Dual-Comb Ghost Imaging with Transformer-Based Reconstruction for Optical Fiber Endomicroscopy
Authors: David Dang
Myoung-Gyun Suh
Maodong Gao
ByoungJun Park
Beyonce Hu
Yucheng Jin
Wilton J. M. Kort-Kamp
Ho Wai Howard Lee
Venue: NeurIPS 2025 poster ()
Keywords: Ghost Imaging
Sparse Sensing
Optics
Transformers
Primary area: applications

Abstract: Endoscopic imaging is indispensable for visualizing internal organs, yet conventional systems remain bulky and costly because they rely on large, multi-element optics, which limits their ability to access and image certain areas of the body. Achieving high-quality endomicroscopy with hundred micron-scale and inexpensive hardware remains a grand challenge. Optical fibers offer a sub-millimeter-scale imaging conduit that could meet this need, but existing fiber-based approaches typically require either raster scanning or multicore bundles, which limit resolution and speed of imaging. In this work, we overcome these limitations by combining dual-comb interferometry with optical ghost imaging and advanced algorithm. Optical frequency combs enable precise and parallel speckle illumination via wavelength-division multiplexing through a single-core fiber, while our dual-comb compressive ghost imaging approach enables snapshot detection of bucket-sum signals using a single-pixel detector, eliminating the need for both spatial and spectral scanning. To reconstruct images from these highly compressed measurements, we introduce Optical Ghost-GPT, a transformer-based image reconstruction model that enables fast, high-fidelity recovery at low sampling rates. Our dual-comb ghost imaging approach, combined with the novel algorithm, outperforms classical ghost imaging techniques in both speed and accuracy, enabling real-time, high-resolution endoscopic imaging with a significantly reduced device footprint. This advancement paves the way for non-invasive, high-resolution, low-cost endomicroscopy and other sensing applications constrained by hardware size and complexity.

Dual-Comb Ghost Imaging with Transformer-Based Reconstruction for Optical Fiber Endomicroscopy
David Dang!. 1.3
Myoung-Gyun Suh? Maodong Gao
Byoung Jun Park2 Ho Wai (Howard) Leek
Beyonce Hu- Yucheng Jin
Wilton J.M. Kort-Kamp"
University of California, Irvine 2TTT Physics and Informatics Laboratories
Los Alamos National Laboratory
{dangd5, beyonceh, yuchej9, Howardhw Howarddu lee] .dd
{myoung-Be gyygg..g........ gyun.suh,
@ntteessarctt-rrhh com, mgwo@caltech..cmmmmmmmmmmm kortkamp@lanl gov
Abstract
Endoscopic imaging is indispensable for visualizing internal organs, yet conven- tional systems remain bulky and costly because they rely on large, multi-element optics, which limits their ability to access and image certain areas of the body. Achieving high-quality endomicroscopy with hundred micron-scale and inexpen- sive hardware remains grand challenge. Optical fibers offer nub-millimeter-seer imaging conduit that could meet this need, but existing fiber-based approaches typi- cally require either raster scanning or multicore bundles, which limit resolution and speed of imaging. In this work, we overcome these limitations by combining dual- comb interferometry with optical ghost imaging and advanced algorithm. Optical frequency combs enable precise and parallel speckle illumination via wavelength- division multiplexing through a single-core fiber, while our dual-comb compressive ghost imaging approach enables snapshot detection of bucket-sum signals using single-pixel detector, eliminating the need for both spatial and spectral scanning. To reconstruct images from these highly compressed measurements, we introduce Optical Ghost-GPT, a transformer based image reconstruction model that enables fast, high-fidelity recovery low sampling ratios. Our dual-comb ghost imaging approach, combined with the novel algorithm, outperforms classical ghost imaging techniques in both speed and accuracy, enabling real-time, high-resolution endo- scopic imaging with a significantly reduced device footprint. This advancement paves the way for non-invasive, high-resolution, low-cost endomicroscopy and other sensing applications constrained by hardware size and complexity.
Introduction
Endoscopes are an important tool in modern medical diagnostics, enabling direct visualization of internal organs and tissues. From gastrointestinal investigations to bronchoscopies and laparoscopies, endoscopic techniques are widely used for both diagnostic and therapeutic purposes. Traditional endoscopes typically consist of long, flexible rigid tube equipped with light source and camera system to capture real-time images [ Despite their essential role in clinical practice, conventional endoscopes face several critical challenges: (a) The complex electronics and imaging systems increases an endoscope's 's size to the order of millimeters to centimeters- resulting in invasive and uncomfortable procedures typically limited by the size of the optics and cameras and prevent the imaging of small body parts; (c) Additionally, these imaging systems play role in the large cost of endoscopes, with average prices in the range of several tens of thousands of dollars 22
patient; (b) Moreover, the imaging capabilities
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Fiber endomicroscopy offers markedly smaller alternative. By exploiting single-core, multicore, or multimode optical fibers with outer diameters below 500 micron, is possible to deliver light and collect signals through conduit scarcely thicker than a human hair. Early demonstrations, including confocal [3] and multiphoton fluorescence microendoscopes
established that cellular-scale imag-
ing could be achieved with a sub-millimeter footprint. Subsequent advances such as scanning-fiber endoscopes and microelectromeccaniicl (MEMS) scanners improved field of view [3] while com- putational approaches have enabled lensless, holographic, and light-field reconstructions through flexible probes as thin as 200 micron 6 7]. Despite these successes, most fiber-based systems still require either mechanical components such as MEMS scanner rotating torque coil for raster scanning or coherent fiber bundles whose inter-core spacing limits resolution, operation speed, and reduces fill factor.
Ghost imaging provides a way to bypass these bottlenecks by replacing pixelated cameras with correlated intensity measurements from single-pixel (bucket) detector[9]. In typical implemen- tation, a sequence of known illumination patterns are projected onto the sample, and statistical correlations between these patterns and the measured total intensities are used to computationally reconstruct the image. Ghost imaging has demonstrated advantages in low light conditions due to improved signal-to-noise ratios [T, 122 the ability to image through scattering media [3 [4], and compatibility with single-pixe detectors making it well suited for fiber-based imaging, where 2D detector arrays are impractical [5, 16]. However, classical ghost imaging remains slow because each pattern must be projected sequentially, and iterative reconstruction algorithms often converge slowly, prone to low image fidelity, stall at low sampling ratios.
In this work, we combine dual optical frequency comb (dual-comb, for short) interferometry I8 [9 with compressive ghost imaging to realize snapshot speckle imaging through single-core fiber, eliminating the need for slow spatial or spectral scanning while preserving a minimal footprint. For image recovery, we introduce Optical Ghost-GPT, transformer-basee reconstruction model that enables real-time, high-fidelity imaging. Our contributions include: (1) First demonstration of optical fiber-based ghost imaging using hardware-softwar co-design that combines dual-comb interferometry and deep learning, (2) superior reconstruction speed and resolution through the hurdware-software software co-design approach, (3) a robust transformer-based framework that maintains performance in noisy environments.
2 Background on Single-Pixel and Ghost Imaging
The most straightforward form of single-pixel imaging involves raster scanning, where a single-pixel detector sequentially measures light intensity at each pixel location, requiring N2 measurements
for N x N image. In contrast, ghost imaging enables image reconstruction from significantly fewer measurements by illuminating the object (x) with structured light patterns (A) and using
single-pixel detector to measure the total transmitted or reflected intensity [20] 21)
The sampling ratio is defined as:
M N2
8
where M is the number of structured light patterns used. Each projected pattern (m)) is modulated by the object and measured as scalar intensity, commonly referred to as the bucket sum:
(m))
(m) i.j
Ti.j
(2)
Early methods, such as Differential Ghost Imaging (DGI), used the bucket detector signals to compute weighted sums of the illumination patterns for object reconstruction, but these approaches often produced low-fidelity results [22] More advanced methods recast ghost imaging as linear system:
Vxx
where v is the sensing matrix formed by flattening and stacking the illumination patterns, and is the vectorized image. In this form, a standard solution using the is given by:
pseudoinverse (PI)
x = v b.
(4)

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To enhance reconstruction quality, compressed sensing techniques leverage tuunform-domalnn spar- sity 23 24 25 often employing & or lg regularization, resulting in
b vda, with x = &a.
(5)
Here, a sparsifying basis (e.g., DCT or wavelets), and a is sparse coefficient vector. Solutions are obtained using iterative optimization methods such as Iterative Hard Thresholding (IHT) [23]
Fast Iterative Thresshoogn Algorithm (FISTA) 24], or Alternating Direction Method of
Multipliers (ADMM) 25 Definitions of the variables used above are provided in Appendix A
Deep neural networks and generative models have been used to improve image reconstruction fidelity [26]. One common approach first applies classical reconstruction algorithm to produce a low-quality image, which is then enhanced using a trained neural network-such as a CNN, U-Net, or, more recently, a diffusion model [27] [28 29]. This method benefits from the ability to incorporate learned priors for denoising and super-resolution
However, its performance depends on the quality as well as speed of the initial reconstruction and requires large datasets of paired examples. Another approach uses an end-to-end strategy by embedding elements of the ghost imaging process directly into the neural network architecture [30 31. This method is typically faster, as it bypasses classical reconstruction algorithms entirely. However, prior studies have been constrained to low-resolution images (e.g., 28x28) and relied on binary mask patterns.
A critical oversight in most work on ghost imaging is the assumption of simultaneous pattern acquisition. This ignores the slow, serial nature of real-world SLM/DMD-based systems, which makes dynamic imaging impractical. In contrast, our hardware- interferometry to achieve true parallel acquisition. This makes high-fidelitg imaging of fast-moving objects feasible and aligns the physical system with the assumptions of modern reconstruction algorithms.
co-design uses dual-comb
3 Dual-Comb Ghost Imaging: Experimental Details
Figure illustrates the concept of dual-comb ghost imaging. An optical frequency comb is laser source whose spectrum consists of series of equally space. mutually coherent narrow frequency lines, resembling the teeth of a comb. our approach, a set of 2D speckle patterns H, comprising uncorrelated speckle distributions across different comb line frequencies. is generated at the fiber tip using dual optical frequency combs (OFCs) and projected onto the 2D target object x. The encoded intensity distribution (H x x) is detected by a single-pixe photodetector, producing a dual-comb interferogram. fast Fourier transform (FFT) converts this time-domain signal into frequency-domen bucket-sum data y H x x. By multiplexing light through the optical fiber, we parallelize the speckle projection process in ghost imaging, significantly increasing imaging speed.
For the dual-comb OFC source, we use two electro-optic (EO) combs with slightly different free spectral ranges (feSR). A continuous- wave (CW) fiber laser at 1550 nm is first amplified and split into two beams via a 50/50 fiber coupler. Each beam is frequency-shifter using an acousto-optic modulator, introducing a center frequency offset A fommer. These beams are then independently modulated by resonant EO modulators driven at fESR and fISR + A fESS. respectively, where fIsa 20 GHZz and A fisR- = 200 Hz. The resulting EO combs are recombined using 50/50 fiber coupler and amplified to compensate for insertion losses. When two mutually coherent combs with slightly different repetition rates are combined and photodetected, their interference produces set of beat signals in the frdiuefry (RF) domain. This allows fast, precise electronic detection without slow optical spectrum analyzers, while mutual coherence enables coherent averaging for improved signal-to-noise ratio (SNR) 17
Before the free-space imaging setup (see Figure Waveshaper is placed in the system to filter individual comb lines if needed. For bucket-sum measurements, the Waveshaper transmits the full dual-comb spectrum. The multimode fiber used in the experiments has core diameter of 200 um and exhibits hundreds of core modes, resulting in speckle patterns when light is coupled to the core of the optical fiber. The generated speckle patterns are then collimated and passed through the target object. A 50/50 beam splitter divides the beam into signal and reference paths, allowing simultaneous acquisition and suppression measurements are performed without the target to correct for optical path imbalances. The set of speckle patterns (H) without the target object is separately recorded using a 2D camera by selecting individual comb lines with the Waveshaper, either before after the imaging. The imaging target is
common-mode temporal noise. Calibration
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negative USAF 1951 resolution chart mounted on motorized stage. Bucket-sum signals are acquired using 500 kHz bandwidth free-space InGaAs photodetectors, and speckle patterns are recorded with 256
256 pixel InGaAs camera.
The imaging experiment
approximately 200 comb lines spanning from 1530 nm 1565
The speckle patterns at different frequencies are uncorrelated, as indicated by low Pearson correlation coefficients (FigureD) ()) with slight residual correlations attributed background contributions the pattern edges. Power variations among comb lines are normalized during processing. Figure 2 shows the signal and reference RF combs obtained via FFT of the interferograms. The amplitude ratios of corresponding comb peaks represent the bucket-sum signals that encode the image information. A calibration measurement without the target used correct for the path differences.
For image reconstruction, we used the calibrated bucket-sum signals and the measured speckle pat- terns. Figure 21 shows the calibrated measurement compared to the theoretical predictions, showing excellent agreement. Images are initially reconstructed using the Aloore-Penrose pseudoinverse, recovering the target image even at 0.3% sampling ratio. However, background noise from speckle structures remains significant. To address this, we developed Optical Ghost-GPT, a transformer-bar reconstruction algorithm that substantially suppresses noise and enhances image quality. Details of Optical Ghost-GPT will be discussed in the following section.
(a)
Measured Intensity
Target Object
Single-Pixel Detector
RF comb
Optical Fiber
Dual OFC
2D Speckle Pattern (H)
Image Reconstruction
(b)
PD (Reference)
PD (Signal)
Osciloscopp
Targel
Collimator
FLL Pattern
FL
2D camera
WaveShaper
MMF
SMF
OSA
Figure 1: Optical Fiber-based Dual-Comb Ghost Imaging. (a) Conceptual illustration of optical fiber-based ghost imaging using dual optical frequency combs. Each comb line generates an uncorre- lated speckle pattern at the fiber tip, and the set of speckle patterns is mapped onto the target object The encoded speckle pattern (H x x)) is collected by a single-pixel detector, and the resulting dual- comb interference signal ("interferogram" ") is recorded. The time-domain interferogram is converted into the frequency domain by fast Fourier transform (FFT), yielding the bucket-sum information (y H x).. The dual-comb technique allows for snapshot ghost imaging, providing highly parallel, high-SNR, and fast imaging capability. Notably, the use of comb-based wavelength-division multiplexing (WDM) and bucket-sum compressive imaging reduces the physical dimensions of both input and output hardware to essentially zero-dimensional (i.e., single spot or pixel), making the system particularly suitable for applications requiring compact form factors, such as endomicroscopy, (b) Experimental setup diagram. SMF: Single Mode Fiber, OSA: Optical Spectrum Analyzer, PD: Photodetector, FL: Focusing Lens, BS: Beam Splitter, NDF: Neutral Density Filter.

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(a)
1532 mm
1:35,04 nm
X539 19
1541 36
154653 rm
1560
(b)
(c)
67
Spec Pattern Carne ation
Combe (FFT Interferogammt) graeieaennnn Retio
1136.31 07
73
2 24
Bignal Roforenoo Radio tbeforo ca Brosinn
51
Erequancyy (etlo)
Munchergth
(d)
Bucket- Sum Signal (after calibration)
Theory
Dual-Comb
Dual Comb
pumm Wf a Mm
Homotrrd
bas
EITer
0.000
www.
1535
1543
1348
1500
1925
1500
Wovellngth (mm))
Figure 2: Experimental Details. (a) Speckle patterns measured different comb line frequencies. Each image contains 256 x 256 pixels. (b) Pearson correlation coefficients calculated between speckle patterns across the wavelength range from 1532 nm to 1564 nm. The correlation is computed within the bright circular region of each speckle pattern. The low off-diagonal values indicate that the speckle patterns are largely uncorrelated. Residual correlations are attributed primarily to the common background near the edges of the speckle patterns. (c) Radio-frequency (RF) combs generated from dual-comb interference signals measured by the signal and reference single-pixer detectors (upper and middle panels). The ratio between the comb peak amplitudes of the signal and reference RF combs is shown (bottom panel). Differences between the signal and reference beam paths are calibrated using measurements performed with and without the target object. (d) The calibrated bucket-sum signal measured from the dual-comb experiment shows excellent agreement with the theoretical prediction, which is obtained by masking the ground-truth pattern onto the stored speckle pattern images. The standard deviation (SD) between the two bucket-sum signals is 0.008. Image reconstruction is performed using the Hoore-Penross pseudoinverse algorithm at sampling ratio (SR) of 0.29%. Both the theoretical reconstruction (left) and the experimental dual-comb reconstruction (right) successfully recover the ground-truth patterns.
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Transformer Modeling
Transformers are powerful deep learning architecture originally introduced for natural language
processing (NLP) tasks but have since found applications in various domains 332 including computer vision 133, speech processing 34,, and scientific data analysis [35]. They leverage a mechanism called self-attention to model long-range dependencies and capture contextual relationships within sequences [36]. Unlike traditional recurrent or convolutional networks, transformers process entire sequences simultaneously. making them highly efficient for parallel computation. Their ability to learn complex patterns from large datasets has made them the backbone state-of-the-art models
like BERT, GPT (37], and Vision Transformers (ViTs) 38 39].
In typical ViTs, the image is broken up into equally sized patches, which serve as the ViTs' token. In order to adapt this methodology to dual-comb ghost imaging, we propose concatenating the flattened illumination patterns that make the sensing matrix, v with the bucket value form the token for our model.
Token
Target Image
Optical Ghost-GPT
Token
BatchNorm E
MHSA
BatchNorm
Linear
GELU
Linear
MSE
Reconstruction
Skip Connections
$12
m Token
Backpropagation
Figure 3: Schematic of Optical Ghost-GPT. The speckle patterns are first compressed into latent space and then concatenated with the corresponding bucket measurements form the input token sequence.
Model Architecture. We introduce Optical Ghost-GPT (or simply Ghost-GPT), a transformer- based model designed for structured image reconstruction from ghost imaging measurements. The model leverages stacked self-attention mechanisms and residual feedforward blocks to model long- range dependencies across the contextual input.
Input Embedding. The model receives two primary inputs each token: a flattened image the speckle pattern Th E N where N 256 x 256, and bucket sum value b The image vector is projected via a learnable linear transformation to a latent embedding of dimension embedding dim and afterwards, the latent representation of the image vector and its corresponding bucket sum are concatenated. To encode positional structure, we add learned positional embeddings of size embedding dim each token in the sequence:
z, = e, + Pi, i
where e; the input token embedding, p, is the corresponding positional embedding, and C=250 is the context size determined by the theorectical maximum number of RF comblines in our setup.
Transformer Blocks. The architecture contains a stack of L, transformer blocks, each composed of multi-head self-attention (MHSA) mechanism and two-layer feedforward network. The attention mechanism employs H number of heads, computed as:
QKT Vdle
Attention(Q. (Q, K, V) = Softmax
V.

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where Q. K, and V are linear projections of the input sequence, and d2 = embedding dim. We also upply a dropout mask with value of 0.1 ensure robustness of the model and allow the model to generalize to missing shots and buckets. Each block includes residual connections and batch normalization operation placed before and after the MHSA layer, defined as:
BatchNorm(x)
X X-
J = E[x],
a² -Vr[x)]
The feedforward network consists of two linear transformations with gaussian error linear unit (GELU) activation; A final transformer block is appended after the main stack to further refine the sequence representation.
FFN Linear2 (Xinean
Output Projection. The final token representations are projected to _ via a linear layer and the output flattened to tensor of size (C 16) and passed through a final linear layer to reconstruct the original image vector in R 256 A sigmoid activation is applied to constrain the output the range [0, 1]. consistent with normalized image intensities:
x a (Linearmna (Flatteen)))))
Network Training. We first obtain set of speckle patterns from the 2-D camera during the calibration phase of the experiment. (We emphasize that the 2-D camera only needed to obtain obtained 188
initial speckle patterns and can be removed during imaging). For this experiment, speckle patterns, which are then used to generate synthetic buckets sums via a convolution between the digitized speckle pattern and images from the MNIST and OMNIglot datasets. We form labeled dataset of synthetic bucket sums as the x-label and its corresponding target images as the y-label. (See Appendix B for train/test split and computational resources used).
In training, Ghost-GPT predicts the target image, given the speckle pattern and bucket sums as input. We use mean squared error our loss function and an AdamW optimizer with learning rate of 0.0003 and a weight decay of 0.001. For the following reconstruction results, we used a model with the number of attention heads set to 8 and the embedding_ dim set to 32 based on hyperparameter sweep. (See Appendix for further information on the hyperparameter analysis).
Ghost-GPT Reconstruction Results
In this section, examine the reconstruction results in simulation using our experimental speckle pattern results. Figure 4 shows series of reconstruction compared with their true images.
MSE: 0.0029
Choos OFT
Tuo Image
0165t GPT
The Imago
Ghoz OPF
True imoge
TS
Y
S
SSIM:
0.8382
0.8827 MSE:
3
3
Figure 4: Reconstructions from Ghost-GPT versus the true image with the Structural Similarity Index Measure (SSIM) and Mean Squared Error (MSE) displayed above.
We emphasize that we achieve very high SSIM (greater than 0.7) and low MSE (less than 0.02) values despite having a sampling ratio of only 0. 0.29%. We observe that the model is able to preserve fine structural details and clear object boundaries, highlighting the model's ability to recover meaningful
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image content from minimal measurements. However, the reconstructions can exhibit variations in intensity across the object, especially in images with long, thin structures or uniform intensity profiles. These artifacts likely stem from the inherent non-uniformity of the speckle patterns generated by the multi-mode fiber. This issue could potentially be mitigated by employing a more uniform speckle distribution or by including smoothness term, such as total variational loss, in the loss function.
We also compared our model against classical reconstruction algorithms on 256 images from our validation dataset. (256 images were chosen due to the long reconstruction times associated with the iterative FISTA algorithm). Table compares the MSE and SSIM of previously discussed reconstruction algorithms- expected the simpler reconstruction algorithms such as Differential Ghost Imaging and the Moore-Penrose Psuedo Inverse perform worse than compressed sensing methods like FISTA. However, Ghost-GPP outperforms these classical algorithms giving an average MSE of 0.008 and SSIM of 0.788, while being approximately 263x faster than FISTA. (We set c = 50 after performing hyperparameter sweep and choosing the best performing SSIM). The extremely fast reconstruction speed of 14 ms enables real-time, video frame-rate ghost imaging in optical fibers. Importantly, while the computational reconstruction speed can be further improved with better computing hardware, the fundamental limit image reconstruction is set by the repetition rate difference of the dual-comb, which is typically a few hundred Hz to several kHz in our experiments. With a larger repetition rate difference, a much higher frame rate is possible. However, this requires high-band wihhdhhh photodetection, and the trade-off between the sampling ratio and frame rate must be considered in accordance with the Nyquist condition.
Table Classical Algorithms vs Ghost-GPT
Algorithm
MSE
SSIM
Computational Time (ms)
Differential Ghost Imaging
0.184 (o=0.037)
0.042 (a=0.022)
15.7 (a=0.138) GPU
5640 (o=52.1) CPU
Moore-Penrose Pseudo-Inverse
0.093 (m=0.027) 0.055 (a=0.027)
157 (a=3.27) GPU
3680 (a=125) CPU
FISTA (200 iters, c=50)
0.045 (a=0.017)
0.092 (a=0.019)
10500 (o=93.8) GPU
Ghost-GPT (Ours)
0.008 (a=0.009) 0.788 (a=0.076)
14.0 (o=0.457) GPU
With our experimental setup, we collected physical intensity measurements of USAF resolution test target (ThorLabs R3L3SIN 3" x 3"). Figure and Table shows the reconstruction results of a stripe (top) and the number 2 (bottom) compared with classical algorithms and ShosttGGP and the corresponding MSE and SSIM values. In real world environments, we verify that Ghost-GPP is able to successfully reconstruct images with higher fidelity for both targets compared to classical methods.
True
DGI
Pl
FISTA
Ghost-GPT
2
Figure 5: Reconstruction using experimental bucket measurements compared with classical algo- rithms and Ghost-GPT. (Top row): Striped lines correspond to group 0, element 4 (1.41 line pairs per millimeter). (Bottom Row): The number corresponds to group 0, element 2 (1.12 lp/mm).
6 Robustness of Ghost-GPT in Experimental Imaging
To examine the effect of noise our experimental measurement, we performed a signal-to-noke to-se ratio analysis by adding varying amounts of noise to simulated buckets and gauged the quality
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Table 2: MSE/SSIM for Experimental Targets with Classical Algorithms and Ghost-GPT
Experimental Target
DGI
Pl
FISTA
Ghost-GPT (Ours)
Stripes Number 2
0.231/0.030
0.140/0.028
0.072/0.057 0.084/0.064
0.058/0.470
0.204/0.036
0. 0.138/0.028
0.077/0.484
0 dB
10 dB
20 dB
25 dB
30 dB
DGI
PI
FISTA
Ghost- GPT
Figure 6: Simulated reconstructions of the USAF striped targets with artificial noise added to the buckets. The signal to noise ratio in dB is shown label at the top with the corresponding reconstruction algorithms on the left-hand side.
of image reconstructions (Figure 7). The artificial noise added to the buckets were calculated by sampling points from gaussian with mean of zero and standard deviation of one and scaling inversely proportional to the value of the SNR. (See Appendix D.I for more information about the SNR calculation). Ghost-GPT demonstrates strong robustness to noise, outperforming classical reconstruction algorithms. with recognizable reconstructions achievable at SNR levels above 20 dB for Ghost-GPT. (See Appendix Figure 9 for plots of the calculated MSE and SSIM values SNR). Based on this analysis, our experimental bucket measurements are approximately equivalent to 20-25 dB. For further experimental results, code /aaa availability, and discussion about project limitations, please refer to D.2 and G
Comparison with Other Deep Learning Models
We compared Ghost-GPT, our transformer-bact reconstruction model, against two deep learning baselines: decoder-only CNN and U-Net with skip connections. All models were matched in parameter count (~ 270M) and trained on identical speckle speckle-bucket bucket inputs for fair comparison. As shown in Table 3, Ghost-GPT achieves the lowest mean- seean-squrre error (MSE 0.0068), representing a 47.7% reduction vs. CNN and 38. 18% vs. U-Net across the entire validation dataset, while maintaining comparable structural similarity (SSIM = 0.787).
Training dynamics further show that Ghost-GPT converges lower validation loss within the same number of epochs, supporting the suitability of the transformer's self-attention mechanism

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Table 3: Image Reconstruction Quality on Various Neural Network Architectures
Model Architecture
MSE
SSIM
Computational Time (ms)
Ghost-GPT (Ours)
0.0068 (o=0.0077)
0.787 (o=0.073)
14.0 (a=0.457) (GPU)
CNN UNet
0.013 (a=0.0086)
0.8079 (o=0.0395)
6.26 (o==0.41) (GPU)
0.011 (r=0.0073)
0.8316 (o=0.0696)
15.09 (a=0.032) (GPU)
for modeling the global correlations inherent in ghost imaging measurements. These findings align with recent studies reporting transformer models outperforming conventional deep architectures in computational imaging tasks [40]. While our model was trained using MSE loss for quantitative comparability, the observed SSIM gap suggests that training with a perceptual loss SSIM- -Sighweighted objective could further enhance structural fidelity without sacrificing reconstruction accuracy. We leave this promising direction for improving perceptual quality in future iterations of Ghost-GPT. It is worth noting that, with simple binary datasets, it was difficult to compare GhostGPT to other deep-learning models because the performance metries gave mixed signals within narrow range. With a larger number of speckle patterns and more realistic grayscale datasets, we should be able to conduct more systematic performance comparison with other deep-learning models.
Ghost-GPT
U-Net
CNN
Validation Loss (MSE)
10
10º
10
25
Epochs
Figure 7: Loss history of different deep learning models compared with Ghost-GPT. Our transformer- based model consistently outperforms both U-Net and CNN for large majority of epochs.
Conclusion
In this work, we demonstrate novel dual-comb ghost imaging using only single-core fiber and a single-pixel detector by combining dual-comb interferometry with a Transformer tailored for fast, high-quality reconstruction-a hardware-software co-design. Our GPT-like deep learning model is application-aware: paired tokens jointly encode each speckle pattern and its corresponding bucket sum meet real-time, high-fidelity goals. Leveraging such deep learning architectures, achieved figh-fidelity reconstructions (average SSIM of 0.788) at ultra-low sampling ratios (as low as 0.29%), significantly outperforming classical methods in both resolution and efficiency. In our first experimental demonstration, the number of uncorrelated speckle patterns was limited to about 200, so we used binary datasets suitable for reconstruction at such low sampling ratios. This, however, is prototype-stee constraint, not fundamental limitation of the method. Realistic grayscale reconstructions could be enabled by increasing the number of comb lines, which can be achieved by broadening the spectrum and reducing speckle correlation width. Future work will focus on achieving video-rate grayscale imaging, enhancing the model via improved sensing matrix design and noise-aware training, and developing efficient beam collection in reflective-mode setups for practical applications. This framework holds strong potential for emerging applications in biomedical imaging and imaging within extreme, low-light environments. The ability to reconstruct high- resolution images from sparse, low-light measurements makes it particularly suited for imaging live or light-sensitive biological samples, for real-time, minimally invasive procedures such as fiber-based endoscopy, or dynamic tissue monitoring -offerinf a promising pathway toward next- next-generation medical diagnostics.
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Acknowledgments and Disclosure of Funding
DD, MGS, and HWHL conceived the concept of Optical Ghost-GPT. DD and WJMKK designed and implemented the GPT model architecture. DD, BH, and MGS carried out model training, evaluation, and comparative analysis with classical reconstruction algorithms. MGS, MG, and BJP led the experimental integration of dual-comb spectroscopy with optical aber-based ghost imaging. YJ explored various optical fiber types for this application and conducted speckle pattern simulations using Finite Difference Time Domain (FDTD) methods. All authors contributed to the discussions and writing of the manuscript.
DD acknowledges the support by UCI-LANL-SoCal Hub graduate fellowship program. WJMKK acknowledges the LANL Laboratory Directed Research and Development program for funding under project 20250492ER.
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A Variable Definitions
The equations in the main text the following symbols:
A(m) E RNNN; The m structured illumination pattern,
E NNNN The unknown image to be reconstructed,
b(m); Total detected intensity for the m pattern,
b E Mx1; Vector of all scalar intensity measurements,
v RMNN;; Sensing matrix composed of flattened patterns,
x E R R x1; Flattened version of the image z,
+ E N²?"; Sparsiffynng basis (e.g., DCT, wavelets), M Sparse coefficients in the transform domain.
B Dataset Generation, Train and Test Split
Since the bucket signal is effectively a convolution between the object and each speckle pattern, computationally efficient synthetically generate labeled training data by pairing known objects with their corresponding bucket sums. For this, we use open-source image datasets and simulate the measurement process. Our dataset includes 19,280 from OMNIglot and 19,280 images from MNIST with a train/val split of 33,000/5,566 respectively. All images are resized to 256 x 256 to match the resolution of the recorded speckle patterns. We use batch size of 32 for the training dataset and batch size of 64 for the validation dataset.
B.1 Licensing
Originally published by [41], downloaded via torchvision datasets. and licensed under the Creative Commons Attribution-S5 Alike 3.0 license (see http://yann. lecun com/exdb/aa h/////xbb/mms//////// mnist/).
MNIST:
Omniglot: Originally published in and licensed under the MIT License (see htctss///gtuubb.cco/rreeddddddd
downloaded via torchvision..attaseta. Omm.
C Hyperparameter Analysis
Train Val
Loss (Mean Squared Error)
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Embedding Dimension
Epochs
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Figure 8: The lowest loss value the validation dataset function of the number of attention heads and embedding dimensions (a) comparison with pattern embedding (b) log scale loss values of models with and without pattern embedding
For hyperparameter tuning, we performed a simple parameter sweep by changing the number of attention heads values of and 8 as well as adjusting the embedding dimension at values of 16, 32,
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and 64. Due to memory constraints, we limited our parameter search to these values, As previously stated, we use mean squared error as our loss function and an AdamW optimizer with a learning rate of 0.0003 and a weight decay of 0.001. In our loss function, we add the error in each pixel and then average over the batch size.
Over 30 epochs, the model took approximately I hour to train on a Linux workstation, using an AMD Ryzen Threadripper 3990X 64-Core Processor and four NVIDIA A6000 GPUs. Our chosen model (8 attention heads, embedding dimension 32) contained ~ 270 million trainable parameters and demonstrates convergence afters 10 epochs (Appendix Figure 8b displaying no characteristics of overfitting.
D Further Experimental Results
D.1 Different Algorithms SNR
The SNR was calculated via the following equation:
E[b]
b Doois (0( b,
SNR
We sample points from gaussian of mean 0 and std I, which scaled by a, the standard deviation. the collection of bucket sum and then dividing by the SNR
is calculated the by averaging value raised to the 10th power. The true bucket sum, b, is added to the previously calculated quantity create the noisy bucket, Draisy. Previous works in ghost imaging formulated their SNR analysis using these exact definitions.
Appendix Figure plots the MSE and SSIM of the reconstructed images compared with classical reconstruction algorithms and Ghost-GPT.
0.35
DG
Pauedo Inverse
Structural Similariyy Index Measure
FISTA
0.5
Mean Squared Erro
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Psuedo Inverse
FIBTA
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Ghost-GPT
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Figure SNR ratio in the buckets versus (a) MSE and (b) SSIM of the USAF target
D.2 Resolution Analysis
In ghost imaging, resolution is fundamentally linked to the spatial characteristics of the light patterns used to probe the scale correlation length of the intensity fluctuations in the illumination patterns (often speckle patterns). This grain size determines the finest detail that can be distinguished in the reconstructed image.
the grain size of these patterns. Grain size refers to the typical
In this section, we evaluated the smallest resolvable feature size achievable by Ghost-GPT in simulation using our current experimental speckle pattern set (Appendix Figure I0 A digital resolution target was generated with 15-pixel-wide bars and varying gap sizes (1, 3, 3, pixels). Ghost-GPT successfully resolved features with S-pixel gap. In our experimental setup, the Air Force resolution target's 23-pixel feature between a pair of stripes corresponds to a physical size of 0.355 mm, indicating that Ghost-GPT achieves resolution of approximately 0.077 mm. At
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Resolution Target
Reconstruction
1-D Resolution Target Overlay
largtt
Paol Intensity (O TO 1)
0.4
02
320 Pixnt Position
250
(a)
(b)
(c)
Figure 10: Simulated resolution testing of Ghost-GPT. (a) True resolution target with increasing pixel gaps (b) Ghost-GPT reconstruction results (c) Line profile comparing the ground truth and model output, obtained by scanning along a horizontal cross-section midway through the images.
this resolution, our ghost imaging model is well-suited for visualizing broad range of biological structures, including tissue architecture such as blood vessels, skin layers, and muscle fibers. also enables observation of developmental forms in small organisms like embryos and larvae, well as structural features in plant tissues such as roots and leaves.
E
Code Availability
The scripts, specific training/validation dataset, experimental dual-comb speckle patterns, and target measurements available at: Code Repository Link Instructions for running the scripts can be found README. txt file, and the required libraries and dependencies are specified in the environment. yml file.
F Broader Impacts
Although there are no evident potential negative societal impacts associated with dual-comb, trans- former based ghost imaging, there are many notable positive impacts. These include advances in the health industry, specifically biomedical image quality, speed, and adaptability clearer images can enable earlier disease detection and more precise surgical guidance. On top better medical image quality, the integration of deep learning into reconstruction imaging heavily benefits endoscopy devices as could allow for less invasive fiber-based procedures. particular, transformer based ghost imaging can also boost overall accuracy by learning and compensating for complex patterns with environmental noise, producing high quality images even low light or highly scattering conditions.
G Limitations
While Ghost-GPT demonstrates high reconstruction fidelity, computational efficiency, and robustness to noise, there are several limitations with our current work. First, the model performance contingent on the quality and representativeness of the calibration speckle patterns; any deviations in experimental conditions may lead to distribution shift, potentially reducing reconstruction accuracy. Second, although the model generalizes well to synthetic and experimental data within the resolution constraints of our setup, it has not been benchmarked on more complex, naturalistic scenes or dynamic objects beyond the training domain. Additionally, while the use of synthetic training data from MNIST and Omniglot enables rapid prototyping, the domain gap between these datasets and real biological clinical targets may necessitate fine-tuning with domain-specific data. Third, our current framework is only comparing our model against classical algorithms.
In this work, we focused our comparison classical ghost imaging reconstruction algorithms such as Differential Ghost Imaging, the Moore- Penrose pseudoinverse, and FISTA. However, further benchmarking against advanced generative super-resolution models-sun as diffusion models,
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adversarial networks, or U-Nets -coud offer insight into alternative priors and reconstruction strategies (i.e. more complex loss functions) that may enhance fidelity or reduce artifacts. We emphasize that this work represents an early proof-of-prn proof-of-principle for dual-comb ghost imaging using transformer-bant architecture and should be interpreted as foundation for future improvements. Continued development will include more sophisticated training data, model architectures, and experimental protocols to fully exploit the potential of this novel imaging paradigm.
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Reviews:
[{"review_id": "R3MOPnR9dy", "reviewer": "Reviewer_wkCt", "summary": "the paper proposes a supervised learning approach using a transformer based model for an inverse imaging problem. Experiments show that the proposed model demonstrates higher accuracy and speed on synthetic datasets and some select real world samples compared to iterative reconstruction algorithms.", "questions": "1. **Choice of training data**: The authors are requested to clarify the motivation behind using MNIST/Omniglot for training. Including results on more realistic or diverse datasets would strengthen the paper considerably.\n\n2. **Real-world data**: For the experiments on real-world data (lines 222–226), to better appreciate the significance of these experiments, it would be helpful to discuss the importance of this real world data. A discussion is also needed around what dataset should be used to train the model for the real data? If the model is trained on synthetic digits, how would it perform on real data? This clarification is important for assessing the generalization capabilities of the proposed method.\n\n3. **Comparative baselines**: It would be helpful to include comparisons with other deep learning-based reconstruction approaches, including CNNs or transformers of varying capacities. This would allow for a more comprehensive evaluation of the proposed model's effectiveness.\n\n4. **Reconstruction speed and system-level constraints**: Lines 216–221 discuss the speed of ~8 ms, but it would be useful to understand how this compares to the theoretical speed limit set by the system’s repetition rate. Additionally, the statement about the Nyquist condition and sampling/frame rate trade-off could benefit from further explanation tailored to a general ML audience.\n\n5. **Noise robustness claims**: The robustness analysis appears to be based on a single example. To make a strong claim about the robustness of the proposed method, experiments need to be performed on a variety of samples.", "limitations": "No. Please see above sections", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "**Strengths**\n\n- The paper is clearly written and easy to follow, which aids accessibility for a broad ML audience.\n- Technical concepts such as ghost imaging and limitations of existing endoscopic imaging techniques are introduced and explained in a way that is informative and approachable.\n- The work introduces a novel and timely problem to the ML community, bridging an interesting gap between optics and machine learning.\n- Experiments include a preliminary study on robustness to noise, which is an important consideration for practical applications.\n\n**Weaknesses**\n\n- **Limited originality in ML contribution**: While the application area is novel, the machine learning component—supervised learning using a transformer—follows a well-established pattern. It is now common knowledge in the community that deep learning models tend to outperform iterative reconstruction algorithms in inverse problems (e.g., compressive sensing, MRI), both in terms of accuracy and speed. As such, the methodological contribution on the ML side may be viewed as limited in novelty.\n\n- **Use of overly simplistic datasets**: The training and evaluation are primarily conducted on MNIST and Omniglot, which are not shown to be representative of the patterns found in endoscopic images. These datasets have binary-valued objects and lack realistic shape complexity. In accordance with related literature, experiments on more complex datasets(such as CelebA or imagenet) would make the experiments more rigourous.\n\n- **Limited diversity of baselines**: All baseline methods used for comparison are iterative algorithms. The lack of comparisons against more recent methods including other deep learning-based reconstruction methods (e.g., CNNs or other transformer variants) makes it difficult to assess how well the proposed model performs relative to recent advancements in the field.\n\n- **Concerns about practical applicability**: It is unclear whether the model was trained on real-world data or only on MNIST/Omniglot. If the latter, the assumption that it would generalize to practical endoscopic images is questionable and should be justified. Without a sufficiently addressing this issue, the applicability of the method to real clinical or imaging scenarios remains uncertain.", "was_revised": true, "final_justification": "- Rebuttal clarified dataset acquisition (optical, not simulated), justified dataset choices and elaborated on the novelty of the work.  \n- Rebuttal clarified how the existing experiments are sufficient and more validation is beyond the scope of this work,\n- The broader themes and novel applications shown in this work would be interesting for the broader Neurips community. And most of the required modifications to the presentation of the paper (as discussed in rebuttal phase), are possible in the final revision of the paper.\n- Such interdisciplinary works should receive more encouragement in the applications track of Neurips", "rebuttal": "We thank the reviewer for the careful reading and constructive feedback. In this rebuttal, we would like to clarify the novelty of our work and explain the constraints imposed by the experimental optics setup.\n \n**Response to the reviewer’s comment on the weaknesses:**\n \n**Weakness 1: Limited originality in ML contribution**\n\n​​We fully acknowledge that deep learning models are now widely appreciated for often outperforming iterative reconstruction algorithms in inverse problems. In our Ghost‑GPT approach, we combine speckle patterns and bucket sums as input tokens, which offers improved convergence and faster training. We believe this aspect brings a degree of novelty. To clarify further, we state that the main novelty of our work is twofold:\n (i) adopting the dual-comb interferometry method for the first time in a ghost imaging experiment, and, \n(ii) combining it with a transformer-based model that enables fast, high-fidelity imaging.\nFirst, the use of dual-comb interferometry in ghost imaging allows rapid, video-rate optical detection. Second, the Ghost-GPT algorithm supports high-fidelity, fast image reconstruction.\n \n**Weakness 2: Use of overly simplistic datasets** & \n**Weakness 4: Concerns about practical applicability**\n\nTo be specific, as this is the first experimental demonstration of dual-comb interferometry in ghost imaging, we were limited to approximately 200 speckle patterns due to experimental constraints (e.g., the FWHM of speckle correlation and the number of comb lines). This restriction limited us to testing Ghost-GPT on simple, binary datasets. Importantly, the small number of speckle patterns is not a fundamental limitation. Rather, the slower pace of experimental improvements, compared to computational algorithms, has constrained our current results. As a follow-up experiment, we are actively working on increasing the number of uncorrelated speckle patterns to the 1,000-10,000 range by reducing the FWHM of speckle correlation and spectral broadening of the optical frequency combs. This will enable testing with more realistic grayscale datasets, as shown in Chen, Yifan, et al. \"Large model enhanced computational ghost imaging.\" arXiv preprint arXiv:2503.08710 (2025). We hope this clarifies why we chose simple binary image datasets rather than more complex image datasets to demonstrate the feasibility of our novel experimental method using dual-comb, and we believe it also addresses the reviewer’s concerns about practical applicability.\n\n**Weakness 3: Limited diversity of baselines**\n\nRegarding the concern about the limited diversity of baseline methods, our primary goal was to demonstrate that the Ghost-GPT model enables fast, high-fidelity imaging when combined with our novel experimental method, rather than to claim superiority over all DL models. Nevertheless, we agree such comparisons are valuable. Since submission, we have compared Ghost-GPT with state-of-the-art models (Diffusion, CNN, and U-Net) using the same binary datasets. Our transformer model outperforms CNN and U-Net variants of similar size by ~10% in mean squared error on the validation set and surpasses a diffusion-based variant by >50%. While our transformer-based models generally perform better in image fidelity and training speed, a more rigorous comparison will require testing with 1,000–10,000 speckle patterns and more complex datasets, as discussed above.\n \nAs an aside, if we had focused solely on the image reconstruction algorithm—assuming a large number of orthogonal imaging basis patterns could be generated using spatial light modulators or digital micromirror devices (as is common in prior computational imaging works)—comparing Ghost-GPT with other DL algorithms would have been simpler. However, in the case of minimally invasive, video-rate optical fiber endomicroscopy using zero-dimensional optical front-end hardware (e.g., single-core fiber and single-pixel detector), achieving video-rate imaging without dual-comb interferometry would be extremely challenging, if not impossible, and generating a large number of uncorrelated speckle patterns is not technically straightforward. Given the significant novelty of dual-comb interferometry in ghost imaging and its potential for further experimental advances, we believe it is somewhat unfair to evaluate our work solely on the Ghost-GPT model’s novelty.\n \n**Responses to specific reviewer questions:**\n \n**Question 1: choice of training data** MNIST and Omniglot were selected because our current experimental system provides only ~200 independent speckle patterns; binary/sparse datasets avoid underdetermined grayscale reconstructions. This does not fundamentally limit the method. Increasing the number of uncorrelated speckle patterns (i.e., comb lines) is already in progress and will enable testing with more complex datasets. We will clarify this rationale.\n \n**Question 2: real-world data** The model is trained on simulated bucket-sum measurements using known image datasets and the exact speckle patterns used experimentally, and is then applied to real bucket-sum data acquired through dual-comb measurements to reconstruct the test target image. This real-world optical experiment is conducted to evaluate the feasibility of dual-comb interferometry in ghost imaging, not to test the Ghost-GPT algorithm itself. Due to the limited number of speckle patterns, we used simple binary datasets for model training and simple binary patterns (e.g., the USAF1951 resolution target) as real-world target objects in the current work. However, as discussed earlier, with an increased number of speckle patterns, we will be able to use more complex grayscale image datasets for model training, and we expect to image real biological samples.\n \n**Question 3: comparative baselines** As briefly mentioned earlier, we have compared Ghost-GPT with state-of-the-art models (Diffusion, CNN, and U-Net) using the same binary datasets since the manuscript submission. Initial comparison results show that our approach achieves superior performance in terms of image fidelity and training speed. Specifically, our transformer model outperforms CNN and U-Net variants of similar size by ~10% in mean squared error on the validation set and surpasses a diffusion-based variant by >50%. These results will be included in the final version.\n \n**Question 4: reconstruction speed and system-level constraints** The model’s rapid reconstruction speed is currently 8 ms per frame when run on Nvidia A6000 GPU, and it can be further improved with more powerful computational hardware. The fundamental speed limit of our dual-comb compressive imaging method is set by the repetition rate difference of the dual comb, which in our current experiments ranges from a few hundred Hz to several kHz. Since the manuscript submission, we have demonstrated imaging of moving objects at frame rates of 30 Hz–1 kHz using a 3 kHz repetition-rate difference. Therefore, the overall imaging speed of the current experiment is limited by the computational reconstruction, not by the optical sampling speed of the dual-comb method.\n\nRegarding the statement about the Nyquist condition and the sampling/frame rate trade-off, we will tailor the explanation for a general ML audience as follows:\n\n“Importantly, while the computational reconstruction speed can be further improved with better computing hardware, the fundamental limit of image reconstruction is set by the repetition rate difference $\\Delta f_{rep}$ of the dual-comb, which is typically a few hundred Hz to several kHz in our experiments. With a larger $\\Delta f_{rep}$, a much higher frame rate is possible. However, this requires high-bandwidth photodetection and introduces an inherent trade-off between the frame rate $f_{frame}$ and the sampling ratio $\\beta$ (i.e., the number of comb lines $M$ divided by the image resolution $N^2$). According to the Nyquist condition in the context of the dual-comb method, the repetition-rate difference of the dual-comb (i.e., the fundamental limit of the frame rate) multiplied by the number of comb lines (i.e., the sampling ratio times the image resolution) defines the RF bandwidth $B_{RF}$ of the sampling frequencies, which must be less than half the repetition rate $f_{rep}$ of each optical frequency comb to avoid aliasing:\n$B_{RF} = \\Delta f_{rep} \\cdot M = f_{frame} \\cdot \\beta \\cdot N^2  < \\frac{f_{rep}}{2}$.\nIncreasing the sampling ratio (more comb lines) reduces the frame rate, and vice versa. This fundamental constraint must be carefully balanced when designing dual-comb compressive imaging systems.”\n\n**Question 5: noise robustness claims** We are extending the noise study to multiple samples and reporting aggregate metrics. These results will be included in the final version.\n \nLastly, we appreciate the reviewer’s valuable comments and suggestions. We believe that the combination of (i) the first dual-comb ghost imaging hardware demonstration and (ii) the Ghost-GPT dual-token transformer model provides substantive novelty. We will clarify dataset choices, add DL baselines, expand noise tests, and better explain system-level constraints to address the reviewer’s concerns."}, {"review_id": "0AVo1iPAcX", "reviewer": "Reviewer_T9rk", "summary": "This paper proposes a transformer-based compressive-sensing reconstruction algorithm for optical fiber endomicroscopy. Experiments have demonstrated the effectiveness and efficiency of the proposed algorithm.", "questions": "1) What are the characteristics of the observation matrix ${\\Psi}$ in this problem. The authors should give a brief discussion on this, because the observation matrix strongly influences the quality of reconstruction.\n2) In Table I, the authors compare the computational time of different algorithms but do not specify the platform that these algorithms run on. Do these algorithms run on a CPU, GPU? or some of them run on a CPU and some of them run on a GPU?\n3) Experiments are conducted on simulated data. The performence of the proposed algorithm on real data is not clear.", "limitations": "1) The novelty is fair. What is the biggest difference between the proposed transformer-based algorithm with existing compressive-sensing reconstruction algorithms? Why should the proposed algorithm be more effective than existing algorithms?\n2) Experimental results are not sufficiently convincing. The compared algorithm Moore-Penrose Psuedo Inverse should not be counted as a compressive-sensing reconstruction algorithm. More advanced algorithms should be compared in experiments.\n3) The effectiveness of the proposed algorithm on real data has not been demonstrated.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\nOptical fiber endomicroscopy is an interesting problem that very suits to apply compressive-sensing algorithms, the authors demonstrate the possibility to improve the imaging quality of optical fiber endomicroscopy by employing transformer-based deep models.\n\nWeakness:\n1) The novelty is fair. What is the biggest difference between the proposed transformer-based algorithm with existing compressive-sensing reconstruction algorithms? Why should the proposed algorithm be more effective than existing algorithms?\n2) Experimental results are not sufficiently convincing. The compared algorithm Moore-Penrose Psuedo Inverse should not be counted as a compressive-sensing reconstruction algorithm. More advanced algorithms should be compared in experiments.\n3) It would be better if the authors could further clarify the difference between optical fiber endomicroscopy between classical compressive-sensing problems, i.e., in aspects of image prior, encoding matrix, input measurements, and so on.", "was_revised": true, "final_justification": "Thank you for the effort in considering and replying to all the points mentioned in the review. The rebuttal has improved my understanding of the contributions of the paper, but limitations on novelty and applicability of the proposed method still exist. Therefore I would like to keep my original score.", "rebuttal": "We thank the reviewer for the careful reading and constructive feedback. In this rebuttal, we would like to clarify the novelty of our work and explain the constraints imposed by the experimental setup.\n \n**Response to the reviewer’s comments on weaknesses and limitations:**\n \n**Weakness & Limitation 1: The novelty is fair. What is the biggest difference between the proposed transformer-based algorithm with existing compressive-sensing reconstruction algorithms? Why should the proposed algorithm be more effective than existing algorithms?**\n\nThe novelty of our work is twofold: (i) adopting the dual-comb interferometry method for the first time in a ghost imaging experiment and (ii) combining it with a transformer-based model that enables fast, high-fidelity imaging. First, the use of dual-comb interferometry in ghost imaging allows rapid, video-rate optical detection. Second, the Ghost-GPT algorithm supports high-fidelity, fast image reconstruction. Regarding the novelty of our Ghost-GPT model, although it is now well known in the community that deep learning models often outperform iterative reconstruction algorithms in inverse problems, our Ghost-GPT model uses both speckle patterns and bucket sums as input tokens, enhancing training speed and convergence while achieving higher image fidelity than non-ML algorithms. We also note that the reviewer’s evaluation appears to focus mostly on the ML-algorithm side, overlooking both the dual-comb optical experiment aspect and the experimental constraints that influenced the choices for ML model design and datasets.\n \nTo be specific, as this is the first experimental demonstration of dual-comb interferometry in ghost imaging, we were limited to approximately 200 speckle patterns due to experimental constraints (e.g., the FWHM of speckle correlation and the number of comb lines). This restriction limited us to testing Ghost-GPT on simple, binary datasets. Importantly, the small number of speckle patterns is not a fundamental limitation. Rather, the slower pace of experimental improvements, compared to computational algorithms, has constrained our current results. As a follow-up experiment, we are actively working on increasing the number of uncorrelated speckle patterns to the 1,000-10,000 range by reducing the FWHM of speckle correlation and spectral broadening of the optical frequency combs. This will enable testing with more realistic grayscale datasets, as shown in Chen, Yifan, et al. \"Large model enhanced computational ghost imaging.\" arXiv preprint arXiv:2503.08710 (2025). We hope this clarifies why we chose simple binary image datasets rather than more complex image datasets to demonstrate the feasibility of our novel experimental method, and we believe it also addresses the reviewer’s concerns about the effectiveness of the proposed algorithm on real data.\n \n**Weakness & Limitation 2: Experimental results are not sufficiently convincing. The compared algorithm Moore-Penrose Psuedo Inverse should not be counted as a compressive-sensing reconstruction algorithm. More advanced algorithms should be compared in experiments.**\n\nRegarding the concerns about the lack of comparison with other advanced algorithms, we would first like to clarify that our primary goal was to demonstrate that the Ghost-GPT model enables fast, high-fidelity imaging when combined with our novel experimental method using dual-comb interferometry, rather than to claim superiority over all DL models. Nevertheless, since the submission, we have compared our model with CNN, U-Net, ViT (on which our Ghost-GPT model is based), and other DL models using the same simple binary datasets. These initial results show that transformer-based models generally perform better in terms of training speed and convergence. However, a more rigorous comparison will require testing the models with a larger number of speckle patterns and more complex datasets, as discussed above.\n\n**Weakness 3: It would be better if the authors could further clarify the difference between optical fiber endomicroscopy between classical compressive-sensing problems, i.e., in aspects of image prior, encoding matrix, input measurements, and so on.**\n\nCompared to previous compressive imaging demonstrations that use spatial light modulators or digital micromirror devices to generate a large number of imaging basis patterns, our dual-comb compressive imaging approach for optical fiber endomicroscopy uses a limited number of speckle patterns due to experimental constraints (though this is not a fundamental limitation) and minimally invasive optical front-end hardware (e.g., single-core fiber and a single-pixel detector). To enable video-rate imaging with such hardware-size constraints, we adopted dual-comb interferometry for fast snapshot detection of bucket-sum signals. Additionally, because of the limited number of available speckle patterns in the current experiment, our algorithmic analysis was restricted to simple binary image datasets.\n\n**Limitation 3: The effectiveness of the proposed algorithm on real data has not been demonstrated.**\n\nThe model is trained on simulated bucket-sum measurements using known image datasets and the exact speckle patterns used experimentally, and is then applied to real bucket-sum data acquired through dual-comb measurements to reconstruct the test target image. This real-world optical experiment is conducted to evaluate the feasibility of dual-comb interferometry in ghost imaging, not to test the Ghost-GPT algorithm itself. Due to the limited number of speckle patterns, we used simple binary datasets for model training and simple binary patterns (e.g., the USAF1951 resolution target) as real-world target objects in the current work. However, as discussed earlier, with an increased number of speckle patterns, we will be able to use more complex grayscale image datasets for model training, and we expect to image real biological samples.\n \n**Answers to the reviewer’s questions:**\n \n**Question 1:  What are the characteristics of the observation matrix in this problem. The authors should give a brief discussion on this, because the observation matrix strongly influences the quality of reconstruction.**\n\nIn our compressive imaging work, the set of uncorrelated speckle patterns constitutes the observation matrix. This observation matrix is random and incoherent, with non-negative real-valued elements. The correlations between the speckle patterns are shown in Figure 2(b). The number of uncorrelated speckle patterns is limited by the number of comb lines and the speckle correlation bandwidth, and this finite number of uncorrelated patterns imposes a limit on the achievable resolution and reconstruction quality.\n \n**Question 2: In Table I, the authors compare the computational time of different algorithms but do not specify the platform that these algorithms run on. Do these algorithms run on a CPU, GPU? or some of them run on a CPU and some of them run on a GPU?**\n\nThe algorithms were run on a Linux workstation using an AMD Ryzen Threadripper 3990X 64-Core Processor and a single NVIDIA A6000 GPU. To be more specific, the differential ghost imaging (DGI) as well as our Ghost-GPT model ran on GPU. The pseudoinverse (PI) and fast iterative shrinkage-thresholding algorithm (FISTA) ran on CPU. We will make these distinctions clear in the revised manuscript. \n\nWe will also include updated tests on GPU enabled versions of these algorithms for a fairer comparison. Initial tests on a PyTorch-GPU version of the PI show a computational time of 157 ms +/- 3.27 ms, while a GPU version of FISTA through the pylops_gpu library show worse performance at an average computational speed of 10.6 seconds +/- 60.2 ms. We attribute this discrepancy in FISTA primarily to data transfer overheads between CPU and GPU in the pylops_gpu implementation.\n\nDespite these updated benchmarks, our original claim remains valid: Ghost-GPT model provides the fastest reconstruction speed among the compared approaches.\n \n**Question 3: Experiments are conducted on simulated data. The performence of the proposed algorithm on real data is not clear.**\n\nThe Ghost-GPT model was trained using simulated data and tested on both simulated data (Figure 4) and experimental optical measurement data (Figure 5). Because the number of speckle patterns was limited to approximately 200, we used the USAF1951 resolution target pattern for the optical experiment, as it is simple enough to reconstruct with a limited number of imaging basis patterns. As shown in the 2025 arXiv paper, we expect that grayscale imaging will be possible with an increased number of speckle patterns (1,000–10,000) and more realistic, complex datasets. Increasing the number of uncorrelated speckle patterns in the dual-comb experiment is an ongoing effort in our lab."}, {"review_id": "BMBENb4QX9", "reviewer": "Reviewer_QCan", "summary": "This work introduces a method for endoscopic imaging using ghost imaging. Building on a large corpus on existing work on ghost imaging, the key contribution of this work is a transformer-based reconstruction method that allows them to sample at relatively low rates. This makes it possible for the authors to design sensing matrices that allow for moderate resolution imaging through a single fiber. The method is compared to conventional compressed sensing methods that have been proposed in the earlier phases of this research direction.", "questions": "- How does the proposed transformer compare to existing transformers proposed for ghost imaging? The design seems to follow vanilla transformers. Please clarify.\n\n- What is the runtime/efficiency of the method? \n\n- Which application warrants the evaluation on the low-resolution images discussed in the paper?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths: \n- The paper is well-written and provides a comprehensive overview of ghost imaging and speckle-based compressive imaging. \n- The optical setup and experiments are well executed and, at low resolution, convincingly demonstrate that the method works as advertised.\n\nWeaknesses: \n- The reconstruction network is not evaluated in detail and ablation experiments are missing in the manuscript. \n- The novelty of the method is limited as there is a large body of work that focuses on transformer/GPT-like reconstruction methods for Ghost imaging, some are listed below:\n\nRen, Wenhan, et al. \"Ghost translation: an end-to-end ghost imaging approach based on the transformer network.\" Optics Express 30.26 (2022): 47921-47932.\n\nLiang, Jiayuan, Yu Cheng, and Jiafeng He. \"Transformer-based flexible sampling ratio compressed ghost imaging.\" Engineering Analysis with Boundary Elements 170 (2025): 106050.\n\nChen, Y., An, H., Sun, Z., Tian, T., Chen, M., Spielmann, C., & Li, X. (2025). Large model enhanced computational ghost imaging. arXiv preprint arXiv:2503.08710.\n\nHe, Y., Zhou, Y., Yuan, Y., Chen, H., Zheng, H., Liu, J., ... & Xu, Z. (2022). TransUNet-based inversion method for ghost imaging. Journal of the Optical Society of America B, 39(11), 3100-3107.\n\nManni, Mathieu, Dmitry Karpov, K. Joost Batenburg, Sharon Shwartz, and Nicola Viganò. \"Noise2Ghost: Self-supervised deep convolutional reconstruction for ghost imaging.\" arXiv preprint arXiv:2504.10288 (2025).\n\nWang, Mingcong, Linqing Jang, Hexiao Wang, and Minghui Ma. \"Computational Ghost Imaging via Speckle Illumination and Deep Learning Under Low Sampling Rates.\" In 2024 5th International Symposium on Computer Engineering and Intelligent Communications (ISCEIC), pp. 567-572. IEEE, 2024.\n\nNone of these existing methods are evaluated and compared to. The evaluation is focused on outdated methods that unfortunately offer only strawman comparisons for the method.\n\n- Esoteric application: The application demonstrated in this work is rather esoteric (see related work above) and offers little insight into the scientific findings that help design better reconstruction algorithms, too. With the low resolution results demonstrated in this work, the method is also not practical.", "was_revised": true, "final_justification": "The authors provided additional experimental validation. As such, I have increased my rating. While the margins are still relatively incremental, the additional experiments are promising.", "rebuttal": "Dear Reviewer Qcan, \n\nThank you for your insightful review and feedback. In this rebuttal, we would like to clarify the novelty of our work and explain the constraints imposed by the experimental setup. Below, we address your comments and questions:\n\n**Weakness 1: The reconstruction network is not evaluated in detail and ablation experiments are missing in the manuscript.**\n\nWe appreciate the reviewer’s suggestion regarding ablation studies. Our primary objective in this work was to demonstrate that the Ghost-GPT model enables fast, high-fidelity imaging when combined with our novel experimental approach. Due to the limited number of comblines in our initial setup, the dataset complexity was constrained, making it less suitable for a broad range of ablation studies. As a result, we focused on analyzing the most critical components of our model architecture—namely, the number of attention heads and the size of the embedded speckle patterns. This analysis is presented in Appendix C (Figure 7), where we show that increasing both of these factors leads to improved model performance.\n\nWe agree that a deeper study of our architecture would be valuable, such as comparisons with other state-of-the-art models (Diffusion, CNN, U-Net), examining the effects of removing speckle pattern embeddings, or experimenting with alternative loss metrics. In our ongoing work, we address these points. Initial results show that our transformer model outperforms CNN and U-Net variants of similar size by approximately 10% in mean squared error on the validation set, and surpasses a Diffusion-based variant by over 50%. Additionally, removing the speckle pattern embedding led to unstable training and failure to converge. We have also explored alternative loss functions including total variation (TV) loss and Multi-Scale Structural Similarity Index Measure (MS-SSIM). It is also observed that the regularised loss function with the optimised weights for each term gives a much better reconstruction. We will incorporate these experiments in the updated manuscript.\n\n**Weakness 2: The novelty of the method is limited as there is a large body of work that focuses on transformer/GPT-like reconstruction methods for Ghost imaging, some are listed below:**\n\nThank you for this important comment. While prior studies have applied transformer architectures to ghost imaging, our work introduces two key novelties. First, the use of dual-comb interferometry in optical fiber ghost imaging enables fast, video-rate imaging even with zero-dimensional optical front-end hardware (e.g., single-core fiber and a single-pixel detector). Without dual-comb interferometry, achieving video-rate (greater than 30 FPS) ghost imaging would be extremely challenging, if not impossible. Second, our Ghost-GPT algorithm achieves high-fidelity, rapid image reconstruction from a limited number of fiber-generated speckle patterns.\n\nMany existing works, including the six papers listed by the reviewer, assume or generate a large number of speckle patterns or structured light patterns via spatial light modulators (SLMs) or digital micromirror devices (DMDs). SLMs and DMDs produce relatively simple binary light patterns, which are easier to model and reconstruct compared to the complex and chaotic speckles produced by our optical fiber system. However, the bulkiness and slow switching speeds of SLMs and DMDs render them impractical for endoscopic applications.\n\nCompared to SLMs and DMDs, generating a large number of uncorrelated speckle patterns from single-core optical fibers is not technically straightforward. As the first experimental demonstration of dual-comb interferometry in ghost imaging, our setup is currently limited to about 200 speckle patterns due to experimental constraints (e.g., the FWHM of speckle correlation and the number of comb lines). In comparison, related works (e.g., Chen et al., Liang et al.) use a much larger number of speckle patterns and sampling ratios 4 to 35 times greater, with simpler speckle patterns. Therefore, direct reconstruction quality comparisons without accounting for these experimental limitations are not entirely fair. Moreover, given the significant novelty of dual-comb interferometry in ghost imaging and its potential for further experimental advances, it is difficult to directly compare our work with previous SLM- or DMD-based studies combined with transformer- or GPT-like reconstruction methods solely on the algorithmic aspect.\n\n**Weakness 3: Esoteric application: The application demonstrated in this work is rather esoteric (see related work above) and offers little insight into the scientific findings that help design better reconstruction algorithms, too. With the low resolution results demonstrated in this work, the method is also not practical.**\n\nWe thank the reviewer for the feedback. We address the esoteric nature of the work in the previous comment, so we will address the low resolution results demonstrated in this work. Again, we emphasize that this is the first experimental demonstration of video-rate capable ghost imaging with dual comb interferometry, and thus, is the primary contribution of this work. While we agree that the reported resolution in the manuscript is insufficient for many practical applications, it represents an important proof-of-concept demonstration of dual comb based ghost imaging and demonstrates its potential. Furthermore, while the limited resolution is a result of the limited number of combines, it is not a fundamental limitation. We are currently working on increasing the number to 1,000~ 10,000, which will allow testing with more realistic and practical grayscale datasets.\n\n**Question 1: How does the proposed transformer compare to existing transformers proposed for ghost imaging? The design seems to follow vanilla transformers. Please clarify.**\n\nWe thank the reviewer for this important question. Although our model is based on the standard transformer architecture, we introduce task-specific adaptations to suit the unique structure of fiber based ghost imaging data. In particular, we implement a physics-aware embedding strategy that maps compressed measurements into a structured token space integrating sampling mask information and coarse positional priors, enabling more effective reconstruction in this domain.\n\n**Question 2: What is the runtime/efficiency of the method?**\n\nWe appreciate the reviewer’s interest in runtime performance. On a single NVIDIA A6000 GPU, the model reconstructs a 256×256 image in approximately 8 ms per frame.\n\n**Question 3: Which application warrants the evaluation on the low-resolution images discussed in the paper?**\n\nWe appreciate the reviewer’s question regarding the use of binary, low-resolution images. As the first experimental demonstration of dual-comb ghost imaging, we are limited to 200 comblines which constrains the achievable resolution and dynamic range. To emphasize, this resolution constraint is not a fundamental limitation of our approach. We are actively working in the lab to increase the number of comb lines to the range of 1,000–10,000, which will enable evaluation on more realistic grayscale datasets and, ultimately, complex biological tissue samples.\n\nDespite this limitation, such the current spatial resolution (on the order of millimeters) is still relevant to several practical applications. In needle-based confocal laser endomicroscopy (nCLE), probe miniaturization often results in lower-resolution images sufficient for binary detection of lesions or contrast uptake. Outside medicine, low-resolution binary imaging is widely used in industrial inspection, such as detecting missing components on printed circuit boards or contaminants on conveyor belts, where presence/absence classification is sufficient. Additionally, in embedded or radiation-constrained environments—like pipeline inspection or wearable sensors—binary, low-resolution imaging reduces data throughput enabling real-time and robust operation. Therefore, although constrained by hardware, the imaging scale remains relevant for multiple important real-world applications.\n\n\nThank you again for your constructive comments. We look forward to incorporating your suggestions to improve our manuscript."}, {"review_id": "FsHg4L763J", "reviewer": "Reviewer_TRQN", "summary": "This paper presents a novel approach to endoscopic imaging by combining dual-comb interferometry, ghost imaging, and a transformer-based reconstruction model (Ghost-GPT). The key innovation lies in leveraging optical frequency combs to generate uncorrelated speckle patterns via wavelength-division multiplexing through a single-core fiber, enabling snapshot detection with a single-pixel detector. The proposed Ghost-GPT model achieves high-fidelity image reconstruction at ultra-low sampling ratios and outperforms classical algorithms in both speed and accuracy.", "questions": "1) Is the proposed method limited to static samples, or can it also handle dynamic imaging scenarios?\n2) Are there any constraints on spatial resolution? Specifically, is cellular-level resolution achievable with this approach?\n3) Could you clarify why comparisons with state-of-the-art generative models were not included?\n4) Given that dual-comb hardware demands precise alignment, does this significantly hinder its feasibility for real-world applications?\n5) Does the model need to be retrained for different types of data? If so, how long does the training process take?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1) Combining dual-comb interferometry with ghost imaging allows snapshot imaging without mechanical scanning, making the system smaller and cheaper.\n2) Ghost-GPT is faster and produces better image quality than traditional methods.\n3) The small fiber probe and computational imaging help solve common problems with standard endoscopes, making it useful for minimally invasive diagnosis.\n\nWeaknesses：\n1) Validation is limited to synthetic data (MNIST/Omniglot) and simple targets (USAF charts), with untested performance on complex biological tissues.\n2) The reconstruction accuracy appears to rely heavily on pre-calibrated speckle patterns, which raises concerns about the system's robustness in the presence of fiber bending, mechanical perturbations, or environmental variations.", "was_revised": true, "final_justification": "The rebuttal clarified that the experiments were designed to validate the feasibility of the proposed method. This kind of interdisciplinary research is likely to attract interest from the NeurIPS community.", "rebuttal": "Dear Reviewer TRQN, \n\nThank you for your insightful review and feedback. In the following, we will address your comments and questions:\n\n**Weakness 1: Validation is limited to synthetic data (MNIST/Omniglot) and simple targets (USAF charts), with untested performance on complex biological tissues.**\n\nThank you for the comment. While the validation is limited to synthetic data and simple targets, we emphasize that this work represents the first optical experimental demonstration of video-rate ghost imaging enabled by dual-comb interferometry. Due to experimental constraints—specifically the limited number of comb lines (~200) and the speckle correlation width—we tested Ghost-GPT primarily on simple binary datasets. This limited resolution is not a fundamental limitation of the approach. We are actively working to increase the number of comb lines to 1,000–10,000, which will allow evaluation on more realistic grayscale datasets and eventually complex biological tissues.\n\n**Weakness 2: The reconstruction accuracy appears to rely heavily on pre-calibrated speckle patterns, which raises concerns about the system's robustness in the presence of fiber bending, mechanical perturbations, or environmental variations.**\n\nIn our current optical experiment, speckle patterns are generated using a hybrid optical fiber consisting of a single-mode fiber (SMF) spliced to a multi-mode fiber (MMF). Bending the SMF does not affect the speckle patterns; however, bending the MMF does affect the speckle pattern and requires recalibration. To improve the robustness of the generated speckle patterns, we are exploring an all-single-mode fiber approach by coating the SMF tip with either a rough multi-mode scattering layer or multi-layered metasurfaces to create a compact wavelength-dependent speckle/structured light generator. We expect this method to minimize the effects of environmental variations and improve the stability and reliability of the generated speckle patterns. Please refer to Shin, J et al., \"Single-pixel imaging using compressed sensing and wavelength-dependent scattering,\" Opt. Lett. 41, 886-889 (2016), who used a scattering layer of TiO2  to achieve wavelength dependent scattering on a SMF with a 0.1 nm spectral step size. \n\n**Question 1: Is the proposed method limited to static samples, or can it also handle dynamic imaging scenarios?**\n\nAs aforementioned, the greatest advantage of our dual comb interferometry setup is that we are able to achieve video-rate capable imaging. We verified this in our most recent work by moving a USAF target at a rate of 2.4 mm/s and recorded a 60 FPS movie of the target moving using our experimental optics setup. In addition, our most current results show imaging frame rate up to 1000 Hz. \n\n**Question 2: Are there any constraints on spatial resolution? Specifically, is cellular-level resolution achievable with this approach?**\n\nSpatial resolution is primarily determined by the speckle grain size; smaller grains yield higher resolution. By adjusting the fiber position, we can dynamically control grain size and thus resolution. Previous work by Zhu et al. demonstrated cellular-level resolution in ghost imaging via a strong magnifying objective lens (20x - 40x) in conjunction with a digital micromirror device (DMD). Similarly, our setup can achieve this by placing a strong objective lens at the fiber tip—though this reduces modularity. \n\nTo maintain modularity while achieving cellular resolution, we propose integrating a metalens—a flat, nanostructured ultra-thin lens—at the fiber tip to focus light using subwavelength structures. Previous works in metamaterials by Khorasaninejad et al, show that metalens are capable of achieving magnifications up to 170x, highlighting their potential to enable compact, cellular-resolution imaging at the tip of an optical fiber.\n\nReferences :\n\nZhu, X.-H., et al. “Live cell imaging and classification via microscopic ghost imaging.” Phys. Rev. Applied, 23(5), 2025.\n\nKhorasaninejad, M et al., Metalenses at visible wavelengths: Diffraction-limited focusing and subwavelength resolution imaging. Science, 352, 1190-1194(2016)\n\n**Question 3: Could you clarify why comparisons with state-of-the-art generative models were not included?**\n\nRegarding the lack of comparison with other advanced algorithms, we would like to clarify that our primary goal was to demonstrate that the Ghost-GPT model enables fast, high-fidelity imaging when integrated with our novel experimental approach. Additionally, the limited number of comb lines in our current experimental optics setup constrained the dataset complexity, making it less suitable for comparisons with other advanced generative models using complex grayscale image datasets.\n\nHowever, we agree that such comparisons would be valuable. Since submission, we have conducted these comparisons and found that our transformer-based Ghost-GPT model outperforms U-Net, CNN, and diffusion models of similar size in terms of training speed and reconstructed image fidelity. Specifically, our transformer model outperforms CNN and U-Net variants of similar size by approximately 10% in mean squared error on the validation set, and surpasses a Diffusion-based variant by over 50%. We will include these results in the camera-ready manuscript.\n\n**Question 4: Given that dual-comb hardware demands precise alignment, does this significantly hinder its feasibility for real-world applications?**\n\nThe dual-comb is generated from a robust fiber-connectorized system and delivered to the optical fiber probe via single-mode fiber, requiring no precise alignment. The level of alignment needed is not significantly different from that of conventional ghost imaging. While alignment between the single-core optical fiber probe and the single-pixel detector remains important in the current transmissive configuration, a reflective configuration—integrating the single-core fiber with the single-pixel detector and a beam collimator to detect the reflected beam from the target object—could make beam collection much more efficient without precise alignment. With such a reflective-mode optical fiber probe, our dual-comb compressive imaging approach is feasible for real-world applications. \n\n\n**Question 5: Does the model need to be retrained for different types of data? If so, how long does the training process take?**\n\nOur model trains in approximately 1 hour and 30 minutes for 50 epochs on a single NVIDIA A6000 GPU. However, as shown in Figure 7b, performance saturates after about 10 epochs, meaning training time can be reduced to ~15 minutes without significant loss in reconstruction quality. When adapting to new data — particularly different speckle patterns — the model does not require full retraining. As shown in Appendix D, Figure 9c, initializing with pretrained weights allows the model to converge on a new speckle configuration in just 1–2 epochs, requiring only a few minutes. This demonstrates that the method is both computationally efficient and highly adaptable to different measurement conditions.\n\n\nThank you again for your thoughtful review. We look forward to improving our manuscript with your valuable suggestions."}]

Author rebuttal:

Meta-review: After the rebuttal, the reviewers were mostly satisfied with the authors’ responses and upgraded their ratings.
The paper ultimately received three acceptances and one borderline rejection.
As AC, I support the recommendation for acceptance.
The authors are encouraged to incorporate their rebuttal responses into the camera-ready version.
Decision: Accept (poster)