Rapid tracking through strongly scattering media with physics-informed neuromorphic speckle analysis

Edmund Y. Lam; Jingyan Chen; Ni Chen; Rongzhou Chen; Shuo Zhu; Yuqing Cao

arxiv: 2604.25310 · v1 · submitted 2026-04-28 · 💻 cs.CV · eess.IV

Rapid tracking through strongly scattering media with physics-informed neuromorphic speckle analysis

Yuqing Cao , Shuo Zhu , Rongzhou Chen , Jingyan Chen , Ni Chen , Edmund Y. Lam This is my paper

Pith reviewed 2026-05-07 16:57 UTC · model grok-4.3

classification 💻 cs.CV eess.IV

keywords neuromorphic trackingspeckle analysisscattering mediaevent sensingmotion estimationlow-light imagingphysics-informed optimization

0 comments

The pith

A neuromorphic framework tracks fast objects through scattering media at ten times the speed and in ten times less light than frame-based cameras.

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

The paper seeks to solve the speed-versus-signal-quality trade-off that limits frame-based cameras when they track moving objects through strongly scattering media in dim conditions. It introduces computational neuromorphic tracking, which pairs asynchronous event sensing with speckle analysis and casts the aggregation step as a spatiotemporal representation. Temporal and spatial parameters are then optimized together to improve stability. Experiments show the resulting system maintains reliable tracking for motions that are ten times faster and under illumination ten times weaker than conventional methods allow. This would matter if true because it extends practical tracking into regimes previously inaccessible due to motion blur or insufficient photons.

Core claim

The central claim is that formulating neuromorphic speckle aggregation as a spatiotemporal speckle representation and jointly optimizing its temporal and spatial parameters produces robust motion estimation that supports tenfold gains in allowable object speed and tenfold reductions in required illumination compared with fixed-exposure frame cameras.

What carries the argument

Neuromorphic speckle aggregation cast as a spatiotemporal speckle representation whose temporal and spatial parameters are jointly optimized to maximize tracking stability under scattering and low light.

If this is right

Tracking of objects moving ten times faster than previously possible becomes feasible in scattering media.
Reliable operation is achieved under illumination levels ten times lower than those required by conventional systems.
The fixed-exposure trade-off between signal-to-noise ratio and temporal resolution is removed for this class of problems.
An efficient, scalable solution is provided for rapid-motion and low-light scenarios that were previously out of reach.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same joint-optimization approach could be tested on other event-camera tasks that involve dynamic scenes or varying illumination.
Leveraging the physics of speckle patterns in this way may reduce the need for heavy post-processing pipelines in low-light imaging.
Natural extensions include applying the framework to volumetric or multi-object tracking problems not addressed in the reported experiments.

Load-bearing premise

Jointly optimizing the temporal and spatial parameters of neuromorphic speckle aggregation will consistently maximize tracking stability across extreme scattering and low-light conditions without post-hoc tuning or dataset-specific biases.

What would settle it

A controlled experiment on the same fast-moving targets and scattering media in which the neuromorphic method fails to deliver at least a tenfold increase in trackable motion speed or a tenfold decrease in required illumination relative to a standard frame-based camera would falsify the performance claims.

read the original abstract

This work addresses the critical problem of tracking fast-moving objects through strongly scattering media in a low-light environment. Different from existing approaches that use frame-based cameras with fixed exposure times, which trade off signal-to-noise ratio for temporal resolution, we introduce computational neuromorphic tracking (CNT), a physics-informed framework that combines asynchronous event sensing with task-driven speckle analysis for robust motion estimation. We formulate the neuromorphic speckle aggregation as a spatiotemporal speckle representation, jointly optimizing the temporal and spatial parameters to maximize tracking stability under extreme conditions. Extensive experiments demonstrate that our method enables robust motion tracking of 10x faster motion and under 10x dimmer illumination compared to conventional systems. These improvements significantly broaden the operational regime for tracking through scattering media, providing an efficient and scalable solution for demanding scenarios involving rapid motion and low-light conditions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The CNT framework's joint spatiotemporal optimization for event-based speckle tracking is a fresh angle on a practical problem, but the 10x speed and illumination claims lack any visible baselines or validation details to back them up.

read the letter

The paper introduces computational neuromorphic tracking as a way to handle fast motion through scattering media in low light by pairing event cameras with a physics-informed speckle aggregation step. The core move is formulating that aggregation as a spatiotemporal representation and jointly tuning its temporal and spatial parameters to improve stability. That specific pairing of asynchronous sensing with task-driven optimization for this exact tracking task does not appear in the cited prior work, so the combination itself counts as new. It also frames the usual frame-camera tradeoff clearly and points to real uses in biomedical or remote sensing settings where scattering and dim conditions are common. The experiments are described as extensive and the gains as robust, which is the right direction for the problem. The main weakness is that none of the quantitative support shows up in the abstract—no error bars, no dataset sizes, no direct comparisons to standard methods, and no ablation on how the parameter optimization behaves when scattering strength or velocity changes independently. The stress-test point about possible condition-specific tuning lands because the central 10x claims depend on that optimization step generalizing, yet nothing confirms it does. If the full paper only reports results on the same regimes used for tuning, the robustness story weakens. This is aimed at researchers already working on event cameras or imaging through turbid media who want practical tracking improvements. A reader looking for new formulations or application ideas could extract value from the setup, but anyone planning to replicate or extend it will need the full experimental section and preferably code to judge the numbers. I would send it for peer review. The idea is grounded enough in the right literature and addresses a clear gap, so referees can check whether the experiments actually deliver on the claims.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces Computational Neuromorphic Tracking (CNT), a physics-informed framework that combines asynchronous event sensing with task-driven speckle analysis for motion tracking of fast-moving objects through strongly scattering media under low-light conditions. It formulates neuromorphic speckle aggregation as a spatiotemporal representation and jointly optimizes temporal and spatial parameters to maximize tracking stability. The central claim, supported by extensive experiments, is that the method enables robust tracking of 10x faster motion and under 10x dimmer illumination compared to conventional frame-based camera systems.

Significance. If the experimental claims are substantiated with proper baselines and generalization tests, the work would meaningfully extend the operational envelope for real-time tracking in scattering and photon-starved regimes, with potential relevance to biomedical imaging, remote sensing, and low-power vision systems. The neuromorphic-plus-speckle combination is a timely direction that could reduce the SNR-temporal-resolution trade-off inherent in fixed-exposure cameras.

major comments (3)

[Abstract] Abstract: The performance claims of '10x faster motion and under 10x dimmer illumination' are stated without any quantitative baselines, error bars, dataset descriptions, ablation results, or statistical measures. This absence is load-bearing for the central claim, as the abstract supplies only a qualitative experimental summary.
[Method] The neuromorphic speckle aggregation step (described as jointly optimizing temporal and spatial parameters) is presented as the key enabler of robustness, yet no equations, loss function, or optimization procedure are shown. Without these, it is impossible to verify whether the optimization is physics-constrained or merely empirical fitting that may not generalize when scattering strength, velocity, or illumination vary independently of the training regime.
[Experiments] Experimental results: The manuscript reports 'extensive experiments' demonstrating the 10x gains, but supplies no cross-condition validation (e.g., testing parameters optimized on one scattering level at a different level) or comparison against standard event-based or speckle-tracking baselines with matched hardware and illumination. This leaves open the possibility that the reported improvements reflect dataset-specific tuning rather than a general property of the CNT formulation.

minor comments (2)

[Abstract] The acronym CNT is introduced without an explicit expansion on first use in the abstract.
[Method] Notation for the spatiotemporal speckle representation is introduced but not defined with symbols or dimensions, which will hinder reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thoughtful and constructive review of our manuscript. We address each major comment below and describe the revisions we will implement to strengthen the presentation and validation of our claims.

read point-by-point responses

Referee: [Abstract] Abstract: The performance claims of '10x faster motion and under 10x dimmer illumination' are stated without any quantitative baselines, error bars, dataset descriptions, ablation results, or statistical measures. This absence is load-bearing for the central claim, as the abstract supplies only a qualitative experimental summary.

Authors: We agree that the abstract would benefit from quantitative support. In the revised manuscript we will expand the abstract to report specific performance metrics (including the 10x factors with error bars), reference the experimental datasets, and summarize key ablation and statistical results from the main text. revision: yes
Referee: [Method] The neuromorphic speckle aggregation step (described as jointly optimizing temporal and spatial parameters) is presented as the key enabler of robustness, yet no equations, loss function, or optimization procedure are shown. Without these, it is impossible to verify whether the optimization is physics-constrained or merely empirical fitting that may not generalize when scattering strength, velocity, or illumination vary independently of the training regime.

Authors: We acknowledge that the current method section would be clearer with explicit mathematics. We will add the full set of equations defining the spatiotemporal speckle representation, the joint optimization objective (with its physics-informed loss terms), and the optimization algorithm in the revised version. These additions will make the physics constraints explicit and allow readers to assess generalization properties. revision: yes
Referee: [Experiments] Experimental results: The manuscript reports 'extensive experiments' demonstrating the 10x gains, but supplies no cross-condition validation (e.g., testing parameters optimized on one scattering level at a different level) or comparison against standard event-based or speckle-tracking baselines with matched hardware and illumination. This leaves open the possibility that the reported improvements reflect dataset-specific tuning rather than a general property of the CNT formulation.

Authors: We appreciate the referee's emphasis on rigorous validation. In the revised manuscript we will include new cross-condition experiments that apply parameters optimized at one scattering/illumination level to unseen levels. We will also add direct comparisons against standard event-based trackers and speckle-correlation methods under matched hardware and illumination conditions, reporting statistical measures and error bars. revision: yes

Circularity Check

0 steps flagged

No circularity: new formulation with experimental validation, no derivations or self-referential reductions shown

full rationale

The abstract and description present CNT as a physics-informed framework that formulates neuromorphic speckle aggregation as a spatiotemporal representation with joint parameter optimization for stability. No equations, derivations, or predictions are exhibited that reduce by construction to fitted inputs or prior self-citations. Performance claims (10x faster/dimmer tracking) rest on experiments rather than any load-bearing mathematical step that equates outputs to inputs. This is a standard non-circular experimental paper; the optimization is described as part of the method, not as a renamed fit or self-defined result.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

Abstract-only review yields limited visibility into parameters and assumptions; the framework relies on an unstated event-camera noise model and the premise that speckle statistics remain informative under extreme scattering.

free parameters (1)

temporal and spatial optimization parameters
Jointly tuned to maximize tracking stability; specific values or fitting procedure not reported.

axioms (1)

domain assumption Asynchronous event data from neuromorphic sensors preserves sufficient motion information in low-light scattering conditions
Invoked by the choice of sensor and the claim of 10x dimmer operation.

invented entities (1)

Computational neuromorphic tracking (CNT) framework no independent evidence
purpose: Spatiotemporal speckle representation with joint parameter optimization
Newly named method introduced to solve the stated tracking problem.

pith-pipeline@v0.9.0 · 5451 in / 1310 out tokens · 45658 ms · 2026-05-07T16:57:15.756699+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

40 extracted references · 40 canonical work pages

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& Bertolotti, J

Penketh, H. & Bertolotti, J. Imaging through scattering media by exploiting the optical memory effect: A tutorial. Journal of Physics: Photonics 6, (2024)

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Shin, S. C. et al. Removal of hot spot speckle on laser projection screen using both the running screen and the rotating diffuser. Displays 27, 91–96 (2006)

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[1] [1]

J. W. Goodman, Statistical Optics, John Wiley and Sons, 2 ed. (2015)

work page 2015

[2] [2]

J. W. Goodman, Speckle Phenomena in Optics: Theory and applications , SPIE Bellingham, WA, USA, 2 ed. (2020)

work page 2020

[3] [3]

Bertolotti, J. et al. Non-invasive imaging through opaque scattering layers. Nature 491, 232–234 (2012)

work page 2012

[4] [4]

Xun, Z. et al. Reconstruction of the tumor spatial microenvironment along the malignant-boundary-nonmalignant axis. Nature Communications 14, 933 (2023)

work page 2023

[5] [5]

& Tsai, D

Gao, Y., Cao, L. & Tsai, D. P. Single -shot, reference -less computational wavefront sensing for complex optical fields. Light: Science & Applications 15, 174 (2026)

work page 2026

[6] [6]

& Bertolotti, J

Jauregui-Sánchez, Y., Penketh, H. & Bertolotti, J. Tracking moving objects through scattering media via speckle correlations. Nature Communications 13, 5779 (2022)

work page 2022

[7] [7]

Liu, H. et al. Learning-based real-time imaging through dynamic scattering media. Light: Science & Applications 13, 194 (2024)

work page 2024

[8] [8]

& Liu, Y

Shi, Y., Sheng, W., Fu, Y. & Liu, Y. Overlapping speckle correlation algorithm for high-resolution imaging and tracking of objects in unknown scattering media. Nature Communications 14, 7742 (2023)

work page 2023

[9] [9]

(Haojiang) & Yang, C

Cua, M., Zhou, E. (Haojiang) & Yang, C. Imaging moving ta rgets through scattering media. Opt. Express 25, 3935–3945 (2017)

work page 2017

[10] [10]

& Zhang, Y

Ma, K., Wang, X., He, S., Zhang, X. & Zhang, Y. Learning to image and track moving objects through scattering media via speckle difference. Optics and Laser Technology 159, 108925 (2023)

work page 2023

[11] [11]

Wu, J. et al. Dark-EvGS: Event camera as an eye for radiance field in the dark. IEEE Transactions on Image Processing (2026)

work page 2026

[12] [12]

& Lam, E

Cao, Y., Zhu, S., Chen, R., Chen, J. & Lam, E. Y. Low -latency object tracking through scattering media with event camera. (2025)

work page 2025

[13] [13]

& Han, J

Zhu, S., Guo, E., Gu, J., Bai, L. & Han, J. Imaging through unknown scattering media based on physics-informed learning. Photonics Research 9, B210–B219 (2021)

work page 2021

[14] [14]

& Arie, A

Pang, Z. & Arie, A. Coherence synthesis in nonlinear optics. Light: Science & Applications 14, 101 (2025)

work page 2025

[15] [15]

& Zhao, C

Wang, Z., Lu, X., Chen, Z., Cai, Y. & Zhao, C. Topological links and knots of speckled light mediated by coherence singularities. Light: Science & Applications 14, 175 (2025)

work page 2025

[16] [16]

Zhang, Q. et al. Non-invasive estimation of the powder size distribution from a single speckle image. Light: Science & Applications 13, 200 (2024)

work page 2024

[17] [17]

A., Luo, Q

Newman, J. A., Luo, Q. & Webb, K. J. Imaging hidden objects with spatial speckle intensity correlations over object position. Physical Review Letters 116, 073902 (2016)

work page 2016

[18] [18]

& Lam, E

Zhang, P., Liu, H., Ge, Z., Wang, C. & Lam, E. Y. Neuromorphic imaging with joint image deblurring and event denoising. IEEE Transactions on Image Processing 33, 2318–2333 (2024)

work page 2024

[19] [19]

Zhang, Y., Chan, S. H. & Lam, E. Y. Photon -starved snapshot holograph y. APL Photonics 8, (2023)

work page 2023

[20] [20]

& Lam, E

Zhu, S., Ge, Z., Wang, C., Han, J. & Lam, E. Y. Efficient non -line-of-sight tracking with computational neuromorphic imaging. Optics Letters 49, 3584–3587 (2024)

work page 2024

[21] [21]

& Scaramuzza, D

Gehrig, D. & Scaramuzza, D. Low -latency automotive vision with event cameras. Nature 629, 1034–1040 (2024)

work page 2024

[22] [22]

& Lam, E

Zhu, S., Wang, C., Liu, H., Zhang, P. & Lam, E. Y. Computational neuromorphic imaging: Principles and applications. vol. 12857 4–10 (2024)

work page 2024

[23] [23]

Chen, R. et al. Self-calibrated neuromorphic hyperspectral derivative imaging . Optica 13, 587–590 (2026)

work page 2026

[24] [24]

Guo, R. et al. EventLFM: Event camera integrated Fourier light field microscopy for ultrafast 3D imaging. Light: Science & Applications 13, 144 (2024)

work page 2024

[25] [25]

& Lam, E

Zhu, S., Yin, Q., Wang, C., Huang, J. & Lam, E. Y. Ultrafast dynamic defect inspection with computational neuromorphic imaging. Advanced Science 12, e10338 (2025)

work page 2025

[26] [26]

Wang, C. et al. Angle-based neuromorphic wave normal sensing. Laser & Photonics Reviews 19, 2400647 (2025)

work page 2025

[27] [27]

Ge, Z., Zhang, P., Gao, Y., So, H. K. -H. & Lam, E. Y. Lens -free motion analysis via neuromorphic laser speckle imaging. Optics Express 30, 2206–2218 (2022)

work page 2022

[28] [28]

Wang, F. et al. Far-field super -resolution ghost imaging with a deep neural network constraint. Light: Science & Applications 11, 1 (2022)

work page 2022

[29] [29]

Li, W. et al. Turning rough surfaces into non-line-of-sight cameras. Optica 12, 626–634 (2025)

work page 2025

[30] [30]

Zhang, Y. et al. Motility-based label-free detection of parasites in bodily fluids using holographic speckle analysis and deep learning. Light: Science & Applications 7, 108 (2018)

work page 2018

[31] [31]

D., Tang, J., Erdener, S

Postnov, D. D., Tang, J., Erdener, S. E., Kılıç, K. & Boas, D. A. Dynamic light scattering imaging. Science advances 6, eabc4628 (2020)

work page 2020

[32] [32]

Zhang, Y. et al. Long-term mesoscale imaging of 3D int ercellular dynamics across a mammalian organ. Cell 187, 6104–6122 (2024)

work page 2024

[33] [33]

& Situ, G

Lyu, M., Wang, H., Li, G., Zheng, S. & Situ, G. Learning -based lensless imaging through optically thick scattering media. Advanced Photonics 1, 036002 (2019)

work page 2019

[34] [34]

& Situ, G

Zheng, S., Wang, H., Do ng, S., Wang, F. & Situ, G. Incoherent imaging through highly nonstatic and optically thick turbid media based on neural network. Photonics Research 9, B220–B228 (2021)

work page 2021

[35] [35]

Gallego, G. et al. Event-based vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 154–180 (2020)

work page 2020

[36] [36]

Ruiming, C., Dekel, G., Amit, K., L, Y. J. & Laura, W. Noise2Image: Noise -enabled static scene recovery for event cameras. Optica 12, 46–55 (2025)

work page 2025

[37] [37]

Goodman, J. W. Introduction to Fourier Optics. (Roberts; Company publishers, 2017)

work page 2017

[38] [38]

Bracewell, R. N. The Fourier Transform and Its Applications. (McGraw-Hill, 1999)

work page 1999

[39] [39]

& Bertolotti, J

Penketh, H. & Bertolotti, J. Imaging through scattering media by exploiting the optical memory effect: A tutorial. Journal of Physics: Photonics 6, (2024)

work page 2024

[40] [40]

Shin, S. C. et al. Removal of hot spot speckle on laser projection screen using both the running screen and the rotating diffuser. Displays 27, 91–96 (2006)

work page 2006