DynGhost: Temporally-Modelled Transformer for Dynamic Ghost Imagings

arXiv:2605.10185 · cs.CV, cs.AI · Submitted 2026-05-11 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "DynGhost: Temporally-Modelled Transformer for Dynamic Ghost Imagings".

Jane: Ghost imaging reconstructs spatial information from a single-pixel bucket detector by correlating structured illumination patterns with scalar intensity measurements, and this work introduces DynGhost,

Tom: First, who's behind it and why it matters.

Title and authors: Tom: So, looking at the title and who wrote this paper, "DynGhost: Temporally-Modelled Transformer for Dynamic Ghost Imagings," it really tells us that the main innovation lies in combining temporal modeling with a transformer architecture specifically for ghost imaging. Jane Exactly, Tom; it’s not just about making a static reconstruction better; it’s about making sure the AI understands how things move between measurements.

Lu: The authors are coming from different strong backgrounds, which suggests they have a good mix of theoretical understanding of quantum optics and deep learning implementation skills to make this work.

Meng: I've seen papers where the theory is great but the implementation struggles with real-world noise; I wonder if their background helps them bridge that gap between the elegant math and a working system.

Lalam: My internal analysis suggests this paper is highly impactful because it directly addresses a known weakness in AI applications—the inability to handle temporal dynamics—by introducing a specialized architecture for it.

Tom: Precisely, Jane; the title itself flags that temporal modeling is central to their approach, moving beyond simple spatial reconstruction. This paper focuses on how motion coherence can be leveraged within the transformer structure to achieve better results in dynamic settings.

Jane: It sounds like they’ve taken a standard pattern recognition tool and given it a special way of "looking" at time, which is a very smart move for this type of imaging technique.

Lu: The architecture they propose, DynGhost, seems to be the key mechanism that achieves this temporal exploitation through its specific attention blocks.

Meng: If they can keep the computational complexity manageable while achieving better temporal understanding, that moves it from a theoretical curiosity to something we could actually deploy in a lab setting.

The paper's summary: Tom: So, to summarize what the paper says about DynGhost, it’s proposing a transformer architecture that uses alternating spatial and temporal attention blocks to handle dynamic ghost imaging by exploiting motion coherence across frames. Jane That means they are essentially teaching the AI how to use the information from one frame's measurement sequence to better predict the next frame's reconstruction.

Lu: They define a token embedding for each frame and pattern that specifically combines learned projections of illumination, bucket measurements, spatial position, and temporal position into z(t) i = Embed(H i) b(t) i + PE spatial(i) + PE temporal(t).

Meng: That specific token embedding structure is fascinating; it shows they’ve thought carefully about which pieces of information need to be fused at each step of the AI's processing.

Lalam: This attention mechanism, alternating between spatial and temporal modes, is what allows the system to model informative patterns per frame while simultaneously propagating that information across frames to exploit motion coherence.

Tom: And they are using a specific training objective called L = L MSE + 0 point 5L SSIM + 0 point 1L temp, which combines reconstruction accuracy, perceptual quality, and temporal consistency loss to guide the learning process.

Jane: That combination of losses is smart because it doesn't just aim for a perfect pixel-by-pixel match; it also ensures the reconstructed frames look perceptually good and that they actually follow the expected motion path.

Lu: The authors point out that while the temporal attention block has a complexity of O(T two) per pattern, with T=eight this overhead is negligible given their parameters, which is a nice efficiency point.

The paper's improvements: Tom: Beyond just the architecture and loss function, DynGhost introduces several important improvements that address the shortcomings of previous ghost imaging deep learning approaches. Jane They specifically target two major issues: first, treating scenes as purely static, and second, assuming additive Gaussian noise models instead of reflecting real Poissonian statistics from single-photon hardware.

Meng: That second point about the noise model is crucial because if you train a model on Gaussian assumptions when the hardware produces Poisson statistics, you end up with what they term a catastrophic distribution shift.

Lu: To combat that shift, they introduce a quantum-aware training framework that uses physically accurate detector simulations like SNSPDs, SPADs, and SiPMs.

Lalam: This is where things get really interesting; by using those physical simulations and applying Anscombe variance-stabilizing normalization, they managed to resolve that catastrophic distribution shift.

Tom: And the result of that adjustment is a +thirty-three point four percent SSIM gain on real hardware when compared to models trained only with Gaussian assumptions, which is a significant figure for real-world performance.

Jane: That gain really shows how critical it is to accurately model the noise statistics inherent in the physical detectors, rather than just using generic noise assumptions.

Lu: They also benchmark seven photon-count normalization strategies and found that Anscombe and Freeman–Tukey transforms significantly outperform all other methods by making Poisson noise look approximately Gaussian with unit variance.

Conclusion: Tom: So, to wrap up the paper "DynGhost: Temporally-Modelled Transformer for Dynamic Ghost Imagings," the main implication is that we can now build transformer models that effectively handle dynamic ghost imaging by incorporating motion coherence directly into the learning process. Jane It really shows that focusing on temporal consistency and physically accurate noise modeling allows these AI systems to perform much better in real-world, moving scenarios compared to what was possible before.

Lu: The potential here is huge; we are moving toward models that can interpret complex motion sequences with more physical grounding, which opens up avenues for advanced applications in dynamic scene understanding.

Meng: For practical deployment, the finding that this architecture operates significantly faster than iterative solvers like FISTA, running at eight point one milliseconds per frame on average, makes it viable for near real-time video processing tasks.

Lalam: I think the biggest cultural impact is showing how sophisticated AI structures can be tailored to specific physical constraints—like quantum detectors and motion blur—to achieve superior results rather than just chasing high-level accuracy in static benchmarks.

Tom: That’s a fantastic summary of what DynGhost delivers, from the architecture to the hardware robustness. We’ve seen how they tackle both the structural modeling and the noise modeling issues head-on. Jane It's clear this paper provides a solid path forward for developing more robust AI solutions in dynamic imaging problems.

Lu: The future work mentioned suggests exploring other sequence modeling approaches, which could allow for even longer temporal dependencies than what T=eight allows here.

Meng: I’m interested to see how they adapt this transformer structure when we move from simple 2D motion to more complex, multi-modal dynamic scenes.

Lalam: I look forward to seeing how these principles of exploiting temporal coherence can be applied across different domains, not just ghost imaging and quantum detection.

Politecnico di Milano · University of Illinois at Chicago · University of Illinois Urbana-Champaign

cs.CV, cs.AI

Submitted: 2026-05-11

Updated: 2026-10-01

Code: https://github.com/vittpall/MMSP-26-GhostImaging

Importance score: 87/100

The gist: Ghost imaging reconstructs spatial information from a single-pixel bucket detector by correlating structured illumination patterns with scalar intensity measurements, and this work introduces

Key concepts

Dynamic Ghost Imaging
This technique reconstructs an image from a single-pixel detector by correlating structured light patterns with intensity measurements. In dynamic settings, the scene changes over time, requiring methods that can handle motion coherence between successive frames to accurately predict the scene.
Spatial-Temporal Attention
This is a transformer mechanism used in DynGhost that allows the model to simultaneously focus on spatial details within a single frame and propagate information across different frames. It helps the model understand how patterns change over time, which is crucial for reconstructing moving scenes.
Temporal Consistency Loss (Ltemp)
This loss function penalizes inconsistencies between consecutive frame predictions. By forcing the model to ensure that the predicted scene in frame t+1 is physically consistent with the prediction in frame t, it explicitly teaches the network to exploit motion coherence and produce smoother results.
Quantum-Aware Training
This framework addresses inaccuracies caused by using classical noise models on real quantum detectors. It uses physically accurate detector simulations and variance-stabilizing normalization techniques to correct for 'catastrophic distribution shifts,' leading to significantly better performance on actual hardware.

Terminology

Summary

Ghost imaging reconstructs spatial information from a single-pixel bucket detector by correlating structured illumination patterns with scalar intensity measurements, and this work introduces DynGhost, a transformer architecture that addresses limitations in existing deep learning approaches by exploiting temporal coherence and incorporating quantum-aware noise modeling to achieve superior reconstruction quality in dynamic settings.

The gist

DynGhost is the first transformer architecture designed for dynamic ghost imaging that exploits motion coherence via spatial-temporal attention and temporal consistency losses, yielding significantly smoother predictions.

Problem Addressed

Existing deep learning methods for ghost imaging suffer from two critical limitations: they treat scenes as purely static, failing to exploit temporal coherence across frames, which leaves dynamic ghost imaging largely unsolved; second, they assume additive Gaussian noise models that do not reflect the true Poissonian statistics of real single-photon hardware. This leads to a catastrophic distribution shift when trained on realistic quantum detector simulations.

DynGhost Architecture

The proposed architecture is a transformer that addresses the static-scene bottleneck by using alternating spatial and temporal attention blocks. The token embedding for each frame and pattern combines learned projections of the illumination, bucket measurement, spatial position, and temporal position:

z(t)i = Embed(Hi) b(t)i + PEspatial(i) + PEtemporal(t)

The alternating blocks alternate between two modes:

  1. Spatial attention: model informative patterns per frame, with a complexity of O(M2) per frame.

  2. Temporal attention: propagate information across frames to exploit motion coherence, with a complexity of O(T2) per pattern, though this is noted as being negligible for the given parameters (M=188, T=8).

Training Objective and Loss Function

The training objective combines reconstruction fidelity, perceptual quality, and temporal consistency. The total loss function is defined as:

L = LMSE + 0.5LSSIM + 0.1Ltemp

Where the components are:

  1. LMSE (Mean Squared Error) measures reconstruction accuracy: xˆ(t) − x(t) 2

  2. LSSIM (Structural Similarity Index Measure) ensures perceptual quality: 1 − SSIM(ˆx(t), x(t))

  3. Ltemp (Temporal Consistency Loss): Measures the difference between consecutive frame predictions and actual scene motion: (ˆx(t+1) − xˆ(t)) - (x(t+1) − x(t))

Quantum-Aware Training Framework

To resolve the detector-model gap, DynGhost is trained using physically accurate detector simulations (SNSPDs, SPADs, SiPMs) and Anscombe variance-stabilizing normalization. This framework identifies a catastrophic distribution shift of Gaussian-trained models and achieves a +33.4% SSIM gain on real hardware by utilizing correct noise modelling and variance-stabilization. The paper benchmarks seven photon-count normalization strategies, finding that Anscombe and Freeman–Tukey transforms dramatically outperform all alternatives by rendering Poisson noise approximately Gaussian with unit variance.

Experimental Results and Performance

Experiments across three benchmarks—Moving MNIST Ghost Imaging, KViSAR, and various baselines (DGI, U-Net, Ghost-GPT)—demonstrate superior performance. DynGhost achieves a 45% reduction in MSE and a 16% improvement in SSIM over static benchmarks on Moving MNIST. Furthermore, it operates 130× faster than iterative solvers like FISTA, with a per-frame latency of 8.1 ms, falling below standard real-time video thresholds. The model's resilience is confirmed across varied kinematic behaviors; for instance, performance remains stable for linear and oscillatory motions up to 5 px/frame, with high structural coherence maintained even at speeds up to 20 px/frame.

Key Findings Summary

  1. Temporal-aware reconstruction: DynGhost is the first dynamic ghost imaging transformer that exploits motion coherence via spatial-temporal attention and temporal consistency losses.

  2. Quantum detector evaluation and distribution shift mitigation: The framework successfully resolves the distribution shift, showing improvements of +34% for SNSPD and +30% for SPAD reconstructions.

  3. Quantum deployment characterization: A definitive operating regime is defined where quantum models decisively outperform classical alternatives (≥100 photons/measurement, DCR 60%).

  4. Noise Robustness: DynGhost maintains superior MSE and SSIM across a wide range of Input SNRs compared to standard reconstruction algorithms.

Improvements for AI systems

Based on the provided scientific paper, here are specific improvements for existing AI systems and what those improved systems can achieve:

  1. Enhance existing deep learning-based ghost imaging models by integrating a dual-attention mechanism (alternating spatial and temporal attention blocks). This system can achieve:

  2. Significantly better reconstruction quality (e.g., 45% reduction in MSE and 16% improvement in SSIM on Moving MNIST) by explicitly modeling motion coherence across frames as an implicit reconstruction prior, leading to smoother predictions than purely static or frame-independent models.

  3. Develop a quantum-aware training framework that incorporates physically accurate detector simulations (SNSPDs, SPADs, SiPMs) and Anscombe variance-stabilizing normalization during model training. This system can achieve:

  4. A +33.4% SSIM gain on real hardware by resolving the catastrophic distribution shift between classical Gaussian-trained models and true Poissonian single-photon hardware statistics (e.g., SNSPDs).

  5. Create a diagnostic tool for detector performance by profiling Dark Count Rate (DCR) and efficiency thresholds, defining the precise operating regime where quantum models decisively outperform classical alternatives (e.g., ≥100 photons/measurement, DCR < 10,000 Hz). This system can achieve:

  6. A structured explanation of failure modes under realistic hardware constraints for ghost imaging deployment.

  7. Implement an adaptive normalization layer that estimates Anscombe parameters dynamically based on real-time photon flux measurements. This system can achieve:

  8. Improved performance when operating under variable illumination by aligning the input distribution with the implicit assumptions of MSE loss functions, mitigating covariate shift in quantum ghost imaging systems.

  9. Develop a robust reconstruction pipeline for noisy or low-SNR environments (e.g., 15–30 dB SNR). This system can achieve:

  10. Superior structural integrity and readability down to 10 dB SNR levels compared to classical baselines, successfully avoiding the catastrophic high-frequency noise collapse characteristic of Pseudo-Inverse and FISTA methods.

  11. Improve reconstruction fidelity for highly dynamic or erratic motion (e.g., random walks, sudden accelerations) by incorporating kinematic constraints derived from temporal attention mechanisms calibrated for different motion types. This system can achieve:

  12. Maintained high structural coherence (SSIM > 0.90) across sequences with predictable trajectories (linear, oscillatory), while gracefully degrading only at extreme velocities where intra-frame motion blur fundamentally overwhelms the measurement interval.

  13. Design a sequence modeling architecture utilizing linear-time complexity methods (e.g., Mamba) instead of quadratic attention blocks for temporal processing. This system can achieve:

  14. Handling significantly longer temporal sequence lengths without the quadratic overhead, allowing for more complex motion tracking in real-world applications like medical or biological imaging, while maintaining efficient inference times.

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