Title: Degradation-Aware Dynamic Schrödinger Bridge for Unpaired Image Restoration
Venue: NeurIPS 2025
Accepted: yes
Keywords: Unpaired Image Restoration

Schrödinger Bridge

Optimal Transport
Abstract: Image restoration is a fundamental task in computer vision and machine learning, which learns a mapping between the clear images and the degraded images under various conditions (e.g., blur, low-light, haze).
Yet, most existing image restoration methods are highly restricted by the requirement of degraded and clear image pairs, which limits the generalization and feasibility to enormous real-world scenarios without paired images.
To address this bottleneck, we propose a Degradation-aware Dynamic Schr\"{o}dinger Bridge (DDSB) for unpaired image restoration.
Its general idea is to learn a Schr\"{o}dinger Bridge between clear and degraded image distribution, 
while at the same time emphasizing the physical degradation priors to reduce the accumulation of errors during the restoration process. 
A Degradation-aware Optimal Transport (DOT) learning scheme is accordingly devised.
Training a degradation model to learn the inverse restoration process is particularly challenging, as it must be applicable across different stages of the iterative restoration process.
A Dynamic Transport with Consistency (DTC) learning objective is further proposed to reduce the loss of image details in the early iterations and therefore refine the degradation model.
Extensive experiments on multiple image degradation tasks show its state-of-the-art performance over the prior arts.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presented Degradation-aware Dynamic Schrödinger Bridge (DDSB), a novel framework for unpaired image restoration that addresses the limitations of paired data and static restoration dynamics.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\nThis paper apply Schrödinger Bridge to unpaired image restoration task and propose degradation-aware optimal transport and dynamic transport with consistency to the realism of restored outputs. The results on diverse degradation scenarios demonstrated the effectiveness of the proposed methods.\n\nWeakness:\n1. Schrödinger Bridge has been used for unpaired image to image translation, which is similar to unpaired image restoration. While the paper proposes two extensions to the Schrödinger Bridge framework, DOT and DTC, my main concerns are their limited novelty in the context of existing literature.\n2. The core principle of DOT, which involves jointly training a forward process (restoration) and a backward process (degradation) to enforce consistency, is conceptually very similar to cycle-consistency loss of the seminal work on CycleGAN.\n3. In DTC section, this paper stated that “Therefore, it is necessary to place greater emphasis on the initial stages during the training of the restoration model.” However, the proposed weighting schedule in Eq.(14) shows that it assigns near-zero weight to the initial stages (t → 0) and maximum weight to the final stages (t → 1). There is lacking of a careful explanation about this.\n4. This paper should write the loss of EROT in end of Section 4.1 to enhance the clarity.\n5. The q_\\phi(x_c|x_j) in Fig. 2 should be q_\\theta(x_c|x_j)?\n6. The subscript of \\mathbb{E} of Eq.(12) is not complete.\n7. In Eq.(5), why you assign the joint distribution to the marginal distribution, omitting the integral sign by default?\n\nQUESTIONS:\n1. The author should discuss the fundamental connection between DOT and Cyclegan on jointly training restoration and degradation process.\n2. The author needs to give a stronger explanation of DTC section about \"the statement emphasizes the early stage while the implementation weights more towards the later stage\".\n3. The quantitative results presented in the paper are promising. To further strengthen the claims, it would be highly beneficial for the authors to demonstrate the framework's effectiveness on perceptually-aligned metrics, such as FID, LPIPS.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

comments: SUMMARY:
The paper introduces a Degradation-aware Dynamic Schrödinger Bridge (DDSB) for unpaired image restoration. By combining the Schrödinger Bridge framework with degradation-aware optimal transport and dynamic consistency, DDSB can handle image degradation without requiring paired data. It incorporates a degradation model to reduce error accumulation and improve restoration quality through iterative refinement. Experiments show that DDSB outperforms existing unpaired restoration methods.

STRENGTHS AND WEAKNESSES:
Strengths:
   1. This paper introduces Degradation-aware Dynamic Schrödinger Bridge (DDSB) for unpaired image restoration.
   2. The DOT  strategy helps reduce the artifact accumulation.
   3. The writing is easy to follow.

Weaknesses:
   1. For real multi-degradation scenarios, despite the DTC strategy is employed, training stability remains an issue. The early iterations may still generate artifacts, which could be propagated through the network.
   2. Poor implementation details, e.g. Qϕ and Dϕ do not give relevant implementation details.
   3. DDSB relies heavily on hyperparameters, and this sensitivity requires a large number of adjustments.

QUESTIONS:
1. Table2 shows that the proposed method takes only 0.019ms to process an image , which is very fast. However, the proposed approach is clearly an iteration-based scheme, which is reasonably computationally expensive.
2. As can be seen in Fig2, the degradation-aware Optimal Transport has only three layers of convolution in the degradation-aware model, how does such a small model perform in complex scenes？

LIMITATIONS:
This method is not validated in complex real-world scenarios.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a novel method called Degradation-aware Dynamic Schrödinger Bridge (DDSB) for unpaired image restoration. It utilizes the Schrödinger Bridge (SB) framework to map degraded image distributions to clean ones without requiring paired training data. The approach introduces two key innovations: Degradation-aware Optimal Transport (DOT), which integrates a degradation model to minimize error accumulation, and Dynamic Transport with Consistency (DTC), which ensures consistency across the restoration process, particularly addressing early-stage artifacts. The method is evaluated on tasks such as denoising, deraining, deblurring, and dehazing, achieving state-of-the-art performance with strong generalization to unseen degradation types.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The proposed DOT enhances restoration realism by incorporating degradation priors, a significant improvement over existing unpaired methods that often overlook physical degradation processes. And DTC’s focus on maintaining consistency throughout the restoration trajectory, especially in early stages, reduces artifacts and improves output quality.\n2. The proposed method demonstrates superior results across multiple tasks, with notable PSNR gains (e.g., up to 1.60 dB over baselines) and efficient inference (0.019 ms per 512x512 image), supported by extensive empirical validation.\n\nWeaknesses:\n1. The most crucial issue is the novelty of the proposed framework. Since Schrödinger bridge has been used in unpaired image restoration learning [1]. This paper seems to extend Schrödinger bridge to multiple degradation types, while [1] only focuses on image deraining. \n[1] Neural Schrödinger bridge for unpaired real-world image deraining, information sciences 2024. \n\n2. DOT’s success hinges on the accuracy of the degradation model. If the degradation is complex, non-linear, or poorly estimated, performance could suffer. The paper does not test DDSB’s robustness to imperfect degradation models, leaving its reliability uncertain in real-world scenarios with unknown or mixed degradations.\n\n3. Comparisons are restricted to unpaired methods, omitting state-of-the-art supervised approaches. Including these would contextualize the trade-offs of using unpaired data and reveal whether DDSB narrows the gap with supervised performance\n\nQUESTIONS:\nSee the points in Weakness. Especially the firts point for difference with existing method.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}

comments: SUMMARY:
The paper proposes DDSB, a novel unpaired image restoration framework using Schrödinger Bridge modeling and degradation amplification constraints to enhance restored image realism. It shows strong effectiveness and generalizability across various degradation scenarios, offering a new approach for data-efficient restoration.

STRENGTHS AND WEAKNESSES:
Strengths:
The proposed DDSB method achieves state-of-the-art performance across multiple image restoration tasks, demonstrating strong generalization ability and practical relevance.

Weaknesses:
1. The current evaluation relies solely on PSNR and SSIM, which is not sufficient. Please consider including several no-reference image quality assessment metrics to better evaluate performance under realistic degradation scenarios.
2. The results for the denoise task are missing.

QUESTIONS:
1. The goal of this paper is to address unpaired tasks, but in reality, it still uses paired training datasets. Moreover, these datasets contain many similar scenes (for example, in the GoPro dataset, there are many images with minimal differences). Therefore, it may not be considered strictly unpaired. Additionally, I would like to know how the clear data is sampled during the training process and how to ensure that the training data pairs are strictly unpaired.
2. This paper employs a diffusion model to tackle the restoration task, thus it is necessary to evaluate it using some non-parametric metrics.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02