List of Papers Browse by Subject Areas Author List
Abstract
Fundus photography is essential for retinal disease screening and diagnosis, yet low-quality images are common in clinical practice, undermining both clinical reading and automated analysis. Fundus image enhancement faces two key challenges: (i) real degradations are often compound, causing enhancement models trained under single-degradation settings to generalize poorly; and (ii) restoring low- and high-frequency components involves conflicting objectives, making it difficult to jointly achieve global consistency, fine-detail recovery, and noise suppression. We propose WaveDE, a wavelet-routed dual-expert network for fundus photograph enhancement. Discrete wavelet transform-based band routing decomposes decoder features into low- and high-frequency subbands processed by two specialized experts: the low-frequency expert performs global correction and provides structural priors, while the high-frequency expert leverages them to enhance textures and suppress noise. A high-frequency-injected VAE decoder further recovers fine details lost to latent compression, and an anatomical consistency regularization enforces structural fidelity of vessels and the optic disc. On multiple public datasets, WaveDE achieves the best PSNR (39.02, 95% CI: 38.90–39.16)
and SSIM (0.98, 95% CI: 0.97–0.98) among 12 state-of-the-art methods, with consistent gains across 2 classification and 4 segmentation datasets; out-of-distribution evaluations further confirm cross-domain robustness.The code and model are available at https://github.com/CowboyH/WaveDE.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026/paper/6370_paper.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to the Code Repository
https://github.com/CowboyH/WaveDE
Link to the Dataset(s)
N/A
BibTex
@InProceedings{NieHai_WaveDE_MICCAI2026,
author = { Nie, Haitao AND Wu, Dongjie AND Xie, Bin},
title = { { WaveDE: Wavelet-Routed Dual-Expert Network for Fundus Photograph Enhancement } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 16888},
month = {September},
page = {pending}
}
Reviews
Review #1
- Please describe the contribution of the paper
This paper proposes WaveDE, a wavelet-routed dual-expert network for fundus image enhancement.
- Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
1) Large-scale experiments and comprehensive evaluation framework.
2) Explicitly decomposing composite degradations into low-frequency global degradations and high-frequency local artifacts has a solid signal processing foundation. The structural guidance map G introduces meaningful interaction between experts rather than independent processing.
- Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.
1.Training relies entirely on programmatically synthesized degradations (brightness, contrast, saturation, halo, dark spots, noise, blur, and combinations). However, real clinical degradations are more complex (e. g. , optical aberrations, cataracts, vitreous opacity, tear film instability). Although the paper acknowledges this gap, it provides limited validation on real unpaired low-quality images (only MR-ART). Downstream evaluations (Table 2) still rely on synthetic degradations, failing to demonstrate effectiveness on real data. This is a critical limitation for clinical translation.
2.WaveDE combines SD v1.5 U-Net, FD-Block (VMamba + asymmetric convolutions), HFI-VAE decoder, and two teacher networks (U-Net++, PraNet). However, the paper does not report model size, training time, inference latency, or GPU memory usage. Training is conducted on RTX PRO 6000 (96 GB), which exceeds typical clinical deployment conditions. Efficiency analysis is essential for practical applicability.
3.The structural guidance map (G) is claimed to enable meaningful interaction between experts. However, ablation only evaluates the presence/absence of FD-Block as a whole, without isolating the contribution of (G). A variant without guidance would strengthen this claim.
4.VMamba is used for long-range modeling, but no comparison is provided with alternatives such as Transformers or CNNs. The choice appears driven by complexity rather than task-specific necessity.
5.Datasets such as RFMiD and FIVES are included in both training and downstream evaluation. Although training uses synthetic pairs, exposure to high-quality images may bias results. This issue is not discussed.
6.The use of PraNet for optic disc detection lacks justification. Fixed weights ((\lambda_v), (\lambda_{od})) and spatial coefficients (Eq. 7) are not analyzed.
- Please rate the clarity and organization of this paper
Satisfactory
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The submission does not mention open access to source code or data, but provides a clear and detailed description of the algorithm to ensure reproducibility.
- Based on your review and your understanding of the MICCAI Scientific Code of Ethics, do you believe this submission may involve a potential ethics concern or violation?
N/A
- Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2026/en/REVIEWER-GUIDELINES.html
N/A
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.
(2) Reject — should be rejected, independent of rebuttal
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
Reference to list the major weaknesses of the paper.
- Reviewer confidence
Very confident (4)
- [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.
Reject
- [Post rebuttal] Please justify your final decision from above.
While the rebuttal clarifies several implementation details, it does not sufficiently resolve my main concerns regarding the paper’s empirical validity and practical significance.
The most critical issue remains the heavy reliance on synthetically generated degradations. Although the authors argue that the synthesis pipeline is physically motivated, the paper still lacks convincing validation on real low-quality clinical fundus images. Importantly, both the restoration benchmarks and downstream evaluations are primarily conducted on synthetic degradations, so the reported gains do not adequately demonstrate real-world robustness or clinical applicability.
In addition, the method is also considerably complex, combining SD v1.5, VMamba-based FD-Blocks, HFI-VAE, and multiple anatomical teacher networks. However, efficiency and deployment analysis remain limited. Reporting ~100 ms inference on RTX 4090 after rebuttal is helpful, but practical resource trade-offs, parameter counts, and memory/runtime comparisons against baselines are still missing.
Overall, I find the engineering effort substantial and the experimental performance competitive. However, the current evidence is still insufficient to support the paper’s broader claims regarding robustness, generalization, and practical clinical applicability.
Review #2
- Please describe the contribution of the paper
This paper studies the problem of fundus image enhancement, with a particular focus on compound degradations commonly encountered in clinical acquisition, such as uneven illumination, blur, noise, and local artefacts occurring simultaneously. The authors propose WaveDE, whose core idea is to decompose features into different frequency components using the discrete wavelet transform and process low- and high-frequency information with two dedicated experts: the low-frequency expert is responsible for global correction and provides structural priors, while the high-frequency expert restores texture details and suppresses noise under its guidance. In addition, the paper introduces a high-frequency-injected VAE decoder to compensate for the loss of high-frequency details caused by latent-space compression, and an anatomical consistency regularization term that uses vessel and optic disc teacher networks to constrain anatomical consistency. Experiments on multiple public datasets show that the proposed method achieves strong performance in image enhancement metrics as well as downstream classification and segmentation tasks.
- Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
In real-world fundus image acquisition, multiple degradations often co-occur, whereas many existing methods are still designed around single-degradation settings. Therefore, the two core challenges identified by the authors, namely compound degradations and the conflict between global correction and fine-detail recovery, are well motivated. The problem formulation is clear, the application background is realistic, and the overall motivation is sound.
The proposed method is built around the central idea of frequency decoupling. DWT is used to separate frequency bands, which are then processed by dedicated low- and high-frequency experts. The high-frequency-injected VAE decoder and anatomical regularization further strengthen detail recovery and structural fidelity.
The authors report not only PSNR and SSIM for image restoration, but also evaluate the effect of the enhanced images on downstream classification and segmentation tasks, and further include OOD evaluation on PAPILA and ARIA. This goes beyond showing that the images merely “look better” and attempts to demonstrate that the enhancement is beneficial for downstream medical image analysis. In particular, in Table 1, the proposed method achieves clearly better PSNR and SSIM than the compared methods on both the full test set and the different subsets, indicating strong overall performance.
The emphasis on anatomical consistency is also well aligned with the requirements of medical imaging tasks. Rather than focusing only on visual quality, the authors additionally use vessel and optic disc priors to constrain the enhancement results, which can help reduce structural artefacts and hallucinated content to some extent. This is meaningful in the medical imaging context.
- Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.
Although the improvements in classification and segmentation are a strength of the paper, the main text does not provide sufficiently detailed descriptions of the downstream models, including their specific architectures, training procedures, and whether exactly the same training and evaluation protocol is used for all enhancement methods. Since the downstream results are an important part of the paper’s empirical support, insufficient detail here weakens both the persuasiveness and the reproducibility of the work.
The selection of baselines and the fairness of the comparisons could also be improved. In addition to fundus enhancement methods, the paper includes methods originating from other tasks such as super-resolution, image inpainting, and ultrasound imaging. However, not all of these baselines are necessarily the most appropriate or strongest competitors for the specific problem studied here. A comparison more focused on directly relevant fundus or medical image enhancement methods would make the conclusions more convincing.
One of the central assumptions of the paper is that different degradations mainly reside in different frequency bands, and can therefore be disentangled through deterministic frequency-band routing. This intuition is reasonable, but at present it is supported mainly indirectly through overall performance and ablation results. The paper lacks more direct analysis showing what the low-frequency and high-frequency experts actually learn, and which types of degradations benefit most from this design.
- Please rate the clarity and organization of this paper
Good
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The submission has provided an anonymized link to the source code, dataset, or any other dependencies.
- Based on your review and your understanding of the MICCAI Scientific Code of Ethics, do you believe this submission may involve a potential ethics concern or violation?
N/A
- Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2026/en/REVIEWER-GUIDELINES.html
N/A
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.
(4) Weak Accept — marginally above the acceptance threshold, but would not mind if rejected, dependent on rebuttal
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
My score is based on the paper’s clear motivation, targeted method design, and strong experimental results. The proposed frequency-decoupled dual-expert framework is well matched to the problem of compound degradations in fundus images, and the paper evaluates the method not only on restoration metrics but also on downstream classification and segmentation tasks, which strengthens its practical relevance. I lean toward a positive recommendation because the method appears effective and the results are consistently strong. However, I remain somewhat cautious because the downstream evaluation setup is not fully described, some baselines are not the most directly relevant, and the paper lacks deeper analysis of the proposed frequency-decoupling mechanism.
- Reviewer confidence
Confident but not absolutely certain (3)
- [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.
Accept
- [Post rebuttal] Please justify your final decision from above.
After reading the authors’ rebuttal, my final opinion is Accept.
The submitted paper addresses an important and practical problem in fundus image enhancement, especially compound degradations and the tension between global correction and fine-detail recovery. The proposed frequency-decoupled dual-expert design is coherent and well matched to the problem setting. The original paper already provides strong empirical evidence, including image restoration metrics, ablation studies, downstream classification and segmentation evaluation, and OOD evaluation.
The authors’ rebuttal is also helpful in clarifying several major concerns raised in the reviews. In particular, the response clarifies the synthetic-data rationale, the training/deployment distinction, the downstream evaluation protocol, the disjointness of data splits, the datasets used for anatomical teacher models, and the baseline selection. These clarifications alleviate my main concerns about experimental fairness and reproducibility.
I still think the paper has limitations. The reliance on synthetic degradations leaves a remaining synthetic-to-real gap, and the analysis of some methodological components, such as the structural guidance map and the LF/HF experts, is still not fully isolated. I also agree that failure cases and more detailed discussion of real clinical degradations would strengthen the paper. I do not rely on promised future ablations or future extensions as new evidence for the decision.
Nevertheless, based on the submitted paper and the clarifications in the rebuttal, I think the strengths outweigh the remaining weaknesses. The method is well motivated, the empirical results are strong, and the paper is likely to be useful to the fundus image enhancement and medical image restoration communities. Therefore, I support acceptance.
Review #3
- Please describe the contribution of the paper
This paper proposes WaveDE, a wavelet-routed dual-expert network for fundus photograph enhancement. The method combines a frequency-decoupled dual-expert block, a high-frequency-injected VAE decoder, and an anatomical consistency regularization term based on frozen vessel and optic disc teachers. Experiments on multiple public fundus datasets show strong PSNR/SSIM and improvements on several downstream classification and segmentation benchmarks.
- Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
1.The paper addresses an important problem in retinal imaging, as low-quality fundus photographs are common in practice and can affect both clinical reading and automated analysis. 2.The overall framework is well structured. The separation between low-frequency global correction and high-frequency detail restoration is reasonable and clearly presented. 3.The experimental section is relatively broad, including image quality assessment, downstream tasks, ablations, and an OOD setting.
- Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.
1.Several datasets used for downstream classification/segmentation are also part of the HQ image pool for generating enhancement training data. The paper does not clearly explain whether the downstream evaluation images are strictly disjoint from the images seen during enhancement training. 2.The downstream evaluation protocol should be described more explicitly, especially whether classifiers/segmenters are retrained or only tested on enhanced inputs. 3.What datasets were used to train the anatomical teacher networks, and is there any overlap with the current experimental datasets? 4.Failure cases are not discussed.
- Please rate the clarity and organization of this paper
Satisfactory
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The submission has provided an anonymized link to the source code, dataset, or any other dependencies.
- Based on your review and your understanding of the MICCAI Scientific Code of Ethics, do you believe this submission may involve a potential ethics concern or violation?
N/A
- Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2026/en/REVIEWER-GUIDELINES.html
N/A
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.
(4) Weak Accept — marginally above the acceptance threshold, but would not mind if rejected, dependent on rebuttal
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
This is a reasonably strong and well-engineered submission. The problem is important, the method is coherent, and the reported performance is promising.
- Reviewer confidence
Confident but not absolutely certain (3)
- [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.
Accept
- [Post rebuttal] Please justify your final decision from above.
WaveDE addresses a clinically relevant fundus enhancement problem with a coherent wavelet-routed dual-expert design. The rebuttal reasonably clarifies data leakage, synthetic data, efficiency, and downstream protocols. Strong PSNR/SSIM, OOD, and downstream classification/segmentation results outweigh missing fine-grained ablations.
Author Feedback
We thank the reviewers for recognizing the problem as “important” (R3), the application background as “realistic” (R2), our “coherent” (R3) and “targeted” (R2) design, the “comprehensive” (R1) and “broad” (R3) evaluation, and the “consistently strong” (R2) performance. Key details below will be added in the camera-ready. 1.R1-W1: Synthetic data. Since paired real LQ–HQ samples are scarce, we generate synthetic data via [23]. It is widely adopted in fundus enhancement, including recent SOTA models MDA [5] and GFE [16]. [23] is physically grounded, explicitly modelling the ophthalmoscope imaging system and real degradation factors. Our synthesis thus approaches the real degradations induced by cataract haze (e.g., brightness/contrast/halo) and vitreous opacity (dark hole + blur). Real paired data are future work. 2.R1-W2: Efficiency & reproducibility. Training and deployment costs differ: 96 GB is for training only; deployment uses ~10 GB on a 24 GB RTX 4090, ~100 ms per image. This is because WaveDE needs only one forward pass (Sec. 2), not iterative diffusion, and the two teachers are dropped at inference (Sec. 2.3). Training details: Sec. 3.1.3.R1-W3: Ablation of G. We agree this would help. The closest existing evidence is w/o FD in Tab. 1 (which removes G together with FD-Block), giving ~2 dB PSNR drops across all subsets. A w/o G ablation is left for future work. 4.R1-W4: VMamba choice. VMamba is task-specific. The LF expert’s global corrections need long-range modelling with preserved spatial structure. CNNs lack long-range capacity; Transformers have weak inductive bias for local structure; VMamba’s 2D selective scan provides both, fitting the task. 5.R1-W5, R3-W1, R3-W3: Data leakage & HQ-exposure bias. Two measures: (i) strictly disjoint splits with no overlap between any training stage (enhancement, ACR teachers, downstream evaluators) and any test split; (ii) WaveDE still ranks first on OOD sets whose HQ images were unseen in training, indicating no HQ-exposure bias. The OD teacher uses Chákṣu/SMDG from the enhancement training split only; the vessel teacher uses DRIVE, LES-AV, TREND, DRHAGIS, all disjoint from our experimental datasets. 6.R1-W6: PraNet, weights, and coefficients. PraNet’s reverse attention refines weak boundaries, matching our OD-preservation goal. The spatial coefficients (1+10σ for vessels, 1+5σ for OD; Eq. 7) are task-aware: higher for sparse vessels, lower for the compact OD. Global weights λ_v=0.01, λ_od=0.04 (Eq. 8) were tuned on the validation set. 7.R2-W1, R3-W2: Downstream protocol. Downstream models — classification: ConvNeXt-Tiny; segmentation: U-Net++ (vessels), PraNet (OD). All trained with AdamW (lr=1e-4, cosine annealing, 100 epochs) using each model’s official loss. Segmentation models reuse ACR teachers’ architectures but train on disjoint data (R1-W5). Identical protocol across methods: each downstream model is trained once on HQ and frozen; enhanced outputs from all methods are tested on the same frozen model—no retraining. 8.R2-W2: Baseline selection. Our baselines cover both SOTA fundus enhancement methods (MDA, OTE, GFE) and methods from related fields whose algorithms are suitable for fundus enhancement; methods relying on modality-specific mechanisms (e.g., algorithms targeting CT/MRI-specific noise) are excluded. Against SOTA fundus methods alone, WaveDE still leads by 4.66–9.24 dB PSNR (Tab. 1). 9.R2-W3: Direct analysis of LF/HF experts. We agree this would help. As indirect evidence, Tab. 1 (w/o FD) shows Local artifact and Blur benefit most, and Fig. 2 shows our model attains the smallest LF and HF residuals. An LF/HF discussion will be added in the camera-ready. We will explore w/o LF and w/o HF ablations in future work. 10.R3-W4: Failure cases. We acknowledge this omission. From Tab. 1, the most apparent relative weakness is the colour subset, where WaveDE’s PSNR (33.73 dB) trails other subsets (39+ dB). This failure mode will be discussed in camera-ready.
Meta-Review
Meta-review #1
- Your recommendation
Invite for Rebuttal
- Please justify your decision. In case you deviate from the reviewers’ recommendations, explain in detail the reasons why. In case of an invitation for rebuttal, clarify which points are important to address in the rebuttal.
This paper tackles an important problem and presents a technically solid solution with strong empirical results. The reviewers are generally positive, but several issues need to be clarified before the work can be fully assessed.
The main concern is limited validation on real-world degradations. The model is trained on synthetic corruptions, while real clinical degradations are more complex. Current evaluation relies largely on synthetic settings, with only limited real-data validation. The authors should better justify the clinical applicability and, if possible, provide additional analysis on real degraded images.
Second, experimental protocol and fairness need clarification. There is potential overlap between training and downstream datasets, and it is unclear whether splits are strictly disjoint. The downstream evaluation setup (e.g., retraining vs. direct inference) is also not clearly described. Baseline selection could be more focused on relevant fundus enhancement methods.
Third, methodological analysis is incomplete. Key components (e.g., structural guidance, frequency decoupling, VMamba choice) are not sufficiently isolated or justified through ablations. More insight into what each module contributes would strengthen the claims.
Finally, efficiency and reproducibility are underreported, including model size, runtime, and training details.
Addressing these points would significantly strengthen the paper.
- After you have reviewed the rebuttal and updated reviews, please provide your recommendation based on all reviews and the authors’ rebuttal.
Accept
- Please justify your recommendation.
After considering the reviews and the authors’ rebuttal, I recommend acceptance.
The paper addresses an important problem in fundus image enhancement and presents a coherent framework supported by strong empirical results across restoration, downstream analysis, and out-of-distribution evaluation. The rebuttal adequately addresses most of the major concerns regarding experimental protocols, reproducibility, and evaluation fairness.
While concerns remain regarding the reliance on synthetic degradations and the need for broader real-world validation, these limitations do not undermine the main findings of the paper. Overall, I believe the contribution is technically sound, practically relevant, and sufficiently supported by the presented evidence.
Recommendation: Accept.
Meta-review #2
- After you have reviewed the rebuttal and updated reviews, please provide your recommendation based on all reviews and the authors’ rebuttal.
Accept
- Please justify your recommendation.
The reviewers generally viewed the problem as important and the design as coherent, and two reviewers supported acceptance after the rebuttal. I also recommend acceptance, while noting that the paper should be framed carefully around its synthetic-degradation setting.
WaveDE addresses a practical problem in fundus image enhancement, where compound degradations can harm both visual quality and downstream analysis. The proposed wavelet-routed dual-expert design is well matched to this setting: low-frequency correction, high-frequency restoration, high-frequency injection in the VAE decoder, and anatomical consistency regularization form a coherent framework. The empirical results are strong, including PSNR/SSIM, ablations, downstream classification/segmentation, and OOD evaluation. The rebuttal also clarifies several important points about data splits, downstream protocols, teacher datasets, and deployment cost.
The main limitation is that training and much of the evaluation rely on synthetic degradations, so claims about real clinical robustness should be stated with caution. For the camera-ready version, the authors should incorporate the rebuttal clarifications on split disjointness, downstream evaluation protocol, teacher-network training data, inference cost, and baseline selection. They should also clearly discuss the synthetic-to-real gap, the limited real-degradation validation, and the remaining failure mode on color-related degradations.
Meta-review #3
- After you have reviewed the rebuttal and updated reviews, please provide your recommendation based on all reviews and the authors’ rebuttal.
Accept
- Please justify your recommendation.
After reading the rebuttal and other reviewers’ comments, I think the authors have reasonably addressed the main concerns.