Collaborative On-Sensor Array Cameras

Arka Majumdar; Cheng Zheng; Congli Wang; Felix Heide; Jipeng Sun; Kaixuan Wei; Thomas Eboli; Wolfgang Heidrich; Zhihao Zhou

REVIEW 3 major objections 5 minor 2 references

Collaborative On-Sensor Array Cameras

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A flat camera whose six metalenses are jointly optimized over the full visible spectrum can out-image prior metalens arrays and avoid the hallucinated details of generative reconstruction.

desk verdict A genuinely new collaborative metalens-array design with a real prototype and a convincing non-generative reconstruction pipeline; the headline numbers are self-consistent but rest on an unquantified neural proxy, and the '100M nanopost' framing overstates what was optimized. read the letter →

arxiv 2506.04061 v1 pith:2UOSW34E submitted 2025-06-04 physics.optics

Jipeng Sun , Kaixuan Wei , Thomas Eboli , Congli Wang , Cheng Zheng , Zhihao Zhou , Arka Majumdar , Wolfgang Heidrich

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Felix Heide

This is my paper

classification physics.optics

keywords computationalimagingflatopticsmetalensarraybroadbanddifferentiablemetasurfaceoptimizationjointdeconvolutionneuralstructure-to-phaseproxydistributed

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

Metalens cameras use subwavelength pillars to shape light, but a single metalens cannot focus all visible wavelengths at once, so earlier flat cameras are either blurry under broadband light or rely on generative reconstruction that can invent false details. This paper develops a 2×3 array of metalenses that are optimized together, with each sublens learning to capture a complementary slice of the 400–700 nm spectrum and a joint Wiener deconvolution merging the six measurements into one image. To make that optimization possible, the authors introduce a neural proxy that maps nanopost geometry to broadband phase, a distributed training scheme that spreads memory across many GPUs, and a parallax-aware, noise-aware reconstruction without generative priors. If the central claim holds, a thin optical layer on the sensor cover glass can produce color images whose quality approaches conventional lenses, a step toward replacing bulky optics in cameras, robotics, and wearable devices.

What carries the argument

The central mechanism is the joint Wiener filter, a multi-image deconvolution in which the denominators sum the Fourier moduli of all six lenses' broadband point-spread functions, so each sublens's contribution is weighted by its own signal-to-noise ratio at every spatial frequency. That weighting is what converts complementary spectral encoding into a fused sharp image. Around it, the method builds a neural structure-to-phase proxy that predicts broadband phase delay from nanopost duty cycle and wavelength, making the full visible spectrum differentiable; a distributed, fully-sharded training pipeline that spreads wavefield computations over many GPUs so a 100-million-post, 108-wavelength optimization fits in memory; and a reconstruction stage that aligns subimages with global homographies and optical flow, applies the joint Wiener filter patchwise, and finishes with a conditional denoiser guided by an estimated noise variance map.

What would settle it

Measure the point-spread function of a fabricated sublens under narrowband illumination at wavelengths inside and between the proxy's training grid and compare the focal-spot energy and PSF contour with $N_{\mathrm{proxy}}$ predictions; a substantial mismatch would show the experimental sharpness is not produced by the optimized design.

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Extended reading notes

Core claim

The paper's discovery is that wavelength-dependent blur can be turned into a feature: instead of forcing every lens to focus the full spectrum, the authors optimize the array so each of the six sublenses maintains high signal across its own complementary band, and the joint Wiener filter in Eq. (13) recombines them by weighting each lens's Fourier-domain contribution by its signal-to-noise ratio. The resulting broadband point-spread functions stay near-continuously sharp from 400 nm to 700 nm, which prior discrete-wavelength designs do not achieve. In simulation the proposed camera reports SSIM 0.95 and PSNR 34.03 dB under broadband illumination versus 0.80 and 27.34 dB for the diffusion-based baseline, and the fabricated prototype shows sharper raw measurements and reconstructions without hallucinated content, such as restoring '86' rather than '65' from a book cover.

Load-bearing premise

The entire claim rests on the learned neural proxy matching the real nanoposts across the full visible spectrum, because if the simulated point-spread functions differ from the fabricated ones, the experimental sharpness is not explained by the design optimization.

Editorial extensions

If this is right

Flat, on-sensor optics can produce broadband color images without the centimeter-thick lens stacks of conventional cameras, because the array sits on the sensor cover glass at a 3.6 mm working distance.
Because reconstruction is non-generative, recovered images do not invent content the way diffusion-based metalens cameras do; text such as '86' on a book cover is restored correctly rather than rewritten as '65'.
The reconstruction pipeline runs at 35 FPS on laptop-class GPUs, about 2000× faster than the diffusion-based baseline, making real-time video capture feasible.
The same optimization framework can be scaled to industrialized form factors; the authors estimate a 2×2 array in a 5 mm × 5 mm footprint at 3 mm standoff could deliver a 60° field of view.
The array's angular multiplexing supports depth estimation directly from the raw subimage captures, since the six lenses view the scene from slightly different positions.

Reading between the lines

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

Beyond the paper: the same spectral-specialization principle could be applied to other multiplexed imaging tasks—depth from parallax, polarization, or hyperspectral capture—by optimizing arrays whose elements encode different spectral or angular slices and fusing them with a joint Wiener-type estimator.
Beyond the paper: the advantage over single lenses may shrink under narrowband illumination, since a sublens whose assigned band is far from the light's wavelength contributes little signal; a monochromatic-capture test would reveal how much of the gain is spectral diversity versus joint deconvolution.
Beyond the paper: an ablation varying the number of sampled wavelengths while fixing the array architecture would directly test the paper's claim that continuous spectral sampling is what lifts the discrete-wavelength limitation of prior imagers.

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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

Two things to know. First, this is a real advance over prior metalens-array cameras: the six sublenses are jointly optimized to specialize over complementary spectral bands, and the reconstruction is a non-generative, parallax-aware joint Wiener filter, not a diffusion prior. Second, the prototype actually works—raw measurements are visibly sharper than the previous state of the art, and the 35 FPS runtime is a genuine improvement over minute-long diffusion-based recovery. If you work in computational imaging or meta-optics, this paper is worth your time.

What is new: the idea of collaborative spectral specialization—letting each sublens be bad at some bands so long as the group covers the full spectrum—is clearly demonstrated in the PSF plots. The joint Wiener formula (Eq. 12) is the right closed-form solution for the stated multi-frame problem. The forward model (ASM, patch-wise shift-variant convolution, heteroscedastic noise) is standard and sound. The paper also lists its own limitations honestly, which I appreciate.

Where the soft spots are, in proportion. The synthetic benchmarks in Table 1 are generated with the paper's own forward model and reconstructed with PSFs from the same model, so the SSIM 0.95 vs 0.80 numbers are partly self-referential. The relative comparison is fair—they re-optimize/re-simulate the baselines with their own PSFs—but the absolute numbers should not be read as "approaching a 50mm lens": the reference camera is a 3.5mm C-series lens, and the comparison is qualitative.

The load-bearing concern raised by my reader is the neural structure-to-phase proxy Nproxy. It has no reported error metric, no held-out FDTD validation, and it extrapolates from a training range of 80–280 nm to design widths up to 300 nm. If that proxy is off by a fraction of a wave, the optimized phase profiles will not produce the simulated PSFs, and the high synthetic metrics would not transfer to the real device. The prototype's sharp raw measurements are circumstantial evidence that the proxy is adequate, but the paper never compares predicted PSFs to measured ones. That should be fixed.

Also worth flagging: the "100-million nanopost" claim is an overstatement. What is actually optimized is a set of six 1D radial phase profiles; the nanopost distribution is determined from those profiles through the polynomial and the proxy. Calling that "end-to-end jointly optimizing 100 million nanoposts" inflates the optimization scale by orders of magnitude.

Who this is for: anyone designing flat cameras, computational imagers, or metasurface optics. It belongs in a reading group that will push on the proxy validation question.

Recommendation: this paper deserves a serious peer review. Send it to referees—with a request that they pin down the proxy error, the simulated-vs-measured PSF comparison, and a same-scene experimental comparison against the prior array camera. These are addressable, and the core idea is worth protecting.

Referee Report

3 major / 5 minor

Summary. The paper proposes a flat computational camera formed by a 2x3 array of metasurface lenses positioned a few millimeters from the sensor, jointly optimized over the full visible spectrum (400–700 nm). The optimization uses a distributed FSDP framework with 100-million-nanopost parameters, a neural proxy N_proxy mapping duty cycle and wavelength to phase, and a differentiable angular-spectrum PSF model with patch-wise shift-variant convolution. Reconstruction is a parallax-aware pipeline: homography/RAFT alignment, joint Wiener deconvolution across the six sub-apertures, and a noise-aware conditional denoiser. The authors validate with simulated broadband reconstructions (SSIM 0.95, PSNR 34.03 dB), ablations, and a fabricated prototype capturing indoor/outdoor scenes, claiming consistent qualitative superiority over prior metalens array cameras and real-time 35 FPS runtime.

Significance. If the design-to-fabrication link is validated, this is a substantial systems contribution: it is the first demonstration of end-to-end optimization over a 100-million-nanopost array across the full visible spectrum at practical memory cost, and the non-generative reconstruction pipeline avoids the hallucination artifacts of diffusion-based baselines. The forward model (angular spectrum method, patch-wise shift-variant convolution, heteroscedastic noise) is standard, and the joint Wiener estimate in Eq. (12) is a correct closed-form solution for the stated multi-frame problem. The paper also builds an actual fabricated prototype with raw measurements that are visibly sharper than the prior array camera, and it includes a careful ablation of the reconstruction modules. The main risk is that the quantitative superiority claim is generated under the same approximate model used for optimization, with no measured PSFs or quantitative experimental comparison; this risk is fixable but currently load-bearing.

major comments (3)

[§3.1 (Eq. (5)); §6] The load-bearing bridge between the optimized phase profiles and the fabricated PSFs is the neural proxy N_proxy, but the paper provides no validation of this proxy. The FDTD training data are for widths 80–280 nm while the design widths span 100–300 nm, so the interval 280–300 nm (and possibly the low-width edge) is extrapolation; the text does not report the proxy's prediction error on held-out FDTD simulations, nor does it compare predicted PSFs with PSFs measured from the fabricated array. The statement in §6 that the raw measurements "validate ... the accuracy of the proposed neural proxy" is indirect and qualitative. Because the entire optimization in §3.2 and the quantitative results in §5 are computed with PSFs produced by this unvalidated surrogate, a phase error of even a fraction of a wave at some wavelengths would invalidate the link between the optimized design and the claimed experimental performance. Please add a held-out FDTD error analysis for N_proxy (including the 280–300 nm range), an evaluation of predicted versus measured PSFs for the fabricated prototype, and a discussion of the neglected near-field coupling between nanoposts.
[§5 (Table 1; Figs. 8–9)] The quantitative claim that the collaborative array reaches SSIM 0.95 / PSNR 34.03 dB and outperforms prior flat meta-optics is generated entirely through the paper's own differentiable forward model: the proposed design is optimized and then evaluated with the same angular-spectrum PSFs from the same proxy, and the baselines are also simulated under this model. This makes the comparison partly self-referential and likely favorable to the proposed design; the baselines may be disadvantaged if their nominal lens profiles were derived under different models or optimization objectives. The independent evidence is the fabricated prototype in §6, but it is assessed only qualitatively on non-identical indoor/outdoor scenes, with no measured PSFs and no quantitative metric. Please provide an independent validation path: for example, compare reconstructions obtained from measured PSFs of the fabricated array with those predicted by the model, or report quantitative metrics (e.g., SSIM/PSNR against a co-captured reference camera) on a controlled experimental test set.
[§3.2 (Spectrally Agnostic Training); §5.2] The spectrally agnostic training samples only |S_nv|=3 wavelengths per iteration to form the measurement and |S_np|=108 wavelengths for the deconvolution PSFs; the paper asserts that this random sampling generalizes to arbitrary real-world illumination spectra. The only evidence is the qualitative comparison of a single broadband lens versus the array under 10 illuminators (§5.2, Fig. 9), generated with the same forward model. Please provide a quantitative robustness study over a held-out set of illuminants (for example, SSIM/PSNR as a function of spectral tilt or discrete-line spectra) and an ablation over |S_nv|; without this, the Abstract's claim that the camera performs "irrespective of the scene illumination spectrum" is not supported.

minor comments (5)

[§3.2 (Eqs. 15–16)] The notation for the sampled wavelength sets is inconsistent: Eq. (15) uses "P_v" in the summation index while the text defines S_nv, and Eq. (16) uses S_v; please unify the notation.
[Table 1] The metric "1-LPIPS" is not defined; please state explicitly that it is 1 − LPIPS, with higher values meaning better perceptual similarity.
[§3.1] The description "FDTD simulations at 2 nm increments for structure sizes ranging from 80 nm to 280 nm, and at 1 nm increments across the visible spectrum" is ambiguous; please specify the full training grid, including which axis uses 1 nm versus 2 nm increments and the total number of FDTD samples.
[Table 2] The full pipeline in Table 2 reports SSIM 0.67, much lower than the SSIM 0.95 in Table 1; the text should explicitly explain that the two tables use different evaluation settings (shift-variance, parallax, noise, and number of sampled wavelengths) so that readers do not interpret this as a contradiction.
[§5.1] The text says a "radial slice" P_slice[:,:, N_x/2, :] is displayed, but this expression denotes a central row; please clarify whether the visualization is a central row, central column, or true radial average.

Circularity Check

2 steps flagged · score 5.0 of 10

Synthetic quantitative comparisons are closed-loop: the optics and reconstruction are optimized and evaluated with the same differentiable forward model and the same hyperspectral dataset; independent evidence is limited to qualitative prototype results.

fitted input called prediction [Section 5.2 (Synthetic Evaluation, Table 1) vs. Section 3.5 (Meta-Optics Optimization) and Eqs. (13), (16)]
"We utilize the proposed distributed large-scale meta-optics framework in conjunction with a hyperspectral dataset [Jeon et al. 2024] to conduct optics optimization. ... To this end, we simulate measurements using a real-world hyperspectral dataset [Jeon et al. 2024] as input. ... The same joint deconvolution algorithm from Section 3.3 is applied for reconstruction except for the parallax alignment module."

The optical phase profiles are optimized end-to-end with the Section 3.1 differentiable PSF model, using the joint Wiener filter of Eq. (13) and the reconstruction-fidelity loss of Eq. (16). Table 1 then synthesizes test measurements with that same Section 3.1 model, on the same [Jeon et al. 2024] dataset used for the optics optimization, and reconstructs with the same Eq. (13)/Section 3.3 deconvolution. No held-out split or independent PSF validation is reported. The reported SSIM/PSNR therefore measures how well the fitted optics and reconstruction satisfy the training objective; the quantitative claim that the collaborative array outperforms baselines is a consistency check on the fitting model, not an independent prediction.
fitted input called prediction [Section 5.2 (Ablation Experiments, Table 2) vs. Section 3.5 and Section 4.1]
"Given a jointly optimized array design, we train the reconstruction pipeline in a sequential training stage. ... denoising on our proposed hyperspectral parallax dataset introduced in Section 4.1. ... we conduct ablation experiments on the synthetic hyperspectral parallax dataset introduced in Section 4.1."

The noise-aware denoiser D and the full reconstruction pipeline are trained on the Section 4.1 synthetic hyperspectral parallax dataset, whose measurements are generated with the same Section 3.1 PSFs used in the ablations. The ablation experiments in Table 2 are then run on that same Section 4.1 pipeline without any reported train/test separation. Consequently the ablation scores quantify fit to the training generator and to the same forward model, so the claimed necessity of each module is established inside the closed loop rather than by independent generalization.

full rationale

The only genuine circularity concerns are in the synthetic quantitative comparisons: the optics optimization and the Section 5.2 benchmark share the same differentiable forward model and the same [Jeon et al. 2024] data, and the reconstruction ablations are trained and evaluated on the same Section 4.1 synthetic dataset. This makes the headline SSIM/PSNR numbers partly self-referential and prevents them from being treated as external confirmation of the neural proxy. The paper does have independent evidence: a fabricated prototype and real captures compared with a refractive reference and with prior array cameras, though those comparisons are qualitative and scene-matched rather than controlled. No load-bearing self-citation, uniqueness argument, or ansatz-by-citation is present; the use of prior polynomial proxies and wedge-phase baselines is explicit and not identical to the proposed derivation. Separately, the unvalidated 280-300 nm extrapolation of Nproxy is a correctness risk rather than a circularity.

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

The central design relies on the standard Fourier optics model and the proxy chain from phase profile to physical nanopost; the main hand-chosen constants are small in number but the proxy-to-fabrication transfer is the unquantified link.

free parameters (7)

nominal design wavelength lambda0 = 658 nm
Hand-picked as the wavelength at which the structure-to-phase mapping is one-to-one; all other wavelengths are predicted from this nominal phase (Section 3.1).
phase-to-structure polynomial order O = 5
Chosen to fit FDTD phase-to-duty-cycle data; the coefficients are fitted to simulation data (Section 3.1).
joint Wiener regularization beta = 0.001
Manual choice in Eq. (11) for multi-frame deconvolution regularization (Section 3.2).
TV loss weight alpha = 0.001
Manual choice in Eq. (22) balancing MSE and total variation in denoiser training (Section 3.3).
noise model parameters (a,b) = 4e-05, 1e-06
Poissonian-Gaussian noise parameters estimated from experimental raw captures (Section 5.2).
wavefield sampling counts |S_np|, |S_nv| = 108, 3
Hand-chosen numbers of PSF wavelengths and measurement wavelengths per training iteration (Section 3.2).
FOV patch grids = 7x7 and 9x9
Manual discretization of the field of view for shift-variant convolution (Sections 3.1 and 3.5).

assumptions (7)

domain assumption Scalar diffraction (Rayleigh-Sommerfeld or shifted angular spectrum) models the optical field after the metasurface accurately for 400-700nm and angles up to 30 degrees.
Standard Fourier optics; the paper does not compare it to full-wave simulation of the full array (Section 3.1).
domain assumption Each nanopost behaves as an isolated scatterer; the phase response of the array is the sum of individual post phases, with no near-field coupling.
The neural proxy is trained on FDTD simulations of single posts and reused across the array (Section 3.1).
domain assumption The fifth-order polynomial mapping from phase to duty cycle is valid for the fabricated structures.
Fitted to FDTD data and assumed to hold at 100-300nm widths and 1000nm height (Section 3.1).
domain assumption PSFs vary smoothly across the FOV so patch-wise shift-invariant convolution with Hann blending is accurate.
Justified by prior art (Lohmann and Paris 1965) and used in Eq. (3).
ad hoc to paper Random sampling of 3 measurement wavelengths and 108 PSF wavelengths during training generalizes to arbitrary real-world illumination spectra.
The 'spectrally agnostic training' claim rests on this random sampling policy (Section 3.2) without a formal guarantee.
domain assumption Fabricated metasurface dimensions match the simulated design within tolerances that preserve the phase response.
Prototype was fabricated, but no fabrication tolerance analysis is included (Section 4.2).
domain assumption The sensor spectral response function for Sony IMX174 and the spectral uplifting from sRGB images are accurate enough for training.
Taken from prior literature (Jiang et al. 2013; Jakob and Hanika 2019).

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Cite this review

Pith. "Pith review of Collaborative On-Sensor Array Cameras." pith.science (2026). https://pith.science/paper/2UOSW34E

@misc{pith2026250604061,
  author       = {Pith},
  title        = {Pith review of: Collaborative On-Sensor Array Cameras},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2UOSW34E}},
  note         = {Machine review of arXiv:2506.04061}
}

read the original abstract

Modern nanofabrication techniques have enabled us to manipulate the wavefront of light with sub-wavelength-scale structures, offering the potential to replace bulky refractive surfaces in conventional optics with ultrathin metasurfaces. In theory, arrays of nanoposts provide unprecedented control over manipulating the wavefront in terms of phase, polarization, and amplitude at the nanometer resolution. A line of recent work successfully investigates flat computational cameras that replace compound lenses with a single metalens or an array of metasurfaces a few millimeters from the sensor. However, due to the inherent wavelength dependence of metalenses, in practice, these cameras do not match their refractive counterparts in image quality for broadband imaging, and may even suffer from hallucinations when relying on generative reconstruction methods. In this work, we investigate a collaborative array of metasurface elements that are jointly learned to perform broadband imaging. To this end, we learn a nanophotonics array with 100-million nanoposts that is end-to-end jointly optimized over the full visible spectrum--a design task that existing inverse design methods or learning approaches cannot support due to memory and compute limitations. We introduce a distributed meta-optics learning method to tackle this challenge. This allows us to optimize a large parameter array along with a learned meta-atom proxy and a non-generative reconstruction method that is parallax-aware and noise-aware. The proposed camera performs favorably in simulation and in all experimental tests irrespective of the scene illumination spectrum.

Figures

Figures reproduced from arXiv: 2506.04061 by the authors.

**Figure 2.** Collaborative Nanophotonic Imaging. We propose a 2 × 3 collaborative metalens array camera wherein individual metalenses in the array capture a part of the whole visible spectrum (the spectrum under each sublens image here is for illustration). After two-step alignment to compensate for parallax, we reconstruct the full visible spectrum at the resolution of the input sub-images, with a multi-image variant of Wiener … view at source ↗

**Figure 3.** Comparison between proposed neural structure-to-phase proxy and polynomial proxy [2021a]. A proxy is needed to model the broadband response of a metalens given plane wave illumination across visible wavelengths. For a single nano-pillar shown on the left, its induced phase delay is determined by both the incident wavelength (vertical axis) and the duty cycle of the structure (horizontal axis). We plot the ground tru… view at source ↗

Figures from the paper (9 more)

**Figure 4.** Figure 4: Distributed large meta-optics model optimization pipeline with Full Sharded Data Parallel (FSDP) illustration. In the forward model, the incident wavefield is distributed across multiple GPUs. Each GPU has a complete metalens array model and computes the PSF for its as…

**Figure 5.** Figure 5: Illustration of synthetic dataset generation. The scene catalog shows representative examples of our dataset. For each scene in the dataset, we set up a 2 × 3 camera array to capture scene parallax and apply hyperspectral lifting to get images under wavelengths from 4…

**Figure 6.** Figure 6: Data acquisition and experimental comparisons. Our setup uses a 40R/60T plate beamsplitter to capture scenes with two cameras at once: a metalens array camera that receives 60 % of the light passing through the beamsplitter, and a conventional camera (reference camera)…

**Figure 7.** Figure 7: Analysis of PSFs for existing metalens array imagers. We plot here X-Z projection of 3D stacked PSFs spanning 400 nm to 700 nm in 1 nm intervals. This visualization highlights the discretely sharp PSFs produced by [Chakravarthula et al. 2023; Tseng et al. 2021a] under …

**Figure 8.** Figure 8: Synthetic broadband image reconstruction with flat metasurface cameras. Due to computational and memory complexity, existing flat metalens optics [Chakravarthula et al. 2023; Tseng et al. 2021a] have been optimized for discrete wavelength sets. As such, these methods p…

**Figure 9.** Figure 9: Comparison between a single broadband-optimized lens and the proposed collaborative array under challenging illumination spectra. Although a single metalens can be continuously optimized for broadband illumination within our framework (left), a single lens struggles t…

**Figure 10.** Figure 10: Qualitative ablation experiments. We evaluate our reconstruction pipeline by comparing three ablated variants on the hyperspectral parallax-aware metalens array dataset: (i) replacement of the spatial-variant deconvolution with spatial-invariant deconvolution using on…

**Figure 11.** Figure 11: Experimental reconstructions of indoor and outdoor scenes. The broadband-optimized metalens design yields a sharp array of image measurements with small chromatic aberration across diverse illumination conditions. The reconstructed image quality and color fidelity app…

**Figure 12.** Figure 12: Experimental comparison to [Chakravarthula et al. 2023]. For similar scenes and illumination conditions, the proposed collaborative metalens array provides more accurate measurements and image reconstructions than [Chakravarthula et al. 2023]. The image zoom-ins show …

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Works this paper leans on

2 extracted references · 2 canonical work pages

[2015]

Yifan Peng, Qiang Fu, Felix Heide, and Wolfgang Heidrich

Computational imaging using lightweight diffractive-refractive optics.Optics Express23 (2015), 31393–31407. Yifan Peng, Qiang Fu, Felix Heide, and Wolfgang Heidrich. 2016. The Diffractive Achromat: Full Spectrum Computational Imaging with Diffractive Optics.ACM Transactions on Graphics (SIGGRAPH)35, 4 (2016), 1–11. Yifan Peng, Qilin Sun, Xiong Dun, Gordon...

work page 2015
[2016]

Latent Space Imaging

Multicolour localization microscopy by point-spread-function engineering. Nature Photonics10 (2016), 590–594. Zheng Shi, Ilya Chugunov, Mario Bijelic, Geoffroi Côté, Jiwoon Yeom, Qiang Fu, Hadi Amata, Wolfgang Heidrich, and Felix Heide. 2024a. Split-Aperture 2-in-1 Computa- tional Cameras.ACM Transactions on Graphics (TOG)43, 4 (2024), 1–19. Zheng Shi, Xi...

work page Pith review arXiv 2016

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [2015]

Yifan Peng, Qiang Fu, Felix Heide, and Wolfgang Heidrich

Computational imaging using lightweight diffractive-refractive optics.Optics Express23 (2015), 31393–31407. Yifan Peng, Qiang Fu, Felix Heide, and Wolfgang Heidrich. 2016. The Diffractive Achromat: Full Spectrum Computational Imaging with Diffractive Optics.ACM Transactions on Graphics (SIGGRAPH)35, 4 (2016), 1–11. Yifan Peng, Qilin Sun, Xiong Dun, Gordon...

work page 2015

[2] [2016]

Latent Space Imaging

Multicolour localization microscopy by point-spread-function engineering. Nature Photonics10 (2016), 590–594. Zheng Shi, Ilya Chugunov, Mario Bijelic, Geoffroi Côté, Jiwoon Yeom, Qiang Fu, Hadi Amata, Wolfgang Heidrich, and Felix Heide. 2024a. Split-Aperture 2-in-1 Computa- tional Cameras.ACM Transactions on Graphics (TOG)43, 4 (2024), 1–19. Zheng Shi, Xi...

work page Pith review arXiv 2016