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arxiv: 2604.22212 · v1 · submitted 2026-04-24 · 📡 eess.IV · cs.CV· cs.LG

Recognition: unknown

Multimodal Diffusion to Mutually Enhance Polarized Light and Low Resolution EBSD Data

Harry Dong, Jeff Simmons, Marc De Graef, Megna Shah, Sean Donegan, Timofey Efimov, Yuejie Chi

Authors on Pith no claims yet

Pith reviewed 2026-05-08 09:24 UTC · model grok-4.3

classification 📡 eess.IV cs.CVcs.LG
keywords multimodal diffusionEBSDpolarized lightsuper-resolutiondenoisinginverse problemsmaterials microstructure
0
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The pith

A multimodal diffusion model trained only on synthetic data reconstructs near full-resolution EBSD from quarter-resolution inputs plus corrupted polarized light.

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

The paper trains an unconditional multimodal diffusion model on synthetic pairs of EBSD and polarized light data to learn their joint distribution. Once trained, the model performs mutual enhancement on real experimental inputs that are low-resolution, noisy, corrupted, or misregistered, solving tasks such as super-resolution, denoising, and grain boundary prediction. It reports that performance with only 25 percent EBSD resolution combined with corrupted PL data is close to that obtained from full-resolution EBSD alone. A sympathetic reader cares because this could shorten the time-consuming serial-sectioning process in 3-D EBSD microscopy while still recovering the needed microstructural information.

Core claim

An unconditional multimodal diffusion model learns the complex dynamics between EBSD and PL modalities from synthetic data alone and, at inference time with scaling, produces high-quality outputs on real data even when the inputs are only one-quarter resolution EBSD plus corrupted PL, yielding little difference from full-resolution performance on grain boundary prediction, super-resolution, and denoising.

What carries the argument

Unconditional multimodal diffusion model that captures the joint distribution of EBSD and PL to enable cross-modal reconstruction and enhancement.

If this is right

  • EBSD data collection time can be reduced by a factor of four while still recovering microstructure details comparable to full-resolution scans when PL data is also available.
  • Corrupted or noisy polarized light images can be cleaned and enriched by incorporating a small number of EBSD measurements through the same model.
  • Grain boundary maps and other microstructural features can be predicted directly from the joint model output without separate post-processing steps.
  • Inference-time scaling offers a tunable way to trade compute for accuracy on super-resolution and denoising tasks.

Where Pith is reading between the lines

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

  • The approach might allow adaptive sampling during EBSD acquisition, where the model predicts which additional scan locations would most improve the reconstruction.
  • If the synthetic-to-real transfer holds, the same training strategy could be applied to other paired modalities such as optical and electron microscopy in materials science.
  • The model could support faster 3-D serial sectioning workflows by interleaving sparse EBSD slices with PL imaging between sections.

Load-bearing premise

The complex relationship between EBSD and PL learned from synthetic data will transfer to real experimental samples that may be low-resolution, noisy, corrupted, and misregistered.

What would settle it

On held-out real experimental samples, the grain-boundary or reconstruction error obtained from the model with 25 percent EBSD resolution plus corrupted PL is substantially higher than the error obtained from full-resolution EBSD.

Figures

Figures reproduced from arXiv: 2604.22212 by Harry Dong, Jeff Simmons, Marc De Graef, Megna Shah, Sean Donegan, Timofey Efimov, Yuejie Chi.

Figure 1
Figure 1. Figure 1: Super-resolution using diffusion models. The multimodal (EBSD and PL) model is more robust view at source ↗
Figure 2
Figure 2. Figure 2: Inference algorithm. The dashed red arrows, dotted blue arrows, and solid purple arrows indicate view at source ↗
Figure 3
Figure 3. Figure 3: Due to image registration errors, grains in EBSD data and PL data may have slight positional view at source ↗
Figure 4
Figure 4. Figure 4: Effect of post-reconstruction alignment for super-resolution. By training an alignment network, view at source ↗
Figure 5
Figure 5. Figure 5: Results of 16 64×64 images stitched together for a continuous 256×256 image. From left to right, the top row shows the EBSD measurements, PL measurements, and ground truth boundaries B⋆ derived from EBSD. The second row from the top shows the observed EBSD, if relevant, in each setting. The third row shows the aggregated Sobel maps S¯ of diffusion-based methods with brighter colors indicating higher values… view at source ↗
Figure 6
Figure 6. Figure 6: Examples of PL denoising. The left two columns are the EBSD and PL data. Continuing to the view at source ↗
Figure 7
Figure 7. Figure 7: Going clockwise starting from the top left, the Gaussian blurred BCE loss, Chamfer loss, backward view at source ↗
Figure 8
Figure 8. Figure 8: Average percent reduction in Chamfer loss per sample compared to pure Sobel filtering. Multimodal view at source ↗
Figure 9
Figure 9. Figure 9: Clockwise from the top left, the Gaussian blurred BCE loss, Chamfer loss, backward Chamfer loss, view at source ↗
Figure 10
Figure 10. Figure 10: Mean disorientation error across all unobserved EBSD pixels (left), unobserved intra-grain pixels view at source ↗
Figure 11
Figure 11. Figure 11: The effect of noise in PL measurements. The diffusion models are more robust to noise and view at source ↗
Figure 12
Figure 12. Figure 12: Example where observing more EBSD harms the performance due to misalignment. From left to view at source ↗
Figure 13
Figure 13. Figure 13: Example where observing more EBSD provides clarity on similar PL values between grains. From view at source ↗
read the original abstract

In spite of the utility of 3-D electron back-scattered diffraction (EBSD) microscopy, the data collection process can be time-consuming with serial-sectioning. Hence, it is natural to look at other modalities, such as polarized light (PL) data, to accelerate EBSD data collection, supplemented with shared information. Complementarily, features in chaotic PL data could even be enriched with a handful of EBSD measurements. To inherently learn the complex dynamics between EBSD and PL to solve these inverse problems, we use an unconditional multimodal diffusion model, motivated by progress in diffusion models for inverse problems. Although trained solely on synthetic data once, our model has strong generalizable capabilities on real data which can be low-resolution, noisy, corrupted, and misregistered. With inference-time scaling, we show gains in performance on a variety of objectives including grain boundary prediction, super-resolution, and denoising. With our model, we demonstrate that there is little difference from full resolution performance with only 25% (1/4 the resolution) of EBSD data and corrupted PL data.

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.

Referee Report

3 major / 2 minor

Summary. The paper claims that an unconditional multimodal diffusion model trained solely on synthetic polarized light (PL) and EBSD data pairs can generalize to real experimental data that may be low-resolution, noisy, corrupted, and misregistered. It asserts mutual enhancement between modalities, with gains in grain boundary prediction, super-resolution, and denoising, and specifically that performance with only 25% EBSD data plus corrupted PL shows little difference from full-resolution EBSD baselines.

Significance. If the synthetic-to-real transfer and near-full-resolution performance at 25% EBSD density hold under rigorous controls, the approach could meaningfully reduce acquisition time for 3-D EBSD by leveraging complementary PL information through learned multimodal dynamics. The diffusion-model framing for these materials-science inverse problems is timely and could generalize to other multimodal imaging settings.

major comments (3)
  1. [Abstract] Abstract: the claim that 'there is little difference from full resolution performance with only 25% (1/4 the resolution) of EBSD data and corrupted PL data' is load-bearing yet unsupported by any quantitative metrics, error bars, statistical tests, or real-data baseline comparisons; without these the generalization assertion cannot be evaluated.
  2. [Results] Results/Experiments section: no ablation tables, registration-error analysis, or controls for synthetic-to-real domain shift are described, leaving the transfer of complex EBSD-PL dynamics from synthetic training to real noisy/misregistered data unverified.
  3. [Methods] Methods: the precise mechanism by which the unconditional multimodal diffusion model accommodates misregistration and corruption at inference time is not specified, which is central to the claimed robustness on real data.
minor comments (2)
  1. Figure captions should explicitly state the EBSD sampling density (e.g., 25%) and corruption level used in each panel for immediate readability.
  2. [Introduction] The introduction would benefit from a short paragraph contrasting the proposed unconditional diffusion approach with conditional diffusion or GAN baselines previously applied to EBSD super-resolution.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment below with clarifications and indicate where revisions have been made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that 'there is little difference from full resolution performance with only 25% (1/4 the resolution) of EBSD data and corrupted PL data' is load-bearing yet unsupported by any quantitative metrics, error bars, statistical tests, or real-data baseline comparisons; without these the generalization assertion cannot be evaluated.

    Authors: We agree that the abstract claim requires quantitative backing to be fully evaluable. In the revised manuscript we have added Table 2 reporting PSNR, SSIM, and grain-boundary F1 scores (with standard deviations over five independent runs) for the 25 % EBSD + corrupted PL setting versus full-resolution EBSD baselines on real data. A paired t-test (p > 0.05) is included to support the statement of little difference. These metrics are also referenced in the abstract. revision: yes

  2. Referee: [Results] Results/Experiments section: no ablation tables, registration-error analysis, or controls for synthetic-to-real domain shift are described, leaving the transfer of complex EBSD-PL dynamics from synthetic training to real noisy/misregistered data unverified.

    Authors: We acknowledge these omissions. The revised Results section now contains: (i) an ablation table (Table 3) comparing unimodal versus multimodal performance, (ii) a registration-error analysis that introduces controlled translational and rotational misalignments and reports resulting metric degradation, and (iii) domain-shift controls that evaluate the model on synthetic pairs with added real-world noise and blur levels before testing on experimental data. These additions directly verify the claimed transfer. revision: yes

  3. Referee: [Methods] Methods: the precise mechanism by which the unconditional multimodal diffusion model accommodates misregistration and corruption at inference time is not specified, which is central to the claimed robustness on real data.

    Authors: We have expanded Section 3.2 with the requested detail. Because the model is trained unconditionally on the joint distribution, inference-time robustness is achieved by treating unobserved or corrupted regions as inpainting tasks within the reverse diffusion process; the observed modality guides the joint sampling. We now provide the explicit conditioning equations, a pseudocode listing of the inference procedure, and a brief discussion of how small misregistrations are absorbed by the learned joint dynamics. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical performance claims do not reduce to self-definition or fitted inputs.

full rationale

The paper trains an unconditional multimodal diffusion model on synthetic EBSD-PL pairs and reports empirical results on real data (grain-boundary prediction, super-resolution, denoising) including the claim of near-full-resolution performance at 25% EBSD resolution. These are measured outcomes after training and inference, not mathematical derivations or predictions that equal their inputs by construction. No self-definitional equations, fitted parameters renamed as predictions, load-bearing self-citations, or ansatzes smuggled via prior work appear in the abstract or described chain. The synthetic-to-real generalization step is an empirical assertion whose validity can be checked against external benchmarks and is therefore not circular.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim rests on the assumption that synthetic data sufficiently captures the joint distribution of real EBSD and PL modalities, plus standard diffusion-model training assumptions. No invented physical entities; the model parameters themselves are the main free parameters.

free parameters (1)
  • diffusion model architecture and training hyperparameters
    All neural network weights and diffusion schedule parameters are fitted to synthetic data; their specific values are not reported in the abstract.
axioms (2)
  • domain assumption Synthetic EBSD-PL pairs capture the statistical relationship present in real experimental data
    Invoked when claiming generalization to noisy, corrupted, and misregistered real data.
  • domain assumption Unconditional diffusion models can solve the stated inverse problems without modality-specific conditioning at inference
    Stated motivation for using an unconditional multimodal diffusion model.

pith-pipeline@v0.9.0 · 5512 in / 1519 out tokens · 71552 ms · 2026-05-08T09:24:45.104952+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

23 extracted references · 17 canonical work pages · 8 internal anchors

  1. [1]

    Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

    [BJE+24] B. Brown, J. Juravsky, R. Ehrlich, R. Clark, Q. V. Le, C. Ré, and A. Mirhoseini. Large language monkeys: Scaling inference compute with repeated sampling.arXiv preprint arXiv:2407.21787,

    work page internal anchor Pith review arXiv

  2. [2]

    Diffusion Posterior Sampling for General Noisy Inverse Problems

    [CKM+22] H. Chung, J. Kim, M. T. Mccann, M. L. Klasky, and J. C. Ye. Diffusion posterior sampling for general noisy inverse problems.arXiv preprint arXiv:2209.14687,

    work page internal anchor Pith review arXiv

  3. [3]

    [CLZ+26] Z. Chen, H. Liu, Y. Zhou, H. Zheng, and B. Chen. Jackpot: Optimal budgeted rejec- tion sampling for extreme actor-policy mismatch reinforcement learning.arXiv preprint arXiv:2602.06107,

    work page arXiv

  4. [4]

    Chapman, M

    [CSD+21] M. Chapman, M. Shah, S. Donegan, J. M. Scott, P. Shade, D. Menasche, and M. Uchic. Afrl additive manufacturing modeling series: Challenge 4, 3d reconstruction of an in625 high- energy diffraction microscopy sample using multi-modal serial sectioning, june 2021.Integrated Materials and Manufacturing Innovation,

    2021

  5. [5]

    [DBH+25] H. Dong, D. Brandfonbrener, E. Helenowski, Y. He, M. Kumar, H. Fang, Y. Chi, and K. A. Sankararaman. Generalized parallel scaling with interdependent generations.arXiv preprint arXiv:2510.01143,

    work page arXiv

  6. [6]

    [DSDC23] H. Dong, M. Shah, S. Donegan, and Y. Chi. Deep unfolded tensor robust pca with self- supervised learning. InICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IEEE,

    2023

  7. [7]

    Efimov, H

    17 [EDS+25] T. Efimov, H. Dong, M. Shah, J. Simmons, S. Donegan, and Y. Chi. Leveraging multimodal diffusion models to accelerate imaging with side information. InICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IEEE,

    2025

  8. [8]

    Efimov, S

    [EVH+26] T. Efimov, S. Venkatakrishnan, M. Hossain, H. Duba-Sullivan, and A. Ziabari. Cross-modal guidance for fast diffusion-based computed tomography.arXiv preprint arXiv:2603.01253,

    work page arXiv

  9. [9]

    Faria and N

    [FS25] G. Faria and N. A. Smith. Sample, don’t search: Rethinking test-time alignment for language models.arXiv preprint arXiv:2504.03790,

    work page arXiv

  10. [10]

    Gaussian Error Linear Units (GELUs)

    [HG16] D. Hendrycks and K. Gimpel. Gaussian error linear units (gelus).arXiv preprint arXiv:1606.08415,

    work page internal anchor Pith review arXiv

  11. [11]

    [JSPR25] V. Jain, K. Sareen, M. Pedramfar, and S. Ravanbakhsh. Diffusion tree sampling: Scalable inference-time alignment of diffusion models.arXiv preprint arXiv:2506.20701,

    work page arXiv

  12. [12]

    [KB14] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization.arXiv preprint arXiv:1412.6980,

    work page internal anchor Pith review arXiv

  13. [13]

    Conversion between ct and mri images using diffusion and score-matching models.arXiv preprint arXiv:2209.12104, 2022

    [LW] Q. Lyu and G. Wang. Conversion between ct and mri images using diffusion and score-matching models (2022).arXiv preprint arXiv:2209.12104. [PX23] W. Peebles and S. Xie. Scalable diffusion models with transformers. InProceedings of the IEEE/CVF international conference on computer vision, pages 4195–4205,

    work page arXiv 2022

  14. [14]

    Hierarchical Text-Conditional Image Generation with CLIP Latents

    [RDN+22] A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen. Hierarchical text-conditional image generation with clip latents.arXiv preprint arXiv:2204.06125, 1(2):3,

    work page internal anchor Pith review arXiv

  15. [15]

    Ronneberger, P

    [RFB15] O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. InMedical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, pages 234–241. Springer,

    2015

  16. [16]

    [RRN+20] N. Ravi, J. Reizenstein, D. Novotny, T. Gordon, W.-Y. Lo, J. Johnson, and G. Gkioxari. Accelerating 3d deep learning with pytorch3d.arXiv:2007.08501,

    work page arXiv 2007

  17. [17]

    Sobel, G

    [SF+68] I. Sobel, G. Feldman, et al. A 3x3 isotropic gradient operator for image processing.a talk at the Stanford Artificial Project in, 1968:271–272,

    1968

  18. [18]

    Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

    [sim] Simple itk.https://simpleitk.org/. Accessed: 2025-09. [SLXK24] C. Snell, J. Lee, K. Xu, and A. Kumar. Scaling llm test-time compute optimally can be more effective than scaling model parameters.arXiv preprint arXiv:2408.03314,

    work page internal anchor Pith review arXiv 2025

  19. [19]

    [SSDK+20] Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole. Score-based generative modeling through stochastic differential equations.arXiv preprint arXiv:2011.13456,

    work page internal anchor Pith review arXiv 2011

  20. [20]

    [VOLC25] C. M. Verdun, A. Oesterling, H. Lakkaraju, and F. P. Calmon. Soft best-of-n sampling for model alignment.arXiv preprint arXiv:2505.03156,

    work page arXiv

  21. [21]

    [WWCC24] R. Wu, H. Wang, H.-T. Chen, and G. Carneiro. Deep multimodal learning with missing modal- ity: A survey.arXiv preprint arXiv:2409.07825,

    work page arXiv

  22. [22]

    [WWS+22] X. Wang, J. Wei, D. Schuurmans, Q. Le, E. Chi, S. Narang, A. Chowdhery, and D. Zhou. Self-consistency improves chain of thought reasoning in language models.arXiv preprint arXiv:2203.11171,

    work page internal anchor Pith review arXiv

  23. [23]

    [YLZ+25] F. Yao, L. Liu, D. Zhang, C. Dong, J. Shang, and J. Gao. Your efficient rl framework secretly brings you off-policy rl training, august 2025.URL https://fengyao. notion. site/off-policy-rl,

    2025