OCTDiff: Bridged Diffusion Model for Portable OCT Super-Resolution and Enhancement
Authors: Ye Tian
Angela McCarthy
Gabriel Gomide
Nancy Liddle
Jedrzej Golebka
Royce Chen
Jeff Liebmann
Kaveri A. Thakoor
Venue: NeurIPS 2025 spotlight ()
Keywords: Super-resolution
Medical Imaging
Diffusion Model
Noise Aggregation
Computer Vision
Primary area: machine_learning_for_sciences
TL;DR: We propose OCTDiff, a bridged diffusion model with Adaptive Noise Aggregation and Multi-Scale Cross-Attention, trained with quality-guided loss. It achieves SOTA enhancement on portable OCT images and shows potential for broader clinical use.

Abstract: Medical imaging super-resolution is critical for improving diagnostic utility and reducing costs, particularly for low-cost modalities such as portable Optical Coherence Tomography (OCT). We propose OCTDiff, a bridged diffusion model designed to enhance image resolution and quality from portable OCT devices. Our image-to-image diffusion framework addresses key challenges in the conditional generation process of denoising diffusion probabilistic models (DDPMs). We introduce Adaptive Noise Aggregation (ANA), a novel module to improve denoising dynamics within the reverse diffusion process. Additionally, we integrate Multi-Scale Cross-Attention (MSCA) into the U-Net backbone to capture local dependencies across spatial resolutions. To address overfitting on small clinical datasets and to preserve fine structural details essential for retinal diagnostics, we design a customized loss function guided by clinical quality scores. OCTDiff outperforms convolutional baselines and standard DDPMs, achieving state-of-the-art performance on clinical portable OCT datasets. Our model and its downstream applications have the potential to generalize to other medical imaging modalities and revolutionize the current workflow of ophthalmic diagnostics. The code is available at https://github.com/AI4VSLab/OCTDiff.

OCTDiff: Bridged Diffusion Model for Portable OCT Super-Resolution and Enhancement
Ye Tian', Angela McCarthy', Gabriel Gomide Nancy Liddle?, Jedrzej Golebka Royce W.S. Chen Jeffrey M. Liebmann Kaveri A. Thakoor
Department of Biomedical Engineering, Columbia University, New York, NY, USA Department of Computer Science, Columbia University, New York, NY, USA
3 Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY, USA {yt2793, k. (cakoor)@coluum ceccouubiaa...m edu
Abstract
Medical imaging super-resolution is critical for improving diagnostic utility and reducing costs, particularly for low-cost modalities such as portable Optical Co- herence Tomography (OCT). We propose OCTDiff, a bridged diffusion model designed to enhance image resolution and quality from portable OCT devices. Our image-to-image diffusion framework addresses key challenges in the condi- tional generation process of denoising diffusion probabilistic models (DDPMs). We introduce Adaptive Noise Aggregation (ANA), novel module improve denoising dynamics within the reverse diffusion process. Additionally, we integrate Multi-Scale Pross-Attention (MSCA) into the U-Net backbone capture local dependencies across spatial resolutions. To address overfitting on small clinical datasets and to preserve fine structural details essential for retinal diagnostics, we design a customized loss function guided by clinical quality scores. OCTDiff out- performs convolutional baselines and standard DDPMs, achieving state-of-the-arr performance on clinical portable OCT datasets. Our model and its downstream applications have the potential to generalize to other medical imaging modalities and revolutionize the current workflow of ophthalmic diagnostics. The code is available https//ggituu.ccmmAALLLLaLL//CCC
Introduction
Medical image analysis has been transformed by recent deep learning advances, including disease classification (T, tissue and cellular segmentation [3]. and contrast enhancement (4). However, most existing models are trained on data from high-end imaging systems, making them Tess effective and even unusable in low-resource or point-of-care settings. One critical domain with this limitation is ophthalmology, where high-quality Optical Coherence Tomography (OCT) systems cost over 50,000 dollars and weigh more than 50 pounds, significantly reducing their accessibility in underdeveloped regions.
Convolutional neural networks (CNNs) and generative adversarial networks (GANs) have been widely applied for OCT image enhancement tasks including super-resolution (SR) and denoising. For instance, an early approach (5) built on ESRGAN [6] and MedGAN [7) demonstrated promising visual improvements
simulated low-resolution OCT images. However, it suffered from mode collapse and generated unwanted artifacts; hence, its generalizability to real-world clinical scans remains untested. CNN-based super-resolution methods have also been applied other medical imaging modalities such as computed tomographpy (CT) and magnetic resonance imaging (MRI) Though quantitative performance improves, these models tend to hallucinate fine details and
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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fail to preserve delicate anatomical structures, which are essential for accurate diagnosis. Medical visual tasks for diagnostic assistance such as anatomical segmentation and motion correction have achieved clinically-usable performance [T0) [1]. but SR remains challenging due to the inherent low contrast of low-resolution images and the scareity of large-scale patient datasets with low-resolution
and high-resolution pairs, despite well-established SR benchmarks natural images [12]
Commercial OCT
High-Reaoltiin Resolution Scan
Dommtrram Clasification
Nynthatic Scan
Control
Hist Match
m >
Trained Clinician
Conv PSF DSx4
Glaucoma
OCTDiff
Y InFuion
AMD
Registraion
Preprocess
Portable OCT
Low Resolution Scan
Super-resohed Scan
Figure I: Pipeline of our study. The clinical low-resolution dataset was captured with portable OCT device and then underwent necessary filtering, preprocessing, and registration. The synthetic dataset [S] was obtained by matching the histogram of the commercial OCT data to that of the portable OCT data, convolving the resulting data with the point spread function (PSF) of the portable OCT, and then downsampling the data by factor of 4 [6]. OCTDiff super-resolves both datasets and generates high-resolution outputs for downstream classification of ophthalmic diseases such as glaucoma and age related macular degeneration (AMD).
Diffusion- based generative modeling, i.c. denoising diffusion probabilistic models (DDPMs) [[3] has demonstrated superior performance over convolutional models and GANs in terms of both generative quality and training stability [14|55] I5). DDPM variants such as latent diffusion models (LDMs) [16] and conditional DDPMs (CDMs) [7] extend the power of DDPMs by incorporating guided conditioning to generate more controlled outputs. However, they are still not guaranteed to reliably translate images between different domains when conditioning
target image. A more recent approach, the bridged Brownian diffusion model (BBDM) [18), emphasizes conditioning by directly using the reference image as the initial point in the reverse diffusion process. This architecture is particularly well-suited for image-to-imane Nevertheless, its potential for super-resolution and its effectiveness when training medical images, which are small in dataset size and large in spatial resolution [99] remains relatively unexplored.
tasks such as style transfer and semantic synthesis.
To address these limitations, we propose OCTDiff, image-to-image super-resolution conditional diffusion model for portable OCT enhancement. OCTDiff builds upon the bridged framework originated from BBDM. We propose (ANA) algorithm that stabilizes the reverse denoising trajectory by aggregating noise predictions from previous time steps in the reverse process; and (2) Multi-scale backbone that enables cross-resolution feature interaction between encoder and decoder to better capture both global anatomy and fine retinal details. (3) During model training, also introduce a custom loss function with modulation from a clinical quality score to guide the model toward perceptually and diagnostically meaningful outputs.
novel components: (1) Adaptive Noise Aggregation
(MSCA) UNet
In summary, our main contributions are:
We propose OCTDiff, which significantly outperforms baseline methods in both quantitative met- rics and qualitative structural fidelity on real-world OCT datasets. The computational efficiency and training speed also preserved, demonstrated in Section 4.
We are the first to address the semantic misalignment issue 20 in conditional DDPMs through temporal fusion across denoising steps, enabling stronger conditioning guidance throughout the generation process. This strategy is uniquely feasible within the bridged diffusion framework, where the conditioning image directly anchors the reverse process.
Clinically, OCTDiff pioneers new direction at the intersection of Al and healthcare, tailored for ultraloo-rrsoolution images from portable devices. Its utility in downstream classification tasks (Section 4.3) shows OCTDiff's potential to deliver affordable and reliable vision care to

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under-served and remote populations, and to generalize to other resource-constraanne medical imaging settings.
2 Related Work
Heuristic Optimization in Diffusion Models Early improvements in diffusion models relied
heavily on heuristic strategies for loss weighting, noise scheduling, and sampling. Hybrid loss
designs [1 22 23 24 combine pixel-wise noise prediction with latent-space objectives to balance
reconstruction accuracy and pereeptual realism. For noise scheduling, linear (25) or cosine S
schedules commonly used, while more recent works [26] 27 propose handcrafted tuning based on empirical settings that better align with DDPM's denoising capacity across timesteps. Additionally, timestep reweighting prioritizes intermediate steps where gradients are more stable [21] 28 29
30). These heuristic strategies improve sample diversity and convergence without modifying model architecture, but they inherently static and task-agnostic, often relying fixed schedules and an assumption that certain timesteps are more important than others. In contrast, our Adaptive Noise Aggregation (ANA) module in OCTDiff dynamically aggregates multiple denoising predictions across timesteps, assigning different noise schedules to different images. This temporal fusion captures complementary information from various noise levels that represent multi-scale features, which is conceptually similar to ensemble learning. ANA only realizable within a bridged diffusion framework, being particularly effective for conditional tasks such as super-resolution and reconstruction for degraded OCT images.
Learnable noise modulation MuLAN [1] learns per-sample noise schedules by predicting the optimal noise scale a for each input, while other methods [32 33 use auxiliary networks or learned noise embeddings to refine denoising behavior. ANA also aims to improve signal-noise alignment but achieves this through temporal fusion rather than direct modulation. Unlike Mul.AN's learned noise schedules, ANA adapts noise dynamically per image without introducing additional supervision, making it well-suited for resource-constraingg handling highly variable noise patterns compared MuLAN, it offers a simpler and more efficient solution, particularly for OCT scans that require consistent, low-complexity adaptations.
applications. While ANA may sacrifice flexibility in
Attention and Diffusion Recent works increasingly integrate attention mechanisms into diffusion models. Transformer- based architectures such as latent diffusion [16), ImageCraft (34), and CDM [35] incorporate self- and cross-attention enhance spatial coherence. Other designs introduce hier- archical [36]. spatially-aware (37), or multi-scale attention 38 39] to improve structure preservation while maintaining efficiency. Some models further combine attention with guidance signals (e.g., text, edge maps) for stronger control 40 41). While prior multi-scal works stack features within encoder or decoder branches or process them in parallel at single resolution, our MSCA introduces explicit cross-attention between encoder and decoder features at different scales, enabling context-aware guidance across resolutions.
Temporal Ensembling and Recurrent Denoising We introduce ANA as a regularizing prior across timesteps, inspired by previous aggregation algorithms. For example, temporal ensembling reduces prediction variance and enhances consistency in semi-supervised learning [42). and recurrent
denoising benefits from multi-step integration avoid error explosion [26). The idea of combining
global and local features in medical images also resembles multi-scale fusion of frequency components via weighted aggregation [43). Our work is also distinctive from iifffrential-equation-- 44 because ANA is discrete-time, controllable (with attenuation a:) and operates during inference (not training). Also rather than modifying the noise sampler like DPM-Solver (45), ANA assumes
methods
fixed noise schedule (c.g., linear cosine) and aggregates the previously-sample noise vectors.
3 Method
3.1
Adaptive Noise Aggregation
Our OCTDiff builds upon bridged diffusion [18 that learns the translation between two image domains directly through a bidirectional diffusion process. We propose Adaptive Noise Aggregation (ANA) strategy improve the stability of the reverse denoising process. Noise predictions at
3

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different time steps t encode complementary information particularly for highffrequency retinal details. Aggregating different noise levels basically leverages different image scales' information. ANA with adaptive weights enhances fine structure reconstruction, especially when the input is severely degraded as in portable OCT scans.
The reverse process starts from the high-resolutien condition žy and iteratively estimates intermediate latent states &, recover clean image to over 'I' denoising steps. each step the model defines the reverse transition as:
po( (141, v) ~ N (1o( Eg(Fe+1,4,,)
where pe is learned Gaussian distribution with parameters predicted by the U-Net backbone. The noise component is estimated as é, (1(111 t, 1). where Eg is the noise prediction network. This predicted noise é is then used reconstruct an intermediate clean estimate 46. which subsequently informs the mean function μo ( () in the reverse transition.
To improve the robustness of the reverse process, ANA algorithm does not rely solely the current noise prediction. Instead, it adaptively aggregates noise predictions from all previous time steps in the reverse process {&, [T=1 using exponential decay. This temporal fusion yields a more stable and informative estimate 6t- The aggregation is formally defined as:
1 T-1
6,
Σ Zz 7=t
expl axp(-a(r= - ()) Er.,
where the weight function w(r, () exp(-a(1 ()) introduces time-decay prior, emphasizing
predictions closer to step / while still leveraging long-range information. The scalar a > controls how fast the weight decays over time. We discuss the impact of different values in ablation study (Section 4.2 The normalization term Ze ensures the aggregated weights sum to 1: >7-1 w(r.t) 1. This updated e, forms soft temporal fusion of noise estimates.
Z.
An intuitive explanation for ANA is the exponential moving average (EMA) 46 We adapt the EMA principle to the spatially-structuree noise tensor space, making ANA temporally-aware ensemble over latent signals within diffusion. In OCTDiff, the forward process generates a sequence of interpolated images ("bridges") between the low-resolution input and high-resolution target. This bridging mechanism ensures all intermediate steps are structurally meaningful and semantically anchored. Therefore, aggregating noise predictions across time more stable and informative here. On the contrary, in standard DDPMs, reverse steps are inherently noisy in early steps and often sensitive to prediction errors, which makes temporal aggregation less stable.
The proposed ANA strategy enables the model to leverage not only the current noise prediction but also aggregated predictions across future steps. This stabilizes the reverse trajectory and enhances final image quality. The ANA algorithm is summarized in Algorithm 1.
To improve robustness at early stages (t > T/2), we apply gradient- based refinement:
Refinement Module:
Algorithm 1 Adaptive Noise Aggregation (ANA)
Require: High-resolution input br = y. total steps T
é + E -1 Ve, Laeeniise
Ensure: Super-resolved output 20
where is a small step size and Ldemosse denotes pixel-level loss against a pseudo-ground truth.
1: for = 1 to do
2: é t Eg 1.)
3:
if > T/2 then
Reconstruction Module: Given de+1 and the aggregated noise é,, version rule:
4:
? < Refine( V Cdcmoise)
the DDIM [47 in-
5:
end if
6:
E
7, -T=
- 1)) &r
#
Vit (81+1
VI āi &t)
7:
2/ Reconstruct($r+1..
8:
2, < 1o(0141, 2/
9:
end for
to approximate the clean image at step Then o is used to compute the posterior mean 2, as in the DDPM/DDDM formulation.
10: return 20

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patiil OCT lineoo
commeciaa OCT lide
Pa (Xe-11(t..))
XT
4--
Femman Process
Rovenu Process
Nowe Aggregation
Adaptive Noise Aggregation (ANA)
Figure 2: The Adaptive Noise Aggregation (ANA) process in reverse diffusion.
3.2 Multi-Scale Cross Attention
We implement multi-scale cross-attention (MSCA) the UNet backbone of our OCTDiff model. The MSCA enables encoder features each scale to attend decoder features different scales. This cross-scale interaction is particularly important for our super-resolution task on OCT images. Medical image
such OCT often contain crucial diagnostic patterns in small, localized patches observed at different scales. For example, at a scale of 32 x 32, only the vessels may be visible, while 128 128, the scan may show larger structures like the retina and tissue. The MSCA helps preserve fine retinal structures while capturing global context in OCT.
For each encoder query Qene at scale E Sence the attention is computed with key-value pairs (Ki dect ViE from different scales 8' E Saec where s' * The attention is computed by the softmax of the scaled dot product between the query and the key. The output of the attention is then added to the original query Qene to preserve the residual connection. (Eq. (3) depicts the MSCA mechanism:
Qine (K VC
Qenc
Σ
Σ
Qeno Softmax
Vas
SES
8' S'e 8 st
3.3 Loss Function with Clinical Quality Score
Due to the physical constraints of portable OCT devices, some acquired OCT scans are highly degraded. We thus incorporate clinical expert knowledge into model training by introducing quality-aware loss function. Each high-resolution training image is assigned a perceptual quality score s(8) quality derived from subjective ratings provided by ophthalmologists. These ratings (e.g., from 1 to 10) aggregated via voting and normalized to form continuous score, reflecting the perceived clinical value each target OCT image. We design a focal-style loss that enables high-quality scans to have larger impact during model training. This is formulated perceptual quality modulated mean squared error:
N Σ (1- quality) )" (x; 2.)2
MSEfocal
Here, 7 focusing parameter that controls the degree which high-quality samples prioritized, usually being set [-2, -1]. The modulation term (1 s(1) qqallty)" prevents the model from being confused by relatively suboptimal OCT data.
3.4 Datasets and Experiments
We use two medical OCT datasets in our experiments: one synthetic and one clinical. The synthetic dataset referred as the Synthetic 500 dataset and contains 524 pairs of high-resolution simulated low-resolution OCT B B-scans, this dataset was designed to simulate the imaging characteristics portable OCT devices. The high-resolution images were acquired from Carl Zeiss Cirrus HD-OCT 5000 machine and serve as ground truth. The low-resolution scans were simulated by first matching the high-resolution seans with the intensity histogram of low-resolution scans, then convolving the histogram intogram-matched matched scans with the point spread function (PSF) of
Synthetic 500 Dataset
5

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Multiscale Cross Attention (MSCA) UNet
256+256
Query
Key Value
128x 128
L
64+64
Convolution
Downsampingg
Skip Connection
ResBlock
J
Upsampling Layer
Mutiscale Attention
ResBlock Self-Attendan Black
GrmupNom + SLU Conv
Figure 3: Illustration of the Multi-scale Cross-Attention (MSCA) mechanism. Encoder features at each scale attend decoder features different scales, enabling cross-scale information fusion.
portable OCT device, and finally downsampling the scans by a factor of 4 as done in ESRGAN 66] experiments. The Synthetic 500 dataset serves as a controlled setting for proof-of-concept evaluation and comparison with baselines to demonstrate the superiority of OCTDiff.
Philophos 84 Dataset Due to the lack of publicly available low-resolution OCT datasets, we collected our own real-world dataset with the Philophos KUOS-0100 portable OCT device. OCT B-scans were captured from patients who visited Columbia Ophthalmology from May through July 2024. To obtain paired high-resolution seans under consistent physical and clinical conditions, each participant also underwent additional OCT scan using commercial device (Zeiss Cirrus 5000) during the same visit. The portable device has significantly limited imaging capabilities: shallower imaging depth of 2.0 mm (commercial: 2.9 mm), smaller field of view 6.5 x 6.5 mm (commercial: >8 mm), and reduced spatial resolution of 562 X
1286 (commercial: >3k).
These hardware limitations result in low-resolution, high-noise, low-contrast images, often with off- centered or partially missing retinal structures. We first filtered out technically corrupted or clinically irelevant scans, then applied an unsupervised denoising approach [48] to all images. Next, affine registration was performed using ImFusion@ software [49] to align remaining scans to standard OCT template [50] and resizing to 256 for model training. Then conducted augmentation including flipping, scaling, rotation, elastic deformation, and contrast enhancement. After preprocessing, we obtained 504 paired B-scans from 84 patients, forming the Philophos 84 Dataset that presents realistic challenges for low-quality portable OCT enhancement. OCTDiff is proposed to faithfully super- resolve these degraded scans from "unusable" to "usable". thereby making portable OCT devices clinically valuable.
4 Results
We present quantitative and qualitative results of our OCTDiff against baseline models. We conduct ablation studies to analyze the effects the ANA and MSCA modules and the quality-score informed loss on model performance, complexity, and training efficiency, including different cross-attention types and the exponential decay rate ANA. demonstrate real-world applicability, we perform downstream disease classification using images generated by OCTDiff, comparing results with those from original low-resolution and target high-nesolution scans. Finally, we discuss the limitations of approach and directions for future work.

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Input
SRCNN
VDSR
CycleGAN
Swin2SR
CDM
BBDM
OCTDiff (Ours)
GT
21
2380
18.1
OS
25.6
Figure 4: Two examples of reconstructed images from baseline models and proposed OCTDiff. The first row shows a left-eye (OS) scan, and the second row shows a right-eye (OD) scan. The first and last columns correspond the input low-resolution image and the ground truth (GT) high- resolution image, respectively. Baseline methods include SRCNN [5T], VDSR (52), CycleGAN [53]
Swin2SR (54). CDM [35]. and BBDM [18). Red boxes highlight regions with notable degradation or artifacts compared to GT. Each image is annotated with its BRISQUE score 55 to quantify perceptual quality.
Table I: Quantitative comparison of models on Philophos 84 and Synthetic 500 datasets. Arrows indicate the desirable direction for each metric.
Philophos 84 Dataset
Synthetic 500 Dataset 5
Model
SSIM% t
PSNR
LPIPS 4
SSIM%
PSNR
LPIPS% $
SRCNN
39.7
18.4
49.3
91.7
28.2
9.2
VDSR
26.1
17.9
47.7
85.2
33.0
12.7
CycleGAN
58.4
29.2
28.3
95.3
30.3
17.9
Swin2SR
78.8
35.9
18.3
96.9
38.2
4.7
CDM
71.9
33.2
31.5
98.6
34.2
14.3
BBDM
87.2
35.3
27.9
98.1
42.7
6.2
OCTDiff(our))
93.6
38.8
16.1
98.9
41.0
1.7
4.1 Performance
OCTDiff is trained and tested separately on the Philophos 84 and Synthetic 500 dataset. Training conducted on Lambda Labs Vector server equipped with two IVIDIAAA000 GPUs, requiring approximately 48 hours to complete from scratch with input images resized to 256x256 pixels and total diffusion time step 7 1000. For quantitative evaluation,
employ structural similarity index measure (SSIM) [56]. peak signal-to-noise ratio (PSNR), and Learned Perceptual Image Patch Similarity (LPIPS) 57 to comprehensively assess reconstruction fidelity, pixel-level accuracy, and perceptual quality, respectively.
The baseline convolutional models include SRCNN [1]] and VDSR 52
Since previous work 5
on the Synthetic 500 dataset implemented ESRGAN and MedGAN, we include CycleGAN (53) for comparison. Swin2SR is chosen as state-of-the-an transformer-based super-resolution model leveraging hierarchical self-attention and shifted windows to compare with our MSCA strategy. Regarding diffusion models, we implement CDM and the bridged model BBDM [18] foundation for our work. All models are trained from scratch under the same training strategy without pretraining to ensure fair comparison.
Quantitative Results Our OCTDiff achieves the best performance in terms of SSIM (0.936 and 0.989 on the Philophos 84 and Synthetic 500 datasets, respectively) and attains the highest PSNR of 38.8 on the Philophos 84 dataset, as shown in Table I Most baseline models perform well on the Synthetic 500 dataset and achieve over 0.95 SSIM. which comparable OCTDiff, with BBDM reaching the highest PSNR of 42.7. This is likely because the Synthetic 500 dataset preserves scaling and local structural information during the synthesis process from high-resolution to low-resolution images, making the super-resolution task relatively easier for models employing local upsampling techniques such as convolutional kernels. Model performances drop on the Philophos 84 dataset, while OCTDiff outperforms all baselines.
7

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Table 2: Ablation study on ANA and MSCA OCTDiff on Philophos 84 dataset.
Table 3: Ablation study on ANA exponential
decay rate a on Philophos 84 dataset.
ANA
MSCA
SSIM% t
Params (M)
FLOPs (G)
Q
SSIM%
PSNR 1
86.1
22.8
406.2
0.1
91.2
37.1
90.7
22.8
451.3
0.3
93.6
37.8
88.0
23.7
613.9
0.6
84.6
38.8
93.6
23.7
762.6
1.0
82.8
38.0
Qualitative Outcome Figure 4 visualizes two representative examples of outputs generated by all models on the Philophos 84 dataset. Each image annotated with its BRISQUE score (55), reference-less perceptual quality metric for which lower values indicate better visual quality. Red boxes highlight regions of structural deviation from the ground truth. Convolution based models SRCNN and VDSR fail to produce structurally stable outputs, often introducing artifacts and distorted anatomical layers. CycleGAN generates "ssuall-coherent results but tends to average out fine-grained variations, missing subtle retinal layers especially the bottom of the scan. Swin2SR produces smooth and consistent textures but over-flattens important curvature details that compromise anatomical realism. Diffusion-basee based models like CDM and BBDM better reconstruct the global retinal structure but struggle with precise local detail, leading to extra peaks, dips, or distortions around the fovea region, which critical in clinical interpretation. Our OCTDiff not only preserves global coherence but also recovers sharp structural boundaries close to the ground truth, although it cannot replicate small subject-specific variations.
4.2 Ablation Study
Impact of ANA and MSCA Modules To evaluate the contribution of the ANA and MSCA modules, we tested on different combinations of them and measured their impact model performance, size (number of trainable parameters), and training cost (in FLOPs) (58), as summarized in Table 2 Another example of output image and corresponding residual maps compared to the ground truth are shown in Figure 5 While ANA does not visibly alter the residual ratio, it effectively suppresses fundamental errors (i.e., the discontinuities in retinal layers) that occur when only MSCA is present.
ANA introduces no additional trainable parameters but increases FLOOPs, offering trade-off that results in SSIM increase. In contrast, MSCA yields more modest performance improvements but is crucial for maintaining spatial consistency, particularly for medical images that require structural integrity for diagnosis. In summary, ANA serves the primary driver of performance gain and MSCA provides structural regularization. Both components together give rise to the superior performance of OCTDiff.
Weight Decay Factor Another key hyperparameter ANA is the exponential decay rate a, which controls how quickly the adaptive noise modulation weights diminish as introduced in Section 3.1 Table reportss the results with varying a values. When a is low (c.g., 0.1), the model keeps dependency noise from earlier time steps over longer duration and yields relatively high SSIM but slightly lower PSNR, reflecting good structural preservation but less sharpness. Increasing a further (e.g., 1.0) makes the model focus the most recent two three steps. This over-smoothing causes loss fine structural details. This trade-off suggests that a moderate a achieves the best balance, and thus selected 0.3 for experiments.
Choices of Cross Attention To justify our MSCA design of using encoder features as queries to attend to decoder features, we provide an empirical analysis compare different cross attentions (CA), including unidirectional and bidirectional CA. The results are reported in Table
Reverse CA (Decoder-Encodey) performed marginally worse though it converges faster, possibly due to the decoder lacking detailed structural localization at early stages that makes the query less efficient. Bidirectional CA is computationally expensive (twice as many attention layers per scale), with limited benefits. Unidirectional CA (Encoder->Decodee yields the most significant improvement. The cost and scalability are also key impact factors for our decision when connecting two scales, as our ultimate goal is to deploy OCTDiff onto clinical OCT devices achieve real-time image processing. We thus prioritized encoder-to-decodee te-doe attention in our task.

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Table 4: Ablation study on cross attention
Table 5: Comparison of loss with and without
types in OCTDiff on Philophos 84 dataset.
quality score on Philophos dataset.
MSCA Type
SSIM% f
Params (M)
F1.OPs (G)
Metric
QS
With QS
No CA Enc - Dec CA
86.1
22.8
406.2
SSIM% 1
74.1
93.6
88.4
23.7
613.9
PSNR f
34.9
38.8
Dec + Enc CA
87.2
23.7
607.7
LPIPS +
22.4
16.1
Bidirectional CA
87.8
25.2
678.4
BRISQUE +
31.7
24.5
Quality Score in Loss Function To quantify how much the clinical input contributed to the overall performance, we compared all metrics with and without quality- loss illustrated in 3.3 The results are in Table [ the quality score significantly improved performance. This highlights how domain knowledge required for the perceptual and structural understanding of OCT images.
Input Low-res
MSCA
ANA
MSCA + ANA
GT High-Res
Residual Map
Pixels > thr (0.1): 25.44%
3.36%
2.13%
1.37%
Figure 5: Example output images when toggling MSCA and ANA. The second shows the corresponding residual maps of the images in the first row compared with GT on the right. At bottom, the ratio of pixels that have difference over 10% shown.
4.3 Downstream Disease Classification
Table 6: Accuracy (%) on downstream disease classification using high-resolution images from commercial OCT, low-resolution images from portable OCT, images generated from our OCTDiff and BBDM models, in columns 1, 2, and 4, respectively. The indicates y-value < 0.05 compared
the OCTDiff-genenan input. The numbers in the format of mean + standard deviation.
Disease Class
Model
High Res.
Low Res.
OCTDiff
BBDM
ViT
74.4 #1.9
50.1 +1.8*
74.5 #3.1
62.0 +2.5*
Glaucoma
CNN2D
93,4
83.3 +2.2*
93.5 +0.5
81.0 +1.0
SwinT
75.5
49.5 +
55.6 +4.3
57.1 +3.2
ViT
86.5 #1.9
48.9 +2.7*
85.8 #1.6
79.5 +2.1
AMD
CNN2D
94.6 +0.9*
83.0 +1.4*
96.4 10.4
91.8 #0.7*
SwinTT
02.441.4
49.4 +
66.3 +1.8
64.9 +2.6
We perform downstream disease classification using images generated by OCTDiff to directly demonstrate its real-world utility in clinical applications. To reduce model bias, we trained three classification architectures: ViT [59], vanilla CNN, and Swin'T (60) on three types of inputs: (1) high-resolution images from commercial OCT devices, (2) low-resolution images from portable OCT devices, (3) OCTDiff super-resolved images, and (4) BBDM super-resolved images. The pretrained weights on ImageNetIK K (61] are imported. We performed 5-fold cross-validation for each model-dataset pair, with the results shown in Table and we conducted Mann-Whitney U tests (62 to assess statistical significance. This evaluation was conducted independently two representative ophthalmic tasks: glaucoma diagnosis and age-related macular degeneration (AMD) classification,

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both of which are widely studied in Al ophthalmology causes of blindness worldwide.
63
64
65
66
67
as they are the top two
Among the six models trained on high-resolution images, three showed no statistically significant dif- ference, with the simplest vanilla CNN performing equally well in accuracy compared to counterparts trained on OCTDiff- OCTDiff-generated generatee images. In contrast, models trained on low-resolution portable OCT images exhibited a statistically significant drop. BBDM is selected as a representative of SR baselines, which is outperformed by OCTDiff in 5 out of rows. These comparisons highlight OCTDiff's ability to transform previously suboptimal portable OCT scans into diagnostically reliable images, indicating that our method can closely match gold-standard OCT-image quality, or even exceed the performance achieved by commercial high-resolution scans.
4.4 Limitations and Future Work
While OCTDiff demonstrates SOTA performance within each clinical dataset, the cross-dataset generalization, such as training on Synthetic 500 and testing on Philophos 84 and vice versa, still under investigation due to the limited amount of data in each set. While OCTDiff is not positioned to be a general-purpose SR benchmark, further experiments on widely used natural image SR datasets are still needed to more comprehensively prove the advantages of the OCTDiff algorithm. Additionally, although OCTDiff is effective, the model remains relatively large and computationally demanding, leading to longer training times. The cross-scale attention mechanism shows promising benefits, but its performance is sensitive to the choice of scale combinations. Currently, only scales of 128, 64, and 32 have been explored.
Future work will focus on overcoming current limitations by exploring latent-space methods [T6] such as LDM, pre- gre-training the model using natural images, and cross-dataset evaluations to further validate OCTDiff's generalizability. Toward system-level advancement, future work will include integrating OCTDiff into physical portable OCT devices using edge computing platforms like NViDIA@Jetson Orin Nano (68) to enable real-time image enhancement for point-of-care clinical applications. Safeguarding against hallucination in generated OCT images will be addressed in future work via posthoc clinical quality assessment.
We plan to extend OCTDiff to other medical imaging and broader fields. OCT shares core signal degradation characteristics, particularly speckle noise, with radiological imaging modalities such as ultrasound and low-dose CT. Also our algorithm's 's novel modules (ANA and MSCA) are not restricted to a specific image type. For these reasons, OCTDiff has potential for cross-modal generalization. There are publicly available portable OCT datasets our knowledge, and portable medical datasets are very rare in general, indicating the paradigm-shiff paves the way for the growing trend of developing tandem AI + imaging technology: making AI algorithms more scalable, interoperable, and easier to deploy within a portable form factor, enabling accessibility for the broadest populations at point-of-care.
role of Al's entry into this field. Our work
5 Conclusion
We propose OCTDiff, a bridged diffusion framework specifically designed for enhancing portable OCT images. We further propose Adaptive Noise Aggregation (ANA) to improve noise scheduling within the diffusion process, allowing the model to adaptively leverage information from multiple time steps. We incorporate Multi-Scale Cross-Attention (MSCA) to effectively capture spatial de- pendencies at multiple resolutions. To mitigate overfitting on limited clinical datasets and preserve diagnostically critical structures, we introduce customized loss function guided by clinical quality scores. Downstream disease classification demonstrates that OCTDiff significantly improves image quality and makes low-cost OCT scans clinically usable. Our work lays the groundwork to revolu- tionize the current workflow of ophthalmic diagnostics with Al's power, making it more accessible and cost-cffective, ultimately improving healthcare outcomes.
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Acknowledgments and Disclosure
The authors would like to thank Tharun Kumar Jayaprakash, Dr. Vlad DiaConita, Dr. Ives (Tony) Valenzuela, Dr. Jack Cioffi, and our funding sources: Research to Prevent Blindness, Inc., the Peacock Trust, and Columbia University's Office of the Provost.
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A Technical Appendices and Supplementary Material
Supplementaty materials will be uploaded in different PDF file.
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: We clearly state our proposal of OCTDiff, a bridged diffusion model for super- resolution and enhancement of portable OCT images, along with the introduction key components such Adaptive Noise Aggregation (ANA) 3.1, Multi-Scale Cross-Attention (MSCA) 3.2 and a customized loss function [33 with clinical quality score. The results include improved image quality, quantitative metries 4.1 downstream clinical applications 4.3 and ablation studies 4.2 The claims in the abstract are well supported by our results and align with the content presented throughout the paper.
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Justification: Our work is primarily empirical and methodological, focusing on developing and evaluating the OCTDiff model for OCT image super-resolution and enhancement. does not present new theoretical results that require formal proofs.
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Justification: We emphasized and evaluated the significant societal impacts of our work in AI healcare (Section | 4.3 and 5). OCTDiff has the potential to revolutionize the current workflow of ophthalmic diagnostics with Al's power, making it more accessible and cost-effective, ultimately improving industrial healthcare outcomes.
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Justification: All third-party assets used in the paper, including OCT imaging devices (Philophos and Zeiss) pretrained models (e.g., ViT, SwinT) and datasets (e.g.. ImageNetI properly cited with corresponding references. Their usage complies with the original licenses.
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The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset used and, if possible, include URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset. For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
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this information is not available online, the authors are encouraged to reach out to the asset's creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [Yes]
Justification: The new assets introduced (e.g. the OCTDiff model, the Adaptive Noise Aggregation (ANA) module, and -linical-quality- qquiidty-guided loss function) are thoroughly documented in the main text. In this camera-ready paper, we are now releasing the full source code with comprehensive instructions. A sample dataset will also be provided, while the full clinical dataset will be released separately due to IRB constraints
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Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?
Answer: [NA]
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Justification: Our work does not involve crowdsourcing direct research with human subjects. All human-related data (retinal OCT scans) were retrospectively collected under IRB-approved clinical protocols, with appropriate anonymization and ethical safeguards. No compensation was provided, and no participants were recruited specifically for this study.
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Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based the requirements of your country or institution) were obtained?
Answer: [Yes]
Justification: All clinical OCT data used in our study were collected under Institutional Review Board (IRB) approval number 1B--AAVV131, in accordance with ethical standards for research involving human subjects. The data were fully deidentified before AI training. No additional risks were posed to participants beyond standard OCT scanning, and informed consent was obtained as part of the data collection protocol.
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Reviews:
[{"review_id": "XMgTvj2RzW", "reviewer": "Reviewer_N6rz", "summary": "This paper focuses on super-resolution and quality enhancement of low-resolution, noisy images from ​​portable OCT​​ devices. This work improves bridged diffusion model by introducing Adaptive Noise Aggregation (ANA)​​ to stabilize the reverse diffusion process by aggregating noise predictions across timesteps, and ​​Multi-Scale Cross-Attention (MSCA)​​ integrated into the U-Net backbone to capture spatial dependencies across different resolutions. The proposed method outperforms CNN，VIT and existing diffusion based method on both synthetic and real-world datasets.", "questions": "Please respond to my concerns in the weakness. The main concerns lead to my score is the clarification on the rationale behind ANA and the justification of the designs of MSCA.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strength\n1) This paper conducts ​​exhaustive experiments​​ to validate the effectiveness of OCTDiff, rigorously evaluating it against ​​six diverse baseline methods​​ spanning multiple super-resolution (SR) paradigms. Evaluation covers ​​both synthetic and real-world clinical OCT datasets​​:\n2) The proposed ​​OCTDiff method demonstrates substantial performance gains over baselines​​, achieving ​​state-of-the-art results​​ across both synthetic and real-world clinical datasets. \n\nWeakness\n1) While Adaptive Noise Aggregation (ANA) demonstrates strong empirical results, the paper would benefit from a ​​more nuanced theoretical or intuitive explanation​​ of its core mechanism.  Why does aggregating past noise predictions​​ yield a more accurate noise estimate for the current step? ​​Why doesn’t ANA amplify errors​​ when historical predictions misalign with the current gradient?\n2) Table 4 demonstrates an interesting results on how OCTDiff enhance classification performance. It would be better to compare this results against other SR baselines. \n3) The Multi-Scale Cross-Attention (MSCA) design choice—using ​​encoder features as queries​​ to attend to ​​decoder features​​ lacks explicit justification.  Are there any other design options? Why prioritize ​​encoder to decoder attention​​ instead of bidirectional/mutual attention? Is it possible to provide some empirical analysis to compare different design choices of this cross attention.", "was_revised": true, "final_justification": "The rebuttal has fully addresses most my concerns and I lean to accept this paper.", "rebuttal": "We are thankful for your detailed review and your insightful questions. We reply to them in order below.\n\nQ(1) ANA theoretical motivation\n\n① **Intuition: noise predictions $\\hat{\\epsilon}_\\tau$ at different steps encode complementary frequency components**. \n\nFor example,  earlier steps (1000-700) comprehend a larger-scale global, low-frequency structure, and later steps (200-50) focus on local, high-frequency refinements. \n\nIn the OCT setting, early steps capture retinal global anatomy such as retinal topology and macular contour, while later steps refine microstructural elements such as RNFL layer and subtle boundary transitions.  (Figure 2)\n\nEmpirically, when we trained GAN-based models (e.g., our previous work and [Wang et al., 2018]), though SSIM was acceptable, local artifacts significantly impaired qualitative appearance. As shown in Figure 4, even CDM tends to lose global structural consistency (duplicated optic cups).  BBDM is closer, but still fails to produce realistic local features (e.g., RNFL regions highlighted in the red box).\n\nThese theoretical intuitive and empirical results let us come up with ANA - mathematically combining global and local features. This resembles multi-scale fusion of frequency components via weighted aggregation. [Liu et al., 2022]\n\n\n**② We propose ANA in the context of Bridged Diffusion** (line 46~54)\nBBDM constructs a deterministic bridge between an input image $x_0$ and a target image $y$, as illustrated in Figure 2. The forward process generates a sequence of interpolated images (“bridges”) between low-res portable OCT image  $x_0$ and high-res commercial OCT image $y$. Then during the reverse process, the model starts from $y$ and iteratively reconstructs the low-res anchor (Figure 4).\n \nThis bridging mechanism ensures all intermediate steps are structurally meaningful and semantically anchored. Therefore, aggregating noise predictions across time is more stable and informative here. Imagine a tree with branches that are tall and short, thick and thin. When all branches are combined, they form a strong and balanced whole. None of the branches is 'useless'.\n\nOn the contrary, in standard DDPMs, reverse steps are inherently noisy in early steps and often sensitive to prediction errors, which makes temporal aggregation less stable. \n\n\n③ **An intuitive explanation for ANA is the Exponential Moving Average (EMA)** [Kingma & Ba, 2015].\nWe extend EMA's principle to the spatially-structured noise prediction space, making ANA a temporally-aware ensemble over latent signals within diffusion. \n\nHowever, ANA is not a direct application of EMA (rather, it is an adaptation):\n\n-Data: EMA = scalar / vectors;  ANA =  spatially structured noise tensors\n\n-Domain: EMA = optimizer; ANA = inference in the reverse diffusion\n \nIn the camera-ready version, we plan to add more motivation and citations about ANA to support these statements.\n\n\nQ(2) Amplifying errors​​ in ANA/ANA stability:\n\nANA *does not* amplify errors because it behaves as a weighted temporal ensemble/fusion instead of a ‘recursive accumulator’.\n\n① **We have the decay factor $\\alpha$ to attenuate distant steps**.\nFor example, in eq.(2), with $\\alpha = 1.0$, weights decay as $[1, e^{-1}, e^{-2}, \\dots]$, assigning roughly 86% of total weight to the current and next two steps. This ensures that dominant guidance always comes from recent and reliable predictions.\nIn Section 4.3, we conduct an ablation study on the decay rate $\\alpha$ controlling ANA’s temporal weighting, as the results show in Table 3 and discussed in lines 238~244. We select $\\alpha = 0.3$ that achieves the highest SSIM with competitive PSNR.\n\n② **The time steps are structured upon bridged diffusion**\nAs mentioned in (1), in our OCTDiff, even ‘imperfect’ predictions at intermediate steps still carry semantically meaningful structure.  The key idea of ANA is that mid-step noise predictions, even if some provide more information than others, all contain rich contextual signals at their steps. Aggregating them enables the model to smooth out noisy or inconsistent estimates and avoid abrupt local minima.\n\n③ **Empirical results prove that ANA is bringing in meaningful context, not noise**\nAs shown in Table 2, the ablation study clearly indicates that adding the ANA module alone improves SSIM by 4.6% (from 86.1% to 90.7%). Table 1 and Figure 4 illustrate the superior robustness of OCTDiff compared to all baseline models.\n\n④ **This design is consistent with findings in temporal ensembling and recurrent denoising literature**\nThe ANA aggregation acts more like a regularizing prior across timesteps than a hard accumulation. For example, [Laine & Aila, 2017]  demonstrated that temporal ensembling reduces prediction variance and enhances consistency in semi-supervised learning; [Xie et al., 2020] showed recurrent denoising benefits from multi-step integration to avoid error explosion.\n\nThus, we conclude ANA is a smoothed, multi-scale-informed estimate of the noise $\\bar{\\epsilon}_t$, which improves robustness by reducing variance from any single $\\hat{\\epsilon}_t$. \n\n\n[Kingma & Ba, 2015] Adam: A Method for Stochastic Optimization. ICLR 2015.\n\n[Liu et al., 2022] Wavelet-based Frequency-aware Image Fusion Network. IEEE TMM 2022.\n\n[Wang et al., 2018] High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs. CVPR 2018.\n\n[Laine & Aila, 2017] Temporal Ensembling for Semi-Supervised Learning. ICLR 2017.\n\n[Xie et al., 2020]Self-training With Noisy Student Improves ImageNet Classification. CVPR 2020.\n\nQuestion（3) SR baseline missing in Table 4\nThank you. We acknowledge the importance of this analysis but did not implement downstream classification for all SR baselines. For example in Figure 4, outputs from convolutional models and CycleGAN distort clinically essential features like RPE layer and flipped RNFL, making them clinically invalid to train a disease classification model.\nWe selected BBDM baseline in the new comparison table below, as an extension of Table 4.\n| **Disease Class** | **Model** | **High Res.**      | **Low Res.**       | **OCTDiff Generated** | **BBDM Generated** |\n|-------------------|-----------|--------------------|---------------------|------------------------|-----------------|\n| **Glaucoma**      | ViT       | 74.4 ± 1.9         | 50.1 ± 1.8 **✱**    | 74.5 ± 3.1             | **62.0 ± 2.5** **✱**  |\n|                   | CNN2D     | 93.4 ± 5.0         | 83.3 ± 2.2 **✱**    | 93.5 ± 0.5             | **81.0 ± 1.0**  **✱** |\n|                   | SwinT     | 75.5 ± 2.6 **✱**   | 49.5 ± 3.1 **✱**    | 55.6 ± 4.3             | **57.1 ± 3.2**   |\n| **AMD**           | ViT       | 86.5 ± 1.9         | 48.9 ± 2.7 **✱**    | 85.8 ± 1.6             | **79.5 ± 2.1**   |\n|                   | CNN2D     | 94.6 ± 0.9 **✱**   | 83.0 ± 1.4 **✱**    | 96.4 ± 0.4             | **91.8 ± 0.7**   |\n|                   | SwinT     | 82.3 ± 1.4 **✱**   | 49.4 ± 2.4 **✱**    | 66.3 ± 1.8             | **64.9 ± 2.6**   |\n\nThe significance in Glaucoma results above may be due to RNFL distortion from BBDM output.\n\n\nQuestion (4) MSCA design choice, encoder for query and decoder for key/value. \n\n1. Yes, we explored other design choices and we picked the empirical best (table below). \n- Reverse Cross Attention (Decoder→Encoder): Performed marginally worse, possibly due to the decoder lacking detailed structural localization at early stages that makes the query less efficient (though converges a bit faster).\n- Bidirectional Attention: Computationally expensive (2× more attention layers per scale), with limited benefit.\n- Unidirectional Cross Attention (Encoder→Decoder): yields the most significant improvement. We also compared with attending more scales in our task -  the most impactful CA connections are from encoder 128 → decoder 64 and encoder 64 → decoder 32.\nWe plan to include this analysis in the camera-ready version if possible.\n| MSCA Type              | PSNR ↑ | SSIM ↑ | Params (M) | FLOPS (G) |\n|------------------------|--------|--------|-------------|-----------|\n| No Cross Attention (CA)     | 34.72  | 0.861  | 22.8        | 406.2     |\n| Encoder → Decoder CA        | 36.47  | 0.884  | 23.7        | 613.9     |\n| Decoder → Encoder CA        | 33.85  | 0.872  | 23.7        | 607.7     |\n| Bidirectional CA            | 36.61  | 0.878  | 25.2        | 678.4     |\n\nThough not explicitly covered in your question, we would like to add two more bullet points to explain:\n\n2. Conceptual motivation for unidirectional MSCA: SR is an inherently asymmetric task across scales\n-why multiscale: Different spatial scales capture complementary information. This multi-resolution fusion is widely adopted in super-resolution literature [ Lim et al., ECCV 2017 (EDSR); Zhang et al., CVPR 2018 (RCAN)].\n-why unidirectional: SR requires guidance from high-level semantic features (encoder) to reconstruct localized structure (decoder).  (Similar to [Ates et al., M 2023 (Dual Cross-Attention U-Net)])\n\n3. Rationale of choice : Computational cost and scalability is the key impact factor for the decision of connecting two scales. Our ultimate goal is to deploy AI onto clinical OCT devices to achieve real-time image processing.\n\nSelf-attention already exists in encoder and decoder branches separately. With this intra-branch attention, we want CA to directionally augment inter-branch interaction.\n\nUsing encoder as query (and decoder as key/value) ensures the model selectively enhances decoder features based on global context guide.\n\nIn conclusion, we explored MSCA options and designed it based on its empirical performance and conceptual motivation.\n\n\n[Lim et al., 2017] Enhanced Deep Residual Networks for Single Image Super-Resolution. ECCV 2017.\n\n[Zhang et al., 2018] Image Super-Resolution Using Very Deep Residual Channel Attention Networks. ECCV 2018.\n\n[Ates et al., 2023] Dual Cross-Attention for Medical Image Segmentation. Engineering Applications of Artificial Intelligence, 2023."}, {"review_id": "8MBDCSZCbQ", "reviewer": "Reviewer_AMGy", "summary": "The paper proposes OCTDiff, a novel image-to-image diffusion model aimed at enhancing the resolution of images from portable Optical Coherence Tomography (OCT) devices. Based on diffusion bridge (i.e., Brownian Bridge Diffusion Models), the authors introduce three new modules: Adaptive Noise Aggregation (ANA), which aims to enhance the noise prediction in reverse process; Multi-Scale Cross-Attention (MSCA) in U-net to capture cross-spatial relationship and customized loss function based on medical expertise. Experimental results show that OCTDiff outperforms baselines.", "questions": "See Weakness above", "limitations": "See Weakness above", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strength: \n1. Overall, I think this is a great application paper. Focusing on improving the resolution of OCT-based images is important and brings impact to the optical research community.\n\n2. The three specialized components are well-designed and effectively contribute to the overall performance improvement.\n\n3. The empirical experiments are well-justified and clearly demonstrate the effectiveness of OCTDiff, even with limited data.\n\nMain Weakness:\n\nThe main weakness, in my opinion, is the lack of sufficient novelty. First, the backbone is based on BBDMs. While the three specialized modules are well-integrated, their core ideas are already well-explored in the community. For example, in the ANA module, several existing works have studied how to leverage information from other diffusion steps to improve the current step (e.g., DPM-Solver: https://arxiv.org/abs/2206.00927). Regarding MSCA, incorporating attention mechanisms into U-Net architectures is also a common practice (e.g., DCA: https://arxiv.org/pdf/2303.17696).\n\nI also have some confusion or questions regarding the design of the ANA module. Why did you choose to incorporate all previous steps to optimize the current step's noise prediction? What is the motivation behind this choice? Since different diffusion steps contain significantly different information, wouldn’t incorporating all previous steps introduce more noise compared to using only a few recent or nearby steps?\nYou propose three specialized modules in your method, but the ablation study for your customized loss function is missing. This raises questions about how much the loss function contributes to the overall performance.", "was_revised": true, "final_justification": "Most of my concerns have been addressed.", "rebuttal": "Thank you for your detailed review and critical insight into the core novelty of our paper. We will reply to your concerns in order below.\n\nQ(1)Similar work exists as ANA that leverages previous diffusion steps?\n\nWe understand that ANA might seem conceptually similar to existing works such as DPM-Solver [Lu et al., 2022]. However, we emphasize that sharing a high-level concept does not imply method redundancy or lack of novelty, especially when the implementation, theoretical foundation and practical impact differ substantially.\n\n**①ANA is built upon the Bridged Diffusion framework**\n\nBridge diffusion (BBDM) introduces a deterministic interpolation bridge between the low-res and high-res images across time. The intermediate states have semantically meaningful and structurally consistent features (Figure 2). This inspired the temporal fusion of noise predictions across time steps(ANA).\n\nOn the contrary, the standard DDPM has noisier intermediate states and weak structural coherency. Applying ANA in DDPM would yield less impactful gains in performance as we experimented.\n\n\n**②Mechanism is different. ANA does not need to impact the sampler itself**\n\nDPM-Solver mathematically formulates the reverse diffusion sampling as a numerical ODE integration problem. It uses finite-step approximations to make their DPM-Solver-1 (= DDIM), Solver-2 and Solver 3 (using Runge-Kutta integral)\n\nIn comparison, ANA does not solve any equation or optimize the sampler itself; it simply requires an additional step to fuse the sampled noise. This also makes ANA less computationally expensive.\n\nANA works as adaptive, weighted aggregation of noise predictions.  Also, ANA has a larger temporal scope and overcomes the constrained context window of conventional solvers. ODE-based samplers have a narrow memory window due to their core of numerical integration (up to 3 steps for DPM-Solver-3). As a result, early-stage noise predictions are quickly overwritten. This limitation is evident in current Figure 4: CDM (does not retain early denoising signals) generates anatomically incorrect image with two OCT dips. In contrast, BBDM model, which inherently accumulates prior information through a brownian bridge formulation, successfully avoids this error.\n\nANA enhances the inherent connection within BBDM. The aggregation mechanism maintains a long-range temporal memory to preserve structure (global info at early and local details at later time steps), especially crucial for medical images.\n\n**③ The purpose of our ANA is to enhance image quality, not speed.**\n\nDPM-Solver accelerates the sampling. While it is a significant to inference efficiency, our ANA instead focuses on improving image quality (Table 1).\n\n**④ ANA allows flexible control via an exponential decay hyperparameter.**\n\nThis flexibility provides regulation over how much long-term influence have on the current step, which is another capability not available in solvers. We will elaborate more on this in Q(3) below.\n\n\nAs you have pointed out, we are also aware of other existing work in the community that has explored the rationale for multi-timestep information fusion.\n\n- For example, [Song et al., 2021] introduces SDE-based score models. Our work distinguishes from it as ANA is discrete-time, controllable (with $\\alpha$) and operates during inference (not training); their work is for improving generation flexibility, while ANA focuses on improving perceptual quality.\n\n- Another example, [Karras et al., 2022] proposed a stochastic-sampler-based method. Again, rather than modifying the noise schedule, ANA assumes a fixed noise schedule (e.g., linear or cosine) and focuses on how to aggregate the previously sampled noise vectors.\n\nIn contrast to these approaches above: ANA does not make a new solver. It maintains compatibility with existing solvers and is more like a ‘plug-and-play’ module. It provides control over long/short-term noise influence based on task preference. For our OCT super-resolution task, looking at ~10 previous steps hit the best trade off based on $\\alpha=0.3$ in Table 3.  \nTo our knowledge, no prior work implements such past-step noise aggregation in diffusion. We therefore claim the novelty of our ANA algorithm.\n\nAlso, while those sampler-based approaches are grounded in mathematical formulations, our OCTDiff is an algorithm design with **robust empirical results** and specific application in OCT. The downstream work to integrate our model into healthcare as a real-time image processor in clinical OCT devices further enhances its practical meaningfulness, as real-time + high-quality ‘portable imaging’ technology has been challenging without AI yet is essential for modern medical imaging.\n\n[Song et al., 2021] Score-Based Generative Modeling through Stochastic Differential Equations. ICLR 2021\n[Karras et al., 2022] Elucidating the Design Space of Diffusion-Based Generative Models. NeurIPS 2022 \n\n\nQ(2) MSCA replicates using attention in UNet?\n\nWe totally agree that attention + UNet is indeed common practice, (e.g. Dual Cross-Attention (DCA) [Ates et al., 2023]). But our implementation is still new in the way we compute attention and the exploration of MSCA choices in the setting of UNet as a diffusion backbone:\n\n**① DCA is intra-branch fusion while our MSCA is inter-branch.** \n\nThe mechanism is different. ‘Branch’ here is defined as encoder/decoder. Within each branch, DCA fuses features at different scales via patch embedding to obtain (encoder) tokens. Then, CCA/SCA is performed on these tokens. No attention to decoder is computed and DCA is inherently single-scale.\n\nThis is unlike our MSCA whose nature is inter-branch attention, directly attending encoder to decoder. Another difference is our MSCA attends different scales. (e.g., encoder 128→decoder 64)\n\nWe do share commonalities:\nWe are both unidirectional, transitioning feature flow from encoder to decoder (right arrow, their Fig 1).\nWe both implement cross-attention technique, though input and output differ.\n\nAfter differentiating our work against DCA, we would like to elaborate the uniqueness of MSCA:\n\n**②We propose using encoder as query (and decoder as key/value)** to guide decoder features based on global context. This idea is absent from previous work\n\n② MSCA in OCTDiff is a compact module **without significantly increasing time-cost or model size** (Table 2). MSCA alone improves SSIM when #trainable parameters only increases by 0.9M (<4% of original). This is critical to our ultimate goal of implementing model on a clinical OCT device to achieve real-time imaging.\n\n③ We explored various MSCA configurations and selected the one with the best empirical performance, including **Reverse CA and Bidirectional / Unidirectional Attention**. Due to space limit, please refer to our response to reviewer N6rz (question (4)) to see the ablation study result table. We plan to include this analysis in the camera-ready version if possible.\nWe acknowledge that the community has explored various forms of ‘multi-scale’ and ‘cross-attention’ within UNet. For example:\n\n-DCA [Ates et al., 2023]: (encoder-internal) intra-branch CA, essentially remains same-scale.\n\n-U-Transformer [Wang et al., 2022]: inter-branch, self-attention within each scale.\n\n-MCA-UNet [Pan et al., 2023]: intra-branch multi-scale ‘feature fusion’.\n\nNone of them examined UNet as diffusion backbone, which brings in additional constraints compared to modifying UNet as a standalone architecture. Also, their results are for segmentation tasks and have not proven performance in super-resolution.\nTo the best of our knowledge, we are the first to implement cross‑scale, cross‑attention between encoder and decoder features (inter-branch), within a UNet backbone integrated into a (bridged) diffusion model. Thus, despite the similar studies, we claim our MSCA is still novel.\n\n\nQ(3)  ANA design - details / introducing noise? \n\nWe did not incorporate all previous steps. The number of previous steps to include is a controllable choice. We have the decay factor $\\alpha$ to control contributions from temporally distant steps and conduct an ablation study in Section 4.3. For example, when $\\alpha = 1.0$, weights decay as $[1, e^{-1}, e^{-2}, \\dots]$, assigning roughly 86% of total weight to the current and next two steps. \n\nYou are definitely right that certain early steps may decrease performance by introducing unwanted noise. But overall, ANA does not amplify errors because it behaves as a weighted temporal ensemble/fusion instead of a ‘recursive accumulator’. As we mentioned in Q(1) and response to reviewer N6rz, even ‘imperfect’ predictions at intermediate steps still carry semantically meaningful structure. Aggregating them enables the model to smooth out noisy or inconsistent estimates and avoid abrupt local minima in the reverse process. \n\nEmpirical results prove that ANA is bringing in meaningful context, not noise, as shown in Table 2.\n\n\nThe ANA design is consistent with findings in temporal ensembling and recurrent denoising literature. \nAn intuitive explanation for ANA mechanism is an extension of Exponential Moving Average (EMA) [Kingma & Ba, 2015]. The ANA aggregation acts more like a regularizing prior across timesteps than a hard accumulation. \n\n\nThus, we conclude ANA is a smoothed, multi-scale-informed estimate of the noise $\\bar{\\epsilon}_t$, which improves robustness by reducing variance from any single $\\hat{\\epsilon}_t$. \n[Kingma & Ba, 2015] Adam: A Method for Stochastic Optimization. ICLR 2015.\n\n\nQ(4) Ablation result w/ and w/o quality-aware loss \n\nWe proceeded with quality-aware loss in all of our experiments, as it significantly improved model performance - shown below.\n\n| Loss                      | SSIM% ↑ | PSNR ↑ | LPIPS% ↓ | BRISQUE ↓ |\n|----------------------------|--------|--------|---------|-----------|\n| MSE  | 74.1  | 34.9  | 22.4   | 31.7      |\n| Quality-aware Focal   | 93.6  | 38.8  | 16.1   | 24.5      |\n (OCTDiff model, Philophos 84 Dataset)"}, {"review_id": "tvTmFbZudf", "reviewer": "Reviewer_PTP2", "summary": "The authors propose OCTDiff, a Bridged Diffusion model to improve image resoluton and quality from Optical Coherence Tomography (OCT) devices. They amend the diffusion nodel with two techniques: adaptive noise aggregation and cattention, and introduce quality loss. They show this approach significantly outperforms traditional convolutional baselines and standard DDPMs in super-resolving degraded OCT scans. This work may benefits for broader applicability in medical imaging and ophthalmic diagnostics.", "questions": "- How do the authors perceive the impact of this paper compared to the larger field of Super-Resolution? The architecture changes proposed are quite general and possibly useful for other SR use cases as well. At the same time, SOTA SR baselines and architectures have not been explored and variations of U-net for SR is an area of active research. Question: how this paper differentiates from the existent SR SOTA baselines.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- Added architectural modifications (Adaptive Noise Aggregation, Multi-scale cross attention) appear to benefit generation of realistically looking OCT images, as demonstrated qualitatively (visual appearance) and quantitatively (different fidelity/quality metrics)\n- Technically sound: ablations / comparisons with baselines\n- Contribution to a field with challenging problems due to data sizes/skewness of distribution\n\n\nWeaknesses:\n- Results are too specific to the dataset/application and may not be of interest to wider audience. There is one real and one synthetic dataset, and even though this may be a valuable contribution in this field, it is hard to judge impact for related fields, the impact of this work would be much broader if it was spanning other types of medical images beyond ophthalmology\n- Related works also biased to specific application and lack SR review.", "was_revised": true, "final_justification": "I appreciate the authors detailed rebuttal. In my first review I pointed the good quality of the work. My major concern was about the narrow application. After the argument that previous NeurIPS publications were also focused on single-modality, single-task medical imaging, I am changing my rating for acceptance.", "rebuttal": "We appreciate your thoughtful review and for recognizing our work as technically sound, with beneficial architectural modifications and clear experimental validation. \n\nWe will clarify the concerns you raised all together below: regarding **generalizability** towards other dataset/application, **positioning within the super-resolution (SR)** field, comparison with **SOTA baselines**, and **impact** of our work.\n\n**1. Our work is application-driven in a diffusion framework**\n\nWe fully agree that an elevating advantage our OCTDiff should be able to generalize across medical imaging modalities (e.g. CT/MRI) and natural image datasets (e.g., ImageNet Subsets). Still, our model is **not** designed to be a general-purpose SR benchmark paper that aims to beat all existing SR models across datasets. Instead, we position our contribution as a **task-driven** (OCT-superresolution) application of how a diffusion-based (e.g., BBDM) framework can be optimized (i.e., deploying ANA, MSCA, quality-aware loss) for clinical datasets, specifically focusing on portable OCT grayscale images. \n\nWe would like to emphasize that our work is fundamentally situated in the context of medical imaging applications, submitted under the **Machine Learning in Healthcare** track.\n\nAs we explained the algorithm in detail when addressing reviewer N6rz and AMGy’s questions, while diffusion / cross attention definitions themselves are not a novel invention, we are the first to:\n\n- Implement multi‑scale cross‑attention (**MSCA**) between encoder and decoder features (inter-branch) with specific choice (unidirectional, encoder 128 → decoder 64) within a UNet backbone in a diffusion model. \n\n- Propose **ANA**, a plug-and-play module without modifying sampler, that improves robustness by reducing variance from any \nsingle $\\hat{\\epsilon}_t$ in the reverse process of diffusion.\n\n\nThe UNet is used as the backbone for the diffusion process, not as a standalone SR UNet architecture that has variations for SR. So our contribution lies in the novel way we adapt the **diffusion** (specifically, BBDM) structure to the unique properties of OCT noise and acquisition. Therefore, comparing our approach directly with all general-purpose UNet-based variants or Transformer-based SR models, such as SwinIR [Liang et al., ICCV 2021] may miss the primary objective of this work: task-aware model adaptation, not general algorithm benchmarking.\n\nWe now realize the missing related work in our submission. In the camera-ready version, as much as space allows, we **will expand to provide a more complete overview of recent SR architectures**, including those based on UNet variations and attention-based models such as RCAN, SwinIR, NAFNet. \n\nWe will also discuss why we did not attempt to outperform all SOTA SR models, but instead explored whether our proposed modules could enhance the performance of a diffusion backbone specifically for OCT. We believe this is particularly important in medical imaging, where **real-world performance and interpretability in domain adaptation are often more critical than marginal improvements on benchmark** natural-image datasets.\n\n\n\n**2. OCT’s underlying signal characteristics and its broad Impact**\n\nWe understand your concern that our experiments are currently constrained to two datasets (one real, one synthetic) within the OCT domain. While this does limit immediate generalizability (as stated in manuscript line 265~269), we want to emphasize that:\n\nOCT shares **core signal degradation patterns** with other imaging modalities, particularly speckle noise, which is also prevalent in **ultrasound (US)** and **low-dose CT**. Also our algorithm novelty (ANA/MSCA) is not for any specific image type. These reasons make the cross-modality generalization possible for our OCTDiff.\n\n- Due to limited rebuttal period and challenge in acquiring US/CT datasets from colleagues, we are unable to present a generalizability experiment result for now. But we will add citations and discussion of works that highlight commonalities between medical imaging modalities (e.g., [Chen et al., 2019, “Speckle Simulation in US”]; [Zhou et al., 2021, “Self-supervised Denoising in CT and OCT”]). \n\n-  We respectfully argue that OCT is not a niche modality. It is among the **most widely adopted imaging tools in clinical ophthalmology and has expanding applications in cardiology, dermatology, and neurology** [Zhou et al., 2023, RETFound: “a foundational model”, Nature]. Recently, OCT has been recognized with the 2024 Lasker Award, a top honor in biomedical science. (Thanks to Fujimoto, Swanson and Huang [Science, 1991; Nature Medicine, 1998] who invented OCT as a non-invasive, high-resolution imaging method that complements and sometimes exceeds MRI or CT for certain disease/organ imaging)\n\n\n- Even within OCT, there are diverse devices (e.g., Optovue, Lumedica, Topcon), and **cross-device validation** is a meaningful future direction. Our current Philophos 84 dataset is new and is a private dataset.  We plan to acquire data from additional manufacturers to validate generalizability in follow-up studies (manuscript line 274~275). \n\n\n**3. Clinical value of portable OCT super-resolution beyond a single SR task**\n\nEven if we view our work as purely OCT-specific, it is worth recognizing the significant clinical and commercial importance of the task we address: portable OCT image enhancement. \n\nSome more background/motivation of our work:\n\nMost commercial medical systems remain expensive, bulky (weighing over 50 pounds) and limited to hospital settings. However, the emerging wave of **low-cost, portable** devices (under $10000 for portable OCT) has emerged since 2022 (after **covid**) to revolutionize **point-of-care diagnostics** in **resource-limited** settings, rural areas, and for telemedicine. These portable devices, though much more accessible, suffer from degraded image quality due to low-cost optics and lower SNR. Traditionally, imaging quality has been improved by enhancing hardware (more sensors) but also increases cost. We provide the alternative solution to compensate for imaging challenges algorithmically, and our empirical results prove our solution feasible.\n\n Our work directly addresses the limitation of portable imaging by providing a real-time AI solution, potentially enabling:\n\n- Early detection of eye diseases (manuscript section 4.3) outside of clinical centers;\n\n- Integration on lightweight hardware (e.g., manuscript line 276: we are working on embedding software on Jetson Orin Nano for real-time image processing);\n\n\nAnother reason we haven’t done a generalizability test is because unfortunately there is **no public portable OCT dataset (and there are very rare portable medical datasets** in general), again showing this field’s **potential with AI’s entry**. While our “application scope/dataset” may seem narrow on the surface, the downstream impact of our work (high-quality imaging from low-end devices using AI) has substantial significance in medical imaging. We value the **practicality and deployability** of the OCTDiff **beyond just the SSIM/PSNR** benchmarks (and still our OCTDiff achieved best SSIM/PSNR in our task).  Our work follows the growing trend of developing medical **AI+Portable Imaging**: to be more scalable with software; easier to deploy with less physical components; targeting accessibility for underserved populations.\n\n\n**4. Generalizability here should be evaluated as a function of problem class, not dataset variety.**\n\nWith the three points above, we respectfully assert that generalizability in medical imaging should **not** be considered equivalent to performance across arbitrarily different datasets. \n\nFor example, the **BBDM model whose paper presented good SR results on natural images (CelebAMask-HQ dataset), is not performing better than OCTDiff on our task** (manuscript Table 1). This is why we focus on generalization within the domain of speckle-degraded, low-cost imaging. We develop OCTDiff to be able to adapt across consistent signal / hardware (e.g. SDOCT, or other modalities suffering from speckle noise) variations.\n\n\n**5. Prior NeurIPS publications were also on single-modality, single-task medical imaging.**\n\nFor example, many influential works previously accepted at NeurIPS:\n\n- Low-dose CT denoising with CNNs [Chen et al., NeurIPS 2017];\n\n- MRI reconstruction using GANs [Dar et al., NeurIPS 2018];\n\n- Chest X-ray enhancement and restoration [Liu et al., NeurIPS 2020].\n\nThese previous papers are also domain-specific and were also recognized by their technical and potential clinical impact. We believe **our work is no less** than such earlier work (and OCT is no less than CT/MRI etc.). \nOur focus on **task-specific diffusion model adaptation in a clinically impactful domain is aligned with the goals of NeurIPS**. While we plan to extend our work to other modalities in the future (manuscript line 279~280), we believe the depth of analysis and significance presented in our current submission is sufficient to justify its standalone value.\n\nIn conclusion, we appreciate your agreement on our OCTDiff model that is technically sound and solves a challenging problem. We comprehend that your concerns and questions are mainly on the impact / generalization / uncertainty about the task (private dataset, OCT) itself. **Please be assured that our study (OCT + portable imaging SR) has very solid clinical ground and large potential** in both healthcare and commercial.  \n\nAs we stated in Future Work and Results, we also plan to add more related work and generalizability tests in the camera-ready version. We hope our 5 bullet-point responses above better explain the novelty of our work. Thank you again for your thorough review and critical suggestions."}, {"review_id": "NBv5Rltr09", "reviewer": "Reviewer_3tvr", "summary": "The manuscript present OCTDiff, a diffusion-based model designed to improve the quality of low-cost portable OCT images. The authors build on diffusion framework and have two main contributions : Adaptive Noise Aggregation (ANA) and Multi-Scale Cross-Attention (MSCA).", "questions": "Since I am not an experts in the field, is the real-world \"Philophos 84\" dataset considered small?\n\nIs there a risk for generalization, is there a high variability across OCT? The introduction of multi-centric data would likely decrease results, but do you anticipate this factor to be a big (or small) challenges in future work?\n\nWhat is the inference time per image? The goal is to enhance portable OCT, which implies potential use at the point-of-care. Is the model efficient enough for that, or is it purely for offline processing? This should be clarified.", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The research question have high societal impact for low-resource settings, and the integration of a clinically-informed loss is a creative way to use expert knowledge for the training process (and potentially prioritize clinically relevant features).\n\nThe manuscript present a strong evaluation of the proposed method from quantitatives performance scores to qualitatives perceptures scores, an ablation study for modules and hyperarameters explorations and the inclusion of a downstream task (disease classification on the super-resolved images). The scores are compared to a wide-range of models.\n\nA critical risk in medical image super-resolution is \"hallucinating\" pathological features that aren't there or smoothing over real ones. The paper mentions this concern  and notes it as future work, but it's a central issue. Does the ANA or MSCA module specifically help mitigate this? The downstream classification results suggest it performs well, but a direct analysis of potential diagnostically misleading artifacts would strengthen the manuscript significantly.", "was_revised": true, "final_justification": "I believe the author adressed the limitations I observed as well as the other reviewers feedback. Considering my initial review was 'optimistic', I think my initial rating should not change, but the changes to the manuscript and clarifications are in line with what I was expecting.", "rebuttal": "We would like to sincerely thank you for your detailed review and supportive comments on the technical novelty and societal impact. We will reply to your concerns and questions in order below:\n\n**(1) Hallucination** \n\nWe are glad that we agree on the importance of avoiding hallucinating results. Yes, as we mentioned in line 278,\nsafeguarding hallucinations has been one of the first priorities during the development of our OCTDiff model and will always be in our next steps.\n\nOur model is able to avoid hallucination better than baseline models. For example, in our Figure 4, CDM generates two false dips in the retinal layers, which do not exist in the ground truth. We attribute this to the lack of information accumulation in early noise stages. By contrast, BBDM with its inherent brownian bridge design and our OCTDiff with noise step aggregation, effectively integrates prior signal and avoids such hallucinations.\n\nAn example of potential hallucination in the downstream classification:\nIn Figure 4, outputs from convolutional models and CycleGAN distort clinically essential features like RPE layer and flipped RNFL, making them clinically invalid to train a disease classification model.\nWe selected BBDM baseline in the new comparison table below, as an extension of Table 4.\n| **Disease Class** | **Model** | **High Res.**      | **Low Res.**       | **OCTDiff Generated** | **BBDM Generated** |\n|-------------------|-----------|--------------------|---------------------|------------------------|-----------------|\n| **Glaucoma**      | ViT       | 74.4 ± 1.9         | 50.1 ± 1.8 **✱**    | 74.5 ± 3.1             | **62.0 ± 2.5** **✱**  |\n|                   | CNN2D     | 93.4 ± 5.0         | 83.3 ± 2.2 **✱**    | 93.5 ± 0.5             | **81.0 ± 1.0**  **✱** |\n|                   | SwinT     | 75.5 ± 2.6 **✱**   | 49.5 ± 3.1 **✱**    | 55.6 ± 4.3             | **57.1 ± 3.2**   |\n| **AMD**           | ViT       | 86.5 ± 1.9         | 48.9 ± 2.7 **✱**    | 85.8 ± 1.6             | **79.5 ± 2.1**   |\n|                   | CNN2D     | 94.6 ± 0.9 **✱**   | 83.0 ± 1.4 **✱**    | 96.4 ± 0.4             | **91.8 ± 0.7**   |\n|                   | SwinT     | 82.3 ± 1.4 **✱**   | 49.4 ± 2.4 **✱**    | 66.3 ± 1.8             | **64.9 ± 2.6**   |\n\n\nThe significance in Glaucoma results above may be due to RNFL distortion from BBDM output.\n\n\n\nWhich module specifically helps mitigate hallucination? \n\n- Let’s say both of them contribute to reducing hallucination. Theoretically, ANA is designed to optimize local structural details (see our Figure 5: bringing in ANA significantly improves the RNFL layer layout; fixes the disconnected RPE layer; exhibits lowest pixel difference ratio, proving its ability to optimize pixels that human eyes cannot see). On the other hand, MSCA, in principle, is added to algorithmically connect encoder and decoder in UNet backbone such that unrealistic OCT structures do not appear. But once global OCT appearance is generated correctly, MSCA does not help as much as ANA to further finetune the details.\nThus, both of them work together to avoid hallucination. Without either of them, the quantitative result (our Table 2) could still maintain in an acceptable range, but only with both modules deployed together, the SSIM (perceptual quality) and PSNR (fidelity) perform the best.\n\n\n- It’s a good point that the clinically-informed loss which brings in clinical expert’s knowledge directly improves the qualitative performance of OCTDiff. We believe that our model, even though as well-developed / powerful as possible, is not more than a convincing tool / aid for clinical utility. Only when clinicians can rely on our OCTDiff-generated high-res images and make fewer mistakes than before, then our model will have valid impact in current diagnostic wordflows. (This is also why we put good downstream disease classification accuracy as an important indicator of our model's successful development). So the clinically-informed, quality-aware loss function, directly augments the AI blackbox with real-word insights and essentially teaches the AI which images are of highest quality (not only with seemingly high SSIM etc.), meaning not missing anatomical features for correct diagnosis.\n\n\nWhat we plan to do to further mitigate hallucination:\n\n- Potentially introduce expert annotations (segmentation map) as the second input modality;\nLeverage perceptual priors (such as anatomy-guided constraints for OCT) to penalize anatomically implausible generations;\nPost-hoc downstream tests: now we are doing same dataset train + test, but for example, we can experiment with ‘train on real OCT, test on AI-generated image’, or vice versa. If our OCTDiff could pass this cross-dataset test by still getting high accuracy, we will then believe our AI is ready to move forward to deployment on a portable device for a clinical trial.\n\n\n**(2) Dataset**\n\nIt’s totally understandable that you don’t know the full details of our training set as both ‘Phillophos 84’ and ‘Synthetic 500’ dataset are private to us at the moment.\n\nThe 84 in its name indicates that there were 84 patients who participated in our portable OCT screening using the Philophos device. But we were able to get 504 OCT b-scans in total (line 177 in manuscript) via these methods:\n- Each patient has two scans: left (OS) and right (OD) eyes. They are both symmetrically useful.\n- Some patients have more than one visit.\n- We implement image registration and augmentation to enlarge our dataset.\n\n\nWhile it’s still a relatively small dataset (as usually seen in medical tasks where obtaining data is expensive and takes more time) compared to natural-image datasets such as ImageNet Subset (1.28M), each of the scans was spatially large in resolution (line 169~170 in manuscript) with rich structural information, and our model was able to perform well (Table 1) by learning on it. We are actively seeking collaboration with companies that provide state-of-the-art portable OCT devices to capture more images to enrich our dataset. We believe the performance can be even better once more portable OCT scans are obtained.\n\n\n**(3) Generalizability**\n\n\nThis is an excellent question and we appreciate you bring this up. Our model's generalizability has been a concern asked by other reviewers; please feel free to refer to our responses to them (e.g., 2nd bullet-point for reviewer PTP2).\n\n\nAs long as the imaging physics by definition is the same, for example, a spectral-domain OCT (SDOCT) b-scan, we anticipate the imaging quality (e.g., pixel intensity) and anatomical information scanned (e.g., Scan length (horizontal extent) within 6 mm, 9 mm, or even 12 mm across the retina, commonly centered at the fovea) would remain similar. With over 30 yrs of evolution of OCT hardware since its invention in 1990s, different manufactures are providing slightly special OCT scan properties or in various formats (e.g., in a medical report), so it is hard to exactly quantify such difficulty to generalize. We anticipate this to be a solvable challenge in future work.\n\nThis is because OCT shares core signal degradation patterns among manufacturers and even with other imaging modalities, particularly speckle noise, which is also prevalent in ultrasound (US) and low-dose CT. Also, our algorithm novelty (ANA/MSCA) is not for any specific image type. These reasons make the cross-device or even cross-modality generalization possible for our OCTDiff. We will add citations and discussion of works that highlight commonalities between different OCT (center) datasets/medical imaging modalities. Our group also has ongoing research on another ophthalmic disease classification project about training on Zeiss OCT scans and testing on Topcon OCT scans.\n\nThe portable medical imaging field is so new that there are no publicly available portable/low-cost/intentionally low-resolution OCT datasets for us to do a quick generalizability test. This actually further enhances the novelty of our paper. Traditionally the improvement to get a high-resolution scan from a portable device is by putting effort in optimizing physical components in hardware (e.g., smaller, portable at-home scanner during covid). In contrast, our work on proves that leveraging AI is a fast, feasible solution to this need while being convenient and robust at the same time.\n\n\nThe previous paragraph hereby leads us to the last question we would like to answer:\n\n\n**(4) Is it purely for offline processing? What is the inference time per image?**\n\nAnother amazing question. As we mentioned in line 276, we are in parallel pushing hardware deployment to achieve point-of-care. So ours is not just an offline processing tool. Once the model reaches its full generative power, we want to make sure it is ready to be deployed in a clinical trial to achieve real-time imaging + processing (quality enhancement). This is also personally the ultimate goal of my degree plan!\n\n\nSpecifically, we obtained a Jetson Orin Nano device (a small portable imaging device with GPU and AI compatibility). We are still working on reducing model size while maintaining its predictive power at its best. But the prototype is nearly ready.\n\n\nRegarding the **inference time** - Our OCTDiff model requires ~380ms per image on an NVIDIA© A6000 GPU. With pruning and quantization, inference on Jetson Nano reaches ~620ms, which we believe is sufficient for near real-time use in portable OCT workflows. \n\n\nWe again appreciate your precise understanding of the novelty of our work, and for insightfully foreseeing the potential drawback (lies in hallucination) and ultimate contribution to the big picture of revolutionizing current workflows in ophthalmic diagnostics. We plan to extend generalizability (**cross manufacturer/cross modality/or at least validation on natural-image** dataset) performance in the camera-ready version, if possible. We will also make sure to include the essence from your questions and our responses above as well."}]

Author rebuttal:

Meta-review: This submission presents a diffusion-based framework for enhancing portable OCT images. It introduces novel components like Adaptive Noise Aggregation (ANA) and Multi-Scale Cross-Attention (MSCA) to address challenges in medical super-resolution. The reviewers initially raised concerns about theoretical grounding for ANA, design choices for MSCA, and generalizability beyond ophthalmology. However, the authors provided a thorough rebuttal, clarifying ANA’s role in stabilizing reverse diffusion through temporal ensemble principles and justifying MSCA’s unidirectional design with empirical ablation studies. They also highlighted the clinical impact of portable OCT enhancement, supported by strong quantitative results (e.g., SSIM improvements) and downstream task validation (e.g., disease classification). While the work is application-driven, its innovations in diffusion-based medical imaging align with NeurIPS’s focus on impactful, technically sound research. The reviewers, after discussion, unanimously upgraded their ratings, acknowledging the paper’s contributions. Thus, this submission is recommended for acceptance.
Decision: Accept (spotlight)