Title: OCTDiff: Bridged Diffusion Model for Portable OCT Super-Resolution and Enhancement
Venue: NeurIPS 2025
Accepted: yes
Keywords: Super-resolution

Medical Imaging

Diffusion Model

Noise Aggregation

Computer Vision
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

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.

1 Introduction

Medical image analysis has been transformed by recent deep learning advances, including disease classification [1, 2], tissue and cellular segmentation [3], and contrast enhancement [4]. However, most existing models are trained on data from high-end imaging systems, making them less 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 can 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 on 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 on other medical imaging modalities such as computed tomographpy (CT) and magnetic resonance imaging (MRI) [8, 9]. Though quantitative performance improves, these models tend to hallucinate fine details and

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 [10, 11], but SR remains challenging due to the inherent low contrast of low-resolution images and the scarcity of large-scale patient datasets with low-resolution and high-resolution pairs, despite well-established SR benchmarks on natural images [12].

CommercialOCT

High-ResolutionScan

DownstreamClassification

SyntheticScan

Control

Trained

ConvPSF

Clinician

DSx4

Glaucoma

OCTDiff

ImFusion

AMD

Registration

Preprocess

Portable OCT

Low-ResolutionScan

Super-resolvedScan

Diffusion-based generative modeling, i.e. denoising diffusion probabilistic models (DDPMs) [13], has demonstrated superior performance over convolutional models and GANs in terms of both generative quality and training stability [14, 15]. DDPM variants such as latent diffusion models (LDMs) [16] and conditional DDPMs (CDMs) [17] 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 on a 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-image tasks such as style transfer and semantic synthesis. Nevertheless, its potential for super-resolution and its effectiveness when training on medical images, which are small in dataset size and large in spatial resolution [19], remains relatively unexplored.

To address these limitations, we propose OCTDiff, an image-to-image super-resolution conditional diffusion model for portable OCT enhancement. OCTDiff builds upon the bridged framework originated from BBDM. We propose two novel components: (1) an Adaptive Noise Aggregation (ANA) algorithm that stabilizes the reverse denoising trajectory by aggregating noise predictions from previous time steps in the reverse process; and (2) a Multi-scale Cross-Attention (MSCA) UNet 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, we also introduce a custom loss function with modulation from a clinical quality score to guide the model toward perceptually and diagnostically meaningful outputs.

In summary, our main contributions are:

We propose OCTDiff, which significantly outperforms baseline methods in both quantitative metrics and qualitative structural fidelity on real-world OCT datasets. The computational efficiency and training speed are also preserved, as demonstrated in Section 4.1.

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 a new direction at the intersection of AI and healthcare, tailored for ultra-low-resolution 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

under-served and remote populations, and to generalize to other resource-constrained 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 [21, 22, 23, 24] combine pixel-wise noise prediction with latent-space objectives to balance reconstruction accuracy and perceptual realism. For noise scheduling, linear or cosine β t schedules [25] are 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, 25, 28, 29, 30]. These heuristic strategies improve sample diversity and convergence without modifying model architecture, but they are inherently static and task-agnostic, often relying on 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 is 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 [31] learns per-sample noise schedules by predicting the optimal noise scale σ 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 MuLAN's learned noise schedules, ANA adapts noise dynamically per image without introducing additional supervision, making it well-suited for resource-constrained applications. While ANA may sacrifice flexibility in handling highly variable noise patterns compared to MuLAN, it offers a simpler and more efficient solution, particularly for OCT scans that require consistent, low-complexity adaptations.

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 to enhance spatial coherence. Other designs introduce hierarchical [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-scale works stack features within encoder or decoder branches or process them in parallel at a 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 the 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 to avoid error explosion [43]. The idea of combining global and local features in medical images also resembles multi-scale fusion of frequency components via weighted aggregation [44]. Our work is also distinctive from differential-equation-based methods [45] because ANA is discrete-time, controllable (with attenuation α ) and operates during inference (not training). Also rather than modifying the noise sampler like DPM-Solver[46], ANA assumes a fixed noise schedule (e.g., linear or cosine) and aggregates the previously-sampled 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 to improve the stability of the reverse denoising process. Noise predictions at

different time steps t encode complementary information particularly for high-frequency 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-resolution condition ˆ x T = y , and iteratively estimates intermediate latent states ˆ x t to recover a clean image ˆ x 0 over T denoising steps. At each step t , the model defines the reverse transition as:

where p θ is a learned Gaussian distribution with parameters predicted by the U-Net backbone. The noise component is estimated as ˆ ϵ t = ε θ ( x t +1 , t, y ) , where ε θ is the noise prediction network. This predicted noise ˆ ϵ t is then used to reconstruct an intermediate clean estimate ˆ x t 0 , which subsequently informs the mean function µ θ ( x t +1 , t, y ) in the reverse transition.

To improve the robustness of the reverse process, our ANA algorithm does not rely solely on the current noise prediction. Instead, it adaptively aggregates noise predictions from all previous time steps in the reverse process { ˆ ϵ τ } T -1 τ = t using exponential decay. This temporal fusion yields a more stable and informative estimate ¯ ϵ t . The aggregation is formally defined as:

where the weight function w ( τ, t ) = exp( -α ( τ -t )) introduces a time-decay prior, emphasizing predictions closer to step t while still leveraging long-range information. The scalar α > 0 controls how fast the weight decays over time. We discuss the impact of different α values in an ablation study (Section 4.2). The normalization term Z t ensures the aggregated weights sum to 1: ∑ T -1 τ = t w ( τ,t ) Z t = 1 . This updated ϵ t forms soft temporal fusion of noise estimates.

An intuitive explanation for ANA is the exponential moving average (EMA) [47]. We adapt the EMA principle to the spatially-structured noise tensor space, making ANA a 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 is 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.

Refinement Module: To improve robustness at early stages ( t > T/ 2 ), we apply a gradientbased refinement:

where η is a small step size and L denoise denotes a pixel-level loss against a pseudo-ground truth.

Reconstruction Module: Given ˆ x t +1 and the aggregated noise ¯ ϵ t , we use the DDIM [48] inversion rule:

to approximate the clean image at step 0. Then ˆ x t 0 is used to compute the posterior mean ˆ x t as in the DDPM/DDIM formulation.

portable OCT low-res

q(xtlxo,t,y)

commercialOCThi-res

xo

x1

pe(xt-1lxt,y)

xt

XT-1

XT

ET

ET-1

Et

Forward Process

Noise Aggregation

Adaptive Noise Aggregation (ANA)

Reverse Process

3.2 Multi-Scale Cross Attention

We implement multi-scale cross-attention (MSCA) in the UNet backbone of our OCTDiff model. The MSCA enables encoder features at each scale to attend to decoder features at different scales. This cross-scale interaction is particularly important for our super-resolution task on OCT images. Medical image scans such as OCT often contain crucial diagnostic patterns in small, localized patches observed at different scales. For example, at a scale of 32 × 32 , only the vessels may be visible, while at 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 Q s enc at scale s ∈ S enc, the attention is computed with key-value pairs ( K s ′ dec , V s ′ dec ) from different scales s ′ ∈ S dec where s ′ = 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 Q s enc to preserve the residual connection. (Eq. (3)) depicts the MSCA mechanism:

̸

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 a quality-aware loss function. Each high-resolution training image is assigned a perceptual quality score S ( i ) quality derived from subjective ratings provided by ophthalmologists. These ratings (e.g., from 1 to 10) are aggregated via voting and normalized to form a continuous score, reflecting the perceived clinical value of each target OCT image. We design a focal-style loss that enables high-quality scans to have a larger impact during model training. This is formulated as a perceptual quality modulated mean squared error:

Here, γ < 0 is a focusing parameter that controls the degree to which high-quality samples are prioritized, usually being set in [-2, -1]. The modulation term (1 -S ( i ) quality ) γ prevents the model from being confused by relatively suboptimal OCT data.

3.4 Datasets and Experiments

Synthetic 500 Dataset We use two medical OCT datasets in our experiments: one synthetic and one clinical. The synthetic dataset is referred to as the Synthetic 500 dataset that contains 524 pairs of OCT B-scans and was designed to simulate the imaging characteristics of portable OCT devices. The high-resolution images were acquired from a Carl Zeiss © Cirrus HD-OCT 5000 machine and serve as ground truth. The low-resolution scans were generated by first matching the intensity histograms of high-resolution scans, then convolving them with the Point Spread Function (PSF) derived from a Lumedica portable OCT device, and finally downsampling by a factor of 4 as used in ESRGAN [6]

Multiscale Cross Attention (MSCA) UNet

256×256

Query

Key / Value

128x128

64x64

Convolution

Downsampling Layer

Skip Connection

ResBlock

Upsampling Layer

MultiscaleCrossAttention

ResBlock+Self-AttentionBlock

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-O100 portable OCT device. OCT B-scans were captured from patients who visited Columbia Ophthalmology from May through July 2024. To obtain paired high-resolution scans under consistent physical and clinical conditions, each participant also underwent an additional OCT scan using a 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 × 6.5 mm (commercial: >8 mm), and reduced spatial resolution of 562 × 1286 (commercial: >3k).

These hardware limitations result in low-resolution, high-noise, low-contrast images, often with offcentered or partially missing retinal structures. We first filtered out technically corrupted or clinically irrelevant scans, then applied an unsupervised denoising approach [49] to all images. Next, affine registration was performed using ImFusion© software [50] to align remaining scans to a standard OCTtemplate [51] and resizing to 256 for model training. Then we 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 superresolve 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 of 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 α in ANA. To 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-resolution scans. Finally, we discuss the limitations of our approach and directions for future work.

Input

SRCNN

VDSR

CycleGAN

Swin2SR

CDM

BBDM

OCTDiff (Ours)

GT

36.2

38.8

31.9

21.4

23.8

29.3

19.1

OS

43.3

24.9

23.1

17.7

18.1

23.5

25.6

OD

4.1 Performance

OCTDiff is trained and tested separately on the Philophos 84 and Synthetic 500 dataset. Training is conducted on a Lambda Labs Vector server equipped with two NVIDIA©A6000 GPUs, requiring approximately 48 hours to complete from scratch with input images resized to 256×256 pixels and a total diffusion time step T = 1000 . For quantitative evaluation, we employ structural similarity index measure (SSIM) [57], peak signal-to-noise ratio (PSNR), and Learned Perceptual Image Patch Similarity (LPIPS) [58] to comprehensively assess reconstruction fidelity, pixel-level accuracy, and perceptual quality, respectively.

The baseline convolutional models include SRCNN [52] and VDSR [53]. Since previous work [5] on Synthetic 500 dataset implemented ESRGAN and MedGAN, we include CycleGAN [54] for comparison. The Swin2SR [55] is chosen as a state-of-the-art transformer-based super-resolution model leveraging hierarchical self-attention and shifted windows to compare with our MSCA strategy. Regarding diffusion models, we implement CDM [35] and the bridged model BBDM [18] as a foundation for our work. All models are trained from scratch under the same training strategy without pretraining to ensure a 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 1. Most baseline models perform well on the Synthetic 500 dataset and achieve over 0.95 SSIM, which is comparable to 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 our OCTDiff outperforms all baselines.

Qualitative Outcome Figure 4 visualizes two representative examples of outputs generated by all models on the Philophos 84 dataset. Each image is annotated with its BRISQUE score [56], a 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. The CycleGAN generates visually-coherent results but tends to average out finegrained variations, missing subtle retinal layers especially at the bottom of the scan. The Swin2SR produces smooth and consistent textures but over-flattens important curvature details that compromise anatomical realism. Diffusion-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 are 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 on model performance, size (number of trainable parameters), and training cost (in FLOPs) [59], 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 FLOPs, offering a 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 as 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 in ANA is the exponential decay rate α , which controls how quickly the adaptive noise modulation weights diminish as introduced in Section 3.1. Table 3 reports the results with varying α values. When α is low (e.g., 0.1), the model keeps dependency on noise from earlier time steps over a longer duration and yields relatively high SSIM but slightly lower PSNR, reflecting good structural preservation but less sharpness. Increasing α further (e.g., 1.0) makes the model focus on the most recent two to three steps. This over-smoothing causes loss of fine structural details. This trade-off suggests that a moderate α achieves the best balance, and thus we selected α = 0 . 3 for our 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 to compare different cross attentions (CA), including unidirectional and bidirectional CA. The results are reported in Table 4.

Reverse CA (Decoder → Encoder) 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 → Decoder) 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 to achieve real-time image processing. We thus prioritized encoder-to-decoder attention in our task.

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-aware loss as illustrated in 3.3. The results are in Table 5; the quality score significantly improved performance. This highlights how domain knowledge is required for the perceptual and structural understanding of OCT images.

Input Low-res

MSCA

ANA

MSCA+ ANA

GT High-Res

Residual

Pixels > thr (0.1):

25.44%

3.36%

2.13%

1.37%

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, 3 and 4, respectively. The * indicates p -value ≤ 0.05 compared to the OCTDiff-generated input. The numbers are in the format of mean ± standard deviation.

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 [60], vanilla CNN, and SwinT [61] 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 ImageNet1K [62] are imported. We performed 5-fold cross-validation for each model-dataset pair, with the results shown in Table 6, and we conducted Mann-Whitney U tests [63] to assess statistical significance. This evaluation was conducted independently for two representative ophthalmic tasks: glaucoma diagnosis and age-related macular degeneration (AMD) classification,

both of which are widely studied in AI ophthalmology [64, 65, 66, 67, 68], as they are the top two causes of blindness worldwide.

Among the six models trained on high-resolution images, three showed no statistically significant difference, with the simplest vanilla CNN performing equally well in accuracy compared to counterparts trained on OCTDiff-generated 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 6 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, is 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 [16] such as LDM, pre-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 [69] 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 with radiological imaging modalities, particularly speckle noise, which is also prevalent in ultrasound and low-dose CT. Also our algorithm'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 no publicly available portable OCT datasets to our knowledge, and portable medical datasets are very rare in general, indicating the paradigm-shifting role of AI's entry into this field. Our work 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.

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 dependencies at multiple resolutions. To mitigate overfitting on limited clinical datasets and preserve diagnostically critical structures, we introduce a 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 revolutionize the current workflow of ophthalmic diagnostics with AI's power, making it more accessible and cost-effective, ultimately improving healthcare outcomes.

6 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 a different PDF file.

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 superresolution and enhancement of portable OCT images, along with the introduction of key components such as Adaptive Noise Aggregation (ANA) 3.1, Multi-Scale Cross-Attention (MSCA) 3.2 and a customized loss function 3.3 with clinical quality score. The results include improved image quality, quantitative metrics 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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Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [Yes]

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Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

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The answer NA means that the paper does not include experiments.

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Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

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The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

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Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [Yes]

Justification: We emphasized and evaluated the significant societal impacts of our work in AI healcare (Section 1, 4.3, 4.4 and 5). OCTDiff has the potential to revolutionize the current workflow of ophthalmic diagnostics with AI's power, making it more accessible and cost-effective, ultimately improving industrial healthcare outcomes.

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11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [Yes]

Justification: Our work focuses on medical image enhancement and does not involve highrisk generative models such as large language models or general-purpose image generators. The model is intended solely for clinical research use. The data used are de-identified and subject to IRB protocols, and the release of code will be accompanied by clear usage disclaimers to prevent misuse outside intended medical and academic contexts.

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The answer NA means that the paper poses no such risks.

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Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [Yes]

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., ImageNet1K), are 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.

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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 a clinical-quality-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]

Justification: Our work does not involve crowdsourcing or 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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Answer: [Yes]

Justification: All clinical OCT data used in our study were collected under Institutional Review Board (IRB) approval number IRB-AAAV1311, 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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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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).\n\nSTRENGTHS AND WEAKNESSES:\nThe 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.\n\nQUESTIONS:\nSince 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.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n- 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrength: \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.\n\nQUESTIONS:\nSee Weakness above\n\nLIMITATIONS:\nSee Weakness above", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrength\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.\n\nQUESTIONS:\nPlease 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}