSF design criteria are sensible, and the demonstrations are convincing within their stated regime. The paper deserves a serious referee. The authors should be pushed to either provide a sparsity/density bound, or explicitly restrict the throughput claims to the demonstrated regime, and to make at least the reconstruction code and a representative dataset public. I would expect a revised version to be a strong contribution to computational microscopy.

Referee Report

3 major / 7 minor

Summary. This paper describes a computational microscope built from a 6x8 array of 48 CMOS sensors, treated as a single gapped "super-sensor," with a custom 64-level diffractive optical element (DOE) in the pupil plane of a nearly 4f system. The DOE produces a 15-spot PSF whose spatial extent exceeds the inter-sensor gaps and the sensor hull, so that the masked-convolution forward model of Eq. (2) becomes an underdetermined inverse problem; the authors reconstruct the full sample image with TV- and L1-regularized gradient descent under a shift-variant distortion model (Eq. (7)). In darkfield mode the system reaches ~2.76 um full-pitch USAF resolution across a reconstructed ~20.0x26.4 mm2 sample-plane FOV at 120 fps (with 4x4 binning), corresponding to 210 MP per frame and a claimed throughput of 25.2 GP/s; fluorescence mode reaches ~4.38 um over a 9.3x9.3 mm2 FOV at ~2.17 GP/s. Demonstrations include static lithography targets, FOV extrapolation beyond the sensor array hull, 15-s darkfield videos of dozens of freely moving C. elegans, and functional GCaMP8f calcium imaging with pharyngeal pumping traces extracted from 12 tracked worms. The paper also provides extensive system characterization: per-sensor resolution, field curvature, and depth-of-field maps, a three-way forward-model comparison, and a wavelength-bandwidth analysis of spectral blur.

Significance. If the sparsity-conditioned claims are accepted, this is a substantial engineering advance: it is one of the largest demonstrated combinations of sensor-array hardware and compressive multiplexing for snapshot wide-FOV microscopy, and the functional imaging of dozens of freely moving C. elegans at 120 fps is a compelling showcase. The paper's strengths are its grounding in real hardware and real data: a fabricated and characterized DOE (mean height deviation ~20 nm, Section 5.2), per-sensor resolution and DOF characterization across the array (Figs. S7-S11), an explicit PSF coordinate table (Table S1), a quantitative comparison of shift-invariant versus shift-variant reconstruction (Fig. S4), and an honest disclosure of density-dependent reconstruction error (Fig. S5). The central experimental result is not circular: reconstructions are checked against reference microscope images and USAF targets. The main caveat is that the inverse problem is underdetermined and the headline throughput counts reconstructed rather than measured pixels, so the quoted numbers are only meaningful inside the sparse-object regime that the paper discloses but does not quantify.

major comments (3)

[Abstract; Section 2.1] The headline numbers "25.2 billion pixels per second" and "additional >5.4x" count reconstructed pixels (12621x16655 = 210 MP per frame, Section 3.1) against the ~38.5 MP per frame actually measured after 4x4 binning; this ratio is the underdetermination factor of the masked convolution in Eq. (2), not an independently measured information gain. Because Section 2.1 concedes that the inverse problem is only well posed for "relatively sparse" samples and Supplementary Fig. S5 shows MSE increasing with object density, the unconditional wording of the abstract overstates the operating envelope. Please qualify these claims explicitly (for example, "reconstructed-pixel throughput in the sparse regime") and state quantitatively the sparsity condition under which the 25.2 GP/s and >5.4x figures hold.
[Supplementary Fig. S5] The density-MSE characterization cannot yet serve as a validity bound because the density axis has no physical units, no simulation noise model, number of trials, or reconstruction settings are given, and the demonstrated C. elegans scenes are never mapped onto the curve, so the reader cannot tell whether the worm experiments fall inside the validated regime. Please express object density in physical units (areal fraction or objects per mm2), state the noise model and regularization settings used for the sweep, report the estimated densities of the darkfield and fluorescence scenes on the same axis, and specify the density threshold at which reconstruction quality (MSE relative to a reference, or a task metric such as tracking accuracy) becomes unacceptable.
[Section 3.1; Supplementary Sec. S2] The resolution claims (2.76 um darkfield, 4.38 um fluorescence) are established with a USAF target, which is itself a near-sparse scene (a small target in an otherwise empty FOV). Given the density dependence shown in Fig. S5, the quoted resolution and its pairing with the claimed throughput are demonstrated only for sparse content. Please either state explicitly that the resolution holds in the demonstrated sparse regime, or provide a resolution characterization on a denser object, for example a resolution target embedded in a complex background or simulated reconstructions of dense scenes at various densities.

minor comments (7)

[Abstract; Section 5.3] The adjective "calibration-free" in the abstract is stronger than what the Methods describe: Section 5.3 jointly optimizes the objective and tube-lens distortion coefficients, distortion centers, DOE orientation, background percentile, and regularization weights from the data. Please clarify that the meaning is "no separate physical PSF calibration," or replace "calibration-free" with a more precise phrase.
[Section 3.1] For the fluorescence mode, only the FOV and total throughput are given; please also report the reconstructed pixel count and pixel pitch at the sample, as is done for darkfield mode, so the two modes can be compared on equal terms.
[Supplementary Fig. S5] The MSE curves appear to be single-realization results; please report the number of random-dot trials and show error bars or interquartile ranges, since single curves are unlikely to be stable at very low densities.
[Data and Code Availability] The raw data are "available upon request" and the code "will be made available on Github"; for a paper making quantitative throughput claims, a permanent archived repository with a DOI at acceptance would substantially strengthen reproducibility.
[References] References [23] and [32] cite the same paper (Lockery et al., "Artificial dirt," J. Neurophysiology 99, 3136-3143 (2008)); please consolidate the duplicate.
[Eq. (7)] The notation in the data-fidelity term conflates a coordinate warp with a function argument: Obj(M(r_p, Delta r_i) (r - Delta r_i)) should be defined explicitly, for example by an operator T_i[Obj] evaluated at r, so that the direct-convolution implementation is transparent to readers.
[Table S1] The PSF coordinates are given in millimeters at the image plane; please state the corresponding sample-plane coordinates at each mode's magnification so that the reader can connect the PSF design to the reported FOVs.

Circularity Check

2 steps flagged · score 6.0 of 10

The advertised >5.4× FOV gain and 25.2 GP/s throughput are the chosen reconstruction-to-measurement pixel ratio by construction, and the system parameters are fit on the same video frame whose reconstruction is displayed; resolution benchmarks remain independently tested.

self definitional [Abstract / Sec. 2.1 / Sec. 3.1 (Eq. 2)]
"Since our method requires demixing overlapped images as part of an underdetermined system (we solve for more pixels than we measure), our samples should be relatively sparse ... a total per-frame pixel count of 12621×16655 = 210 MPs and a total imaging throughput of up to ∼25.2 billion pixels per sec."

Eq. 2 predicts m ≈ 38.5M measured pixels per frame at 4× downsampling (48 sensors × 784 × 1024) from an n = 12621×16655 = 210M-pixel object grid, so n/m ≈ 5.45. The advertised >5.4× FOV gain and 25.2 GP/s throughput are computed directly as n × 120 fps; they count the solver's output pixels, not an independently measured information gain. The paper states it 'solve[s] for more pixels than we measure,' so the factor is the chosen underdetermination ratio of the masked convolution by construction, not a quantity established by the experiments. USAF tests establish resolution for sparse targets, but they do not measure a 5.4× throughput gain for arbitrary scenes.
fitted input called prediction [Sec. 5.3 (Data preprocessing); abstract's 'calibration-free' claim]
"In the second step, we estimated the imaging system distortion and DOE orientation (Sec. 2.3) by jointly optimizing them with the reconstruction of a single frame from the video (or a superposition of multiple frames)."

The forward-model loss (Eq. 7) treats the object Obj(r) and the lens distortion coefficients and DOE orientation as unknowns; Sec. 5.3 says these system parameters are 'jointly optimizing them with the reconstruction of a single frame from the video.' Thus the first displayed frame is not an independent prediction of a calibrated model—it is the object fitted together with the system parameters to that same frame's data. The abstract's 'calibration-free' claim is contradicted by this self-calibration step. Because the parameters are fixed for the remaining frames, the rest of the video is only partially affected; the circularity is limited but real for the initial reconstruction and the 'calibration-free' label.

full rationale

The paper's central experimental validation is largely not circular: reconstructions are compared to reference microscope images (Fig. 2) and USAF targets are used to establish 2.76–4.38 µm resolution across the FOV (Sec. 3.1). However, the advertised >5.4× FOV gain and 25.2 GP/s throughput are not independently measured; they are the ratio of the reconstructed pixel grid (12621×16655 = 210 MP) to the number of measured pixels (~38.5 MP), i.e., the underdetermination factor of Eq. 2, which the paper explicitly says it solves. The sparsity prior makes the inverse problem solvable, but no quantitative sparsity bound is attached, so the throughput gain is a counting of solver outputs, not an information-theoretic measurement. In addition, Sec. 5.3 states that distortion and DOE orientation are jointly optimized with a single frame from the same video that is then shown as a reconstruction, so the first displayed frame is fit rather than predicted; the abstract's 'calibration-free' label is unsupported. These are partial circularities in the headline metric and the initial reconstruction, while the physical optics and benchmarked resolution tests retain independent content.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

No new physical entities are introduced. The free parameters are the distortion coefficients, DOE orientation, background percentile, and regularization weights, all estimated or tuned during data processing. The super-sensor is a conceptual model, not a new entity.

free parameters (6)

Objective radial distortion coefficients a_obj_i = unknown; m up to 2
Fitted jointly with the reconstruction in Sec. 5.3 using Eq. 5; the distortion correction is essential for the shift-variant forward model.
Tube lens radial distortion coefficients a_tube_i = unknown; m up to 2
Fitted in the same joint optimization; affects the PSF positions across the array.
Distortion centers r0_obj, r0_tube = unknown
Optimal centers in Eq. 5 are selected during the same calibration fit.
DOE orientation angle = unknown
Estimated with the distortion parameters in the preprocessing step described in Sec. 5.3.
Background percentile n = nth percentile, exact n not specified
Static background is estimated as the nth percentile across time (Sec. 5.3); this choice affects the darkfield reconstructions.
Regularization weights lambda1 and lambda2 = exact values not stated in main text
TV and L1 weights are selected via the L1 sweep in Supplementary Fig. S5; reconstruction quality depends on them.

assumptions (5)

domain assumption The object is sparse in some representation
Required for unique demixing of the 15 shifted copies from an underdetermined system; the paper states samples should be relatively sparse (Sec. 2.1, Fig. S5).
domain assumption The imaging model is incoherent and linear (masked convolution)
Eq. 2 is exact for fluorescence and approximate for darkfield; the inverse solver assumes this linear model.
domain assumption The fabricated DOE produces the designed 15-point PSF with small error
Fabrication height error is reported as ~20 nm mean (Sec. 5.2), but the reconstruction relies on the designed impulse positions and amplitudes.
domain assumption The separable radial distortion model captures all relevant shift variance
Eqs. 5-6 model radially varying magnification; if astigmatism, coma, or vignetting are not captured, the forward model mispredicts data. The paper notes residual vignetting artifacts in the bottom-left corner (Fig. S4).
domain assumption Nonnegativity plus TV/L1 regularization drives the inverse problem to the true scene
The solver assumes that the regularizers select the correct solution among many underdetermined solutions; compressive sensing theory supports this only under sparsity and incoherence conditions.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Large-scale compressive microscopy via diffractive multiplexing across a sensor array." pith.science (2026). https://pith.science/paper/T47UZF5D

@misc{pith2026250714437,
  author       = {Pith},
  title        = {Pith review of: Large-scale compressive microscopy via diffractive multiplexing across a sensor array},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T47UZF5D}},
  note         = {Machine review of arXiv:2507.14437}
}

read the original abstract

Microscopes face a trade-off between spatial resolution, field-of-view, and frame rate -- improving one of these properties typically requires sacrificing the others, due to the limited spatiotemporal throughput of the sensor. To overcome this, we propose a new microscope that achieves snapshot gigapixel-scale imaging with a sensor array and a diffractive optical element (DOE). We improve the spatiotemporal throughput in two ways. First, we capture data with an array of 48 sensors resulting in 48x more pixels than a single sensor. Second, we use point spread function (PSF) engineering and compressive sensing algorithms to fill in the missing information from the gaps surrounding the individual sensors in the array, further increasing the spatiotemporal throughput of the system by an additional >5.4x. The array of sensors is modeled as a single large-format "super-sensor," with erasures corresponding to the gaps between the individual sensors. The array is placed at the output of a (nearly) 4f imaging system, and we design a DOE for the Fourier plane that generates a distributed PSF that encodes information from the entire super-sensor area, including the gaps. We then computationally recover the large-scale image, assuming the object is sparse in some domain. Our calibration-free microscope can achieve ~3 {\mu}m resolution over >5.2 cm^2 FOVs at up to 120 fps, culminating in a total spatiotemporal throughput of 25.2 billion pixels per second. We demonstrate the versatility of our microscope in two different modes: structural imaging via darkfield contrast and functional fluorescence imaging of calcium dynamics across dozens of freely moving C. elegans simultaneously.

Figures

Figures reproduced from arXiv: 2507.14437 by the authors.

**Figure 2.** Single-frame results using (a-b) darkfield configuration and (d-e) fluorescence [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** High-speed (120 fps) darkfield imaging of wild-type [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: High-speed (120 fps) of C. elegans expressing GCaMP8f in their pharynx. (a) Raw data (with static background subtraction) of a single frame, displayed with each sensor image in its correct relative position. The blue outlines indicate the sensor boundaries. See Video 3…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

33 extracted references · 33 canonical work pages

[1]

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

J. Park, D. J. Brady, G. Zheng,et al., “Review of bio-optical imaging systems with a high space-bandwidth product,” Adv. Photonics3, 044001–044001 (2021)

work page 2021
[2]

Whole slide imaging: technology and applications,

M. G. Hanna, A. Parwani, and S. J. Sirintrapun, “Whole slide imaging: technology and applications,” Adv. Anat. Pathol.27, 251–259 (2020)

work page 2020
[3]

Adaptive scanning optical microscope (asom): A multidisciplinary optical microscope design for large field of view and high resolution imaging,

B. Potsaid, Y. Bellouard, and J. T. Wen, “Adaptive scanning optical microscope (asom): A multidisciplinary optical microscope design for large field of view and high resolution imaging,” Opt. express13, 6504–6518 (2005)

work page 2005
[4]

Random-access wide-field mesoscopy for centimetre-scale imaging of biodynamics with subcellular resolution,

R. Shi, X. Chen, J. Deng,et al., “Random-access wide-field mesoscopy for centimetre-scale imaging of biodynamics with subcellular resolution,” Nat. Photonics18, 721–730 (2024)

work page 2024
[5]

Wide-field, high-resolution Fourier ptychographic microscopy,

G. Zheng, R. Horstmeyer, and C. Yang, “Wide-field, high-resolution Fourier ptychographic microscopy,” Nat. photonics 7, 739–745 (2013)

work page 2013
[6]

Fourier ptychography: current applications and future promises,

P. C. Konda, L. Loetgering, K. C. Zhou,et al., “Fourier ptychography: current applications and future promises,” Opt. express28, 9603–9630 (2020)

work page 2020
[7]

Concept, implementations and applications of Fourier ptychography,

G. Zheng, C. Shen, S. Jiang,et al., “Concept, implementations and applications of Fourier ptychography,” Nat. Rev. Phys.3, 207–223 (2021)

work page 2021
[8]

Single-molecule localization microscopy,

M. Lelek, M. T. Gyparaki, G. Beliu,et al., “Single-molecule localization microscopy,” Nat. reviews methods primers 1, 39 (2021)

work page 2021

Show all 33 references

[9]

Surpassingthelateralresolutionlimitbyafactoroftwousingstructuredilluminationmicroscopy,

M.G.Gustafsson,“Surpassingthelateralresolutionlimitbyafactoroftwousingstructuredilluminationmicroscopy,” J. microscopy198, 82–87 (2000)

2000
[10]

Multiplexed coded illumination for Fourier ptychography with an LED array microscope,

L. Tian, X. Li, K. Ramchandran, and L. Waller, “Multiplexed coded illumination for Fourier ptychography with an LED array microscope,” Biomed. optics express5, 2376–2389 (2014)

2014
[11]

Computational illumination for high-speed in vitro Fourier ptychographic microscopy,

L. Tian, Z. Liu, L.-H. Yeh,et al., “Computational illumination for high-speed in vitro Fourier ptychographic microscopy,” Optica2, 904–911 (2015)

2015
[12]

Single-shot extended field of view imaging using point spread function engineering,

R. Malik and K. Khare, “Single-shot extended field of view imaging using point spread function engineering,” JOSA A 40, 1066–1075 (2023)

2023
[13]

Multi-channel data acquisition using multiplexed imaging with spatial encoding,

R. Horisaki and J. Tanida, “Multi-channel data acquisition using multiplexed imaging with spatial encoding,” Opt. express18, 23041–23053 (2010)

2010
[14]

Design architectures for optically multiplexed imaging,

R. H. Shepard, Y. Rachlin, V. Shah, and T. Shih, “Design architectures for optically multiplexed imaging,” Opt. express23, 31419–31435 (2015)

2015
[15]

Increasing a microscope’s effective field of view via overlapped imaging and machine learning,

X. Yao, V. Pathak, H. Xi,et al., “Increasing a microscope’s effective field of view via overlapped imaging and machine learning,” Opt. Express30, 1745–1761 (2022)

2022
[16]

Neural space–time model for dynamic multi-shot imaging,

R. Cao, N. S. Divekar, J. K. Nuñez,et al., “Neural space–time model for dynamic multi-shot imaging,” Nat. Methods pp. 1–6 (2024)

2024
[17]

Motion-resolved quantitative phase imaging,

M. Kellman, M. Chen, Z. F. Phillips,et al., “Motion-resolved quantitative phase imaging,” Biomed. optics express9, 5456–5466 (2018)

2018
[18]

Video-rateimagingofbiologicaldynamicsatcentimetrescaleandmicrometreresolution,

J.Fan,J.Suo,J.Wu, et al.,“Video-rateimagingofbiologicaldynamicsatcentimetrescaleandmicrometreresolution,” Nat. Photonics13, 809–816 (2019)

2019
[19]

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

K. C. Zhou, M. Harfouche, C. L. Cooke,et al., “Parallelized computational 3D video microscopy of freely moving organisms at multiple gigapixels per second,” Nat. photonics17, 442–450 (2023)

2023
[20]

Imaging across multiple spatial scales with the multi-camera array microscope,

M. Harfouche, K. Kim, K. C. Zhou,et al., “Imaging across multiple spatial scales with the multi-camera array microscope,” Optica10, 471–480 (2023)

2023
[21]

A practical algorithm for the determination of plane from image and diffraction pictures,

R. W. Gerchberg, “A practical algorithm for the determination of plane from image and diffraction pictures,” Optik 35, 237–246 (1972)

1972
[22]

High-content behavioral analysis ofCaenorhabditis elegans in precise spatiotemporal chemical environments,

D. R. Albrecht and C. I. Bargmann, “High-content behavioral analysis ofCaenorhabditis elegans in precise spatiotemporal chemical environments,” Nat. methods8, 599–605 (2011)

2011
[24]

Adapting and optimizing GCaMP8f for use in Caenorhabditis elegans,

J. Liu, E. Bonnard, and M. Scholz, “Adapting and optimizing GCaMP8f for use in Caenorhabditis elegans,” Genetics 228, iyae125 (2024)

2024
[25]

High-throughput imaging of neuronal activity in caenorhabditis elegans,

J. Larsch, D. Ventimiglia, C. I. Bargmann, and D. R. Albrecht, “High-throughput imaging of neuronal activity in caenorhabditis elegans,” Proc. National Acad. Sci. United States Am.110, E4266–E4273 (2013)

2013
[26]

Three-dimensional, single-molecule fluorescence imaging beyondthediffractionlimitbyusingadouble-helixpointspreadfunction,

S. R. P. Pavani, M. A. Thompson, J. S. Biteen,et al., “Three-dimensional, single-molecule fluorescence imaging beyondthediffractionlimitbyusingadouble-helixpointspreadfunction,” Proc.NationalAcad.Sci. 106, 2995–2999 (2009)

2009
[27]

Fourier diffuserscope: single-shot 3D Fourier light field microscopy with a diffuser,

F. Linda Liu, G. Kuo, N. Antipa,et al., “Fourier diffuserscope: single-shot 3D Fourier light field microscopy with a diffuser,” Opt. Express28, 28969–28986 (2020)

2020
[28]

Fast multicolor 3D imaging using aberration-corrected multifocus microscopy,

S. Abrahamsson, J. Chen, B. Hajj,et al., “Fast multicolor 3D imaging using aberration-corrected multifocus microscopy,” Nat. methods10, 60–63 (2013)

2013
[29]

DiffuserCam: lensless single-exposure 3D imaging,

N. Antipa, G. Kuo, R. Heckel,et al., “DiffuserCam: lensless single-exposure 3D imaging,” Optica5, 1–9 (2017)

2017
[30]

Multi-sensor lensless imaging: Synthetic large-format sensing with a disjoint sensor array,

E. Zhao, N. Deshler, K. Monakhova, and L. Waller, “Multi-sensor lensless imaging: Synthetic large-format sensing with a disjoint sensor array,” inComputational optical sensing and imaging, (Optica Publishing Group, 2020), pp. CF2C–6

2020
[31]

The genetics ofCaenorhabditis elegans,

S. Brenner, “The genetics ofCaenorhabditis elegans,” Genetics77, 71–94 (1974)

1974
[32]

Artificial dirt: microfluidic substrates for nematode neurobiology and behavior,

S. R. Lockery, K. J. Lawton, J. C. Doll,et al., “Artificial dirt: microfluidic substrates for nematode neurobiology and behavior,” J. neurophysiology99, 3136–3143 (2008)

2008
[33]

Enhanced caenorhabditis elegans locomotion in a structured microfluidic environment,

S. Park, H. Hwang, S.-W. Nam,et al., “Enhanced caenorhabditis elegans locomotion in a structured microfluidic environment,” PloS one3, e2550 (2008)

2008
[34]

Spectral focal sweep: Extended depth of field from chromatic aberrations,

O. Cossairt and S. Nayar, “Spectral focal sweep: Extended depth of field from chromatic aberrations,” in2010 IEEE International Conference on Computational Photography (ICCP), (IEEE, 2010), pp. 1–8

2010

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

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

J. Park, D. J. Brady, G. Zheng,et al., “Review of bio-optical imaging systems with a high space-bandwidth product,” Adv. Photonics3, 044001–044001 (2021)

work page 2021

[2] [2]

Whole slide imaging: technology and applications,

M. G. Hanna, A. Parwani, and S. J. Sirintrapun, “Whole slide imaging: technology and applications,” Adv. Anat. Pathol.27, 251–259 (2020)

work page 2020

[3] [3]

Adaptive scanning optical microscope (asom): A multidisciplinary optical microscope design for large field of view and high resolution imaging,

B. Potsaid, Y. Bellouard, and J. T. Wen, “Adaptive scanning optical microscope (asom): A multidisciplinary optical microscope design for large field of view and high resolution imaging,” Opt. express13, 6504–6518 (2005)

work page 2005

[4] [4]

Random-access wide-field mesoscopy for centimetre-scale imaging of biodynamics with subcellular resolution,

R. Shi, X. Chen, J. Deng,et al., “Random-access wide-field mesoscopy for centimetre-scale imaging of biodynamics with subcellular resolution,” Nat. Photonics18, 721–730 (2024)

work page 2024

[5] [5]

Wide-field, high-resolution Fourier ptychographic microscopy,

G. Zheng, R. Horstmeyer, and C. Yang, “Wide-field, high-resolution Fourier ptychographic microscopy,” Nat. photonics 7, 739–745 (2013)

work page 2013

[6] [6]

Fourier ptychography: current applications and future promises,

P. C. Konda, L. Loetgering, K. C. Zhou,et al., “Fourier ptychography: current applications and future promises,” Opt. express28, 9603–9630 (2020)

work page 2020

[7] [7]

Concept, implementations and applications of Fourier ptychography,

G. Zheng, C. Shen, S. Jiang,et al., “Concept, implementations and applications of Fourier ptychography,” Nat. Rev. Phys.3, 207–223 (2021)

work page 2021

[8] [8]

Single-molecule localization microscopy,

M. Lelek, M. T. Gyparaki, G. Beliu,et al., “Single-molecule localization microscopy,” Nat. reviews methods primers 1, 39 (2021)

work page 2021