IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
Authors: Hanting Wang
Tao Jin
Wang Lin
Shulei Wang
Hai Huang
Shengpeng Ji
Zhou Zhao
Venue: ICML 2025 poster (2025)
Keywords: Image Restoration
Diffusion Models
Bridge Models
Primary area: applications->computer_vision
TL;DR: We propose a noval framework that enables leveraging existing powerful generative priors in image restoration bridges.

Abstract: Bridge models in image restoration construct a diffusion process from degraded to clear images. However, existing methods typically require training a bridge model from scratch for each specific type of degradation, resulting in high computational costs and limited performance. This work aims to efficiently leverage pretrained generative priors within existing image restoration bridges to eliminate this requirement. The main challenge is that standard generative models are typically designed for a diffusion process that starts from pure noise, while restoration tasks begin with a low-quality image, resulting in a mismatch in the state distributions between the two processes. To address this challenge, we propose a transition equation that bridges two diffusion processes with the same endpoint distribution. Based on this, we introduce the **IRBridge** framework, which enables the direct utilization of generative models within image restoration bridges, offering a more flexible and adaptable approach to image restoration. Extensive experiments on six image restoration tasks demonstrate that IRBridge efficiently integrates generative priors, resulting in improved robustness and generalization performance. Code will be available at GitHub.

IRBridge:
Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
Hanting Wang" 1
Tao Jin
Wang Lin
Shulei Wang'
Hai Huang
Shengpeng Ji
Zhou Zhao"
Abstract
Generative Diffusion
Restorative Diffuusion
X
Bridge models in image restoration construct diffusion process from degraded to clear images. However, existing methods typically require train- ing bridge model from scratch for each specific type of degradation, resulting high computa- tional costs and limited performance. This work aims to efficiently leverage pretrained generative priors within existing image restoration bridges eliminate this requirement. The main challenge is that standard generative models are typically designed for a diffusion process that starts from pure noise, while restoration tasks begin with low- quality images, resulting a mismatch in the state distributions between the two processes. To address this challenge, we propose transition equation that bridges two diffusion processes with the same endpoint distribution. Based on this, we introduce the IRBridge framework, which enables the direct utilization of generative models within image restoration bridges, offering a more flexible and adaptable approach image restoration. Ex- tensive experiments on six image restoration tasks demonstrate that IRBridge efficiently integrates generative priors, resulting in improved robust- ness and generalization performance. Code will available at GitHub.
Transition Equation
Our core contribution lies in bridging the transition between two diffusion processes with the same endpoint distribution, enabling the leverage of pretrained generative priors in image restoration bridges.
Figure 1:
notable performance, studies (Delbracio & Milanfar, 2023) reveal that their learning objective often converges the mean of multiple plausible solutions, limiting their ability to recover fine details such intricate textures.
In contrast, advancements in generative modeling, particu- larly within the family of diffusion models -Sohl-Dickstein et al., 2015; Ho et al., 2020; Song et al., 2020a;b), have demonstrated remarkable performance. These models capable of generating satisfying, visually detailed image samples, attracting attempts (Zhu et al., 2023b; Chung et al., 2022; Fei et al., 2023; Gou et al., 2024) to explore their potential in image restoration tasks. However, most of these methods follow the standard diffusion process, diffusing images into pure noise and using degraded images as the condition for generation, which often requires integrating prior knowledge of the degradation process.
1. Introduction
Image restoration seeks to reconstruct high-quality images from degraded inputs affected by factors such rain, fog, blur, or noise. As a longstanding fundamental problem, it has been extensively studied over the past decades. Main- methods typically adopt an end-to- end supervised paradigm, training regression models for pixel-level reconstruction. While these approaches achieve
To model the transition from low-quality images to high- quality images more intuitively, bridge models (Liu et al., 2022a; De Bortoli et al., 2021; Zhou et al., 2023) have garnered increasing attention, as they model the stochastic process between two distributions or fixed points. Com- pared generation processes initiated from noise, these methods (Delbracio & Milanfar, 2023; Luo et al., 2023; Yue et al., 2023a) better align with the physical dynamics of image restoration, offering distinct theoretical advantages (Delbracio & Milanfar, 2023; Yue et al., 2023a).
stream learning-bass
"Equal contribution "Corresponding author 'Zhejiang Univer- sity. Correspondence to: Zhou Zhao <zhaozhou @ zju.edu.cn>>
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).
However, these approaches invariably require training a model from seratch for each type of degradation, limited by

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IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
dataset scale and model capacity. Given the consisteney in target distributions between image generation models and image restoration bridge models (i.e., both regard the distri- bution of high-quality images as their target distribution), hopeful avenue is to leverage their powerful generative pri- ors in the context of image restoration bridges. To achieve this, a key challenge lies the differences between the two diffusion processes. The mismatch between the distribu- tions of intermediate states in the process flows prevents the pretrained generative model from directly handling the states of the image restoration bridge.
2. Preliminaries
2.1. Generative Diffusion Models
Generative diffusion models (Ho et al., 2020; Song & Er- mon, 2019; Rombach al., 2022) define a forward noising process that gradually perturbs the data distribution into a known tractable prior by progressively injecting noise. A corresponding reverse process is then Iearned to generate data by reversing this transformation. From the perspec- tive of stochastic differential equations (SDEs), generative diffusion models can be formulated (Song et al., 2020b) as discretizations of different types of SDEs, typically catego- rized into variance- preeeecriggee (VP) and variance-exx explldig- (VE) formulations. In the case of DDPM(Ho et al., 2020), its forward diffusion process is defined by Markov chain. At any timestep I, the state xi has closed-form conditional distribution:
To address this challenge, we propose a transition equation (Section 3.1) that bridges two stochastic processes sharing the same endpoint distribution, enabling transitions between their states. We analyze the boundary conditions (Section 3.2) of the transition equation and define forward transition and reverse transition accordingly. Building on the above concepts, we propose the IRBridge framework (Section 3.3), which enables the application of pre-trained genera- tive diffusion models to solve the image restoration bridge problem. Additionally, we explore inference-time hyperpa- rameter strategies (Section 5.1) for various tasks to optimize performance. Our framework eliminates the need to train restorative bridge models from scratch for specific types of degradation and reduces training costs while enhancing model capacity and generalization performance.
p(ax) = (ax; vā, (1 (1-ā,)
(1)
where āg are predefined coefficients. A denoising network ea (ate 1) is trained to predict the added noise e ~ N(0, I), and the original sample 2g can then be estimated as:
Tt v1 (
to
Vaa
Our main contributions can be summarized as follows:
2.2. Image Restoration Bridge Models
We primarily discuss two types of image restoration bridges: IR-SDE(Luo et al., 2023) and GOUB(Yue et al., 2023a). IR-SDE employs a mean-reverting SDE that transforms a high-quality image The into its degraded counterpart Tig- treating the latter as the mean of the terminal Gaussian distribution. The employed SDE is formulated as:
1. We propose a transition equation that bridges two dif- fusion processes with identical endpoint distributions, enabling state transitions between an image restoration bridge and a generative diffusion model.
2. We explore the boundary conditions of the proposed transition equation, leading to a novel image restora- tion framework, IRBridge, which effectively integrates pretrained generative priors into the image restoration bridge.
dx. 6,(μ 2ye )d + g:dw;
(3)
where w; is standard Brownian motion and p== Tig. Sup- pose that the SDE coefficients satisfy g? == 2X for all times t, the solution(Luo et al., 2023) to the SDE determined in Eq 3 is:
p(x+) N (xz; m4, V[T)
3.
The proposed framework eliminates the need to train a separate image restoration bridge model from scratch for each specific type of degradation. Additionally, it provides greater flexibility by allowing the customiza- tion of inference hyperparameters for different tasks to optimize performance.
m, μ (xo u]e 6.
(4)
0 = X2 - -20)
where 0. f 0_d.. Similar to diffusion models, IR-SDE restores images by training neural network to estimate the score function of the reverse SDE corresponding to Eq 3. GOUB(Yue et al., 2023a) further incorporates the Doob's 's h-transform (Särkkä & Solin, 2019) into the SDE described by Eq 3, eliminating the noise at the diffusion endpoint:
4. Extensive experiments on 6 mainstream image restora- tion tasks demonstrate that the integrated generative priors significantly enhance the robustness and gener- alization performance of the image restoration bridge model.
2 -00TTT
dr: = (fe + 91 6t
MxT xz)dIt + g:dw; (5)
2

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IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
where da.t == 20, (1 - e 20 and vr == Flg- Correspond-
Proposition 3.1. Given two diffusion processes with the same endpoint distribution Paata and a sample xo Paota- the transition between their states 2i x 23 at any timestep i,j can be expressed as:
ingly, the forward transition is given by:
p(x))= = N(r;;;; m4, T)
m4= Ee f.T 20 +
(1-e c -1 fT
200:r of te-
of
o7
07.
FT.
zj = a
xi + 20 + + 7 ge.
6 = of :t
(0;)2 0²
0f
a
(
(6)
GOUB can be viewed as imposing boundary conditions on the generalized
(o )2 0²
8 = fj
fi.
process described in Eq 3, by specifying fixed endpoints for the stochastic trajectory.
(o
(og)² 02 (of)²
7 7- bj
3. Method
where and 3 represent the coefficients of x; and To, respectivel,,, while 7 denotes a known drift. The noise coefficient a can be treated as a controllable variable. Ad- ditionally, Eq 7 is actually a reparameterization. Therefore, the condition for its validity is a, 3. 2 0, which leads to max (0, [00]) -S) (5
Our goal is to effectively integrate pre-trained generative models into image restoration bridges. A significant chal- lenge arises from the differing diffusion processes defined by image generation models and image restoration bridges. This discrepancy causes their states to follow distinct dis- tributions, preventing pre-trained generative models from directly handling the states of image restoration bridges. We first introduce the proposed transition equation, which bridges two diffusion processes with the same endpoint distributions, addressing this challenge. Then, we discuss the validity conditions for this equation, followed by the introduction of the proposed IRBridge framework.
1? ) S 0² s {0))).
Through the Proposition 3.1, the transition between the states of two diffusion processes can be achieved. Therefore, by transforming the state of an image restoration bridge onto the diffusion trajectory of a generative model, the pre-trained denoising network can be applied to estimate the initial state No, thereby enabling the iterative process to proceed.
3.1. Transition Equation
However, this transformation necessitates a pre-estimated To. which is inaccessible during actual inference. Fortu- nately, through further discussion of the boundary condi- tions in Eq 7, we find that appropriate choices of hyperpa- rameters can help us circumvent the need for prior estimate.
Assume that high-quality image samples xo follow the data distribution Pacta- It can be concluded that the endpoints of the diffusion processes defined by generative diffusion models and image restoration bridge models share the same distribution Paata- For simplicity, we rewrite Eq (1, 4, 6) into the reparameterized form 2, fizo + b + Tye. This form indicates that the state at any given time t can be expressed as linear combination of the image sample 20, known offset term b,, and standard noise c. Specifically, in the case of DDPM, the offset term by is always zero, whereas for IR-SDE and GOUB, it is a known coefficient related to the degraded image Fig- We use superscripts to differentiate them: generally, x represents the state at time i in the image restorative bridge, and æ represents the state at time j in generative diffusion. Typically, both generative models and existing bridge models for image restoration adopt similar linear Gaussian paths. We present reparameterizations of different categories of diffusion models in the Appendix B (Table 2) to illustrate this insight.
3.2. Boundary Condition
We treat the noise term a in Eq 7 as a manually determined coefficient for the transition. When reaches its maximum value, the coefficient becomes O, and Eq 7 degenerates into the general form of the forward diffusion process. How- ever, our primary focus is on the scenario where a attains its minimum value. In this case, the transition equation exhibits minimal dependence on my and incorporates the smallest noise term.
Critical Timestep. Given the timestep i of the source state 27, we refer to the timestep j that satisfies (/; )2 = ? )2
the Critial Timestep f, When f, and reaches its minimum value of O, the coefficient /3 of xo in Eq 7 also becomes O. The critical timestep is essentially the smallest timestep that allows the coefficient 3 to take the value of O.
Since the diffusion processes are completely independent, the states of the two processes are conditionally independent given a sample xn ~ Patta- This conditional independence leads to the following transition equation:
Note that the value of f, depends solely on the diffusion coefficients defined by the two diffusion processes, which
3

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IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
Citiial sinstess E
XT
DDPM Timestgs
IR-SDE Timestops
IRBridge
Restcredivee Diffeatt Difftzion
Forward
Transition
Cital tmmtapel
Revese Thansitin
Gasrative Dultaiin
IR-SFF Timesteps
DBPS Timastaps
Forward transition
Reverse transition
Figure 2: An example of critical timesteps: the generative diffusion model is Stable Diffusion v1-5 (Rombach et al., 2022), while the restorative diffusion model is IR-SDE (Luo et al., 2023).
X
Figure 3: Overview of the proposed IRBridge framework. This framework enables the direct application of pre- pre-trained models in image restoration bridges, effectively leveraging their powerful priors to achieve image restoration.
provides guidance for selecting the parameter À (in Eq (3, 5)) of image restoration bridge models. Figure 2 illustrates an example of Stable Diffusion v1-5 and IR-SDE. In general, a smaller value of X result in smaller critical timesteps. Typ- ically, we choose in such a way that the critical timestep & falls within the range the pre-trained DDPM timesteps.
bridges. As illustrated in Figure 3, IRBridge first applies forward transition to convert the state of of the restora- tion bridge to state of DDPM trajectory, enabling the pre-trained generative model to estimate the initial sample To. A subsequent reverse transition then transforms this state into =; facilitatinn the next iterative step. By repeat- edly performing these transitions, IRBridge achieves image restoration via conditional generative models (discussed in Section 5.2), effectively harnessing their generative priors.
Forward Transition. If we select j > L (i.e, above the line in Figure 2 left) such that (az)² - 1- 0i
> 0, then ( (5-00 )3, we can f
17
/ (a))2
by choosing the smallest ensure that the coefficient 3 of xo in Eq 7 becomes 0. At this point, Eq 7 simplifies to a process of adding noise to the source state xf independently of Ty. This implies that we can convert the state of the image restoration bridge into the state of DDPM without requiring a prior estimate of zo-
It is important to emphasize that Eq 7 describes the transi- tion bridging two Gaussian conditional probability paths, requiring the noise term of in the forward process to be non-zero. certain cases, such as diffusion bridge models like GOUB (Yue et al., 2023a), which model a stochastic process between two fixed points, the noise coefficient its initial timestep is typically zero. This renders the transition equation invalid. To address this issue, we directly skip the initial timestep in this situation during inference. Experi- ments (Section 4) show that this simple strategy does not negatively impact performance.
Following the DDPM convention, we refer to this process as the forward transition. is worth noting that no additional constraints are placed on the selection of j. We will further illustrate that the strategy for selecting j primarily depends on the characteristics of the specific task (Section 5.1).
Reverse Transition. Once we have estimate of zo. we can similarly apply the transition equation to convert the state 2ÿ of DDPM back into the state - of the image restoration bridge, thereby supporting the next iteration. We invert the roles of the restorative diffusion and the generative diffusion in Eq and ensure that the next timestep less than the critical timestep Łú (i.e., below the line in Figure 2 right) such that (aj)2 55 of ya < 0. At this point, f
IRBridge offers flexible parameter selection, enabling the adjustment of the inference process by selecting appropriate hyperparameters for specific task (Section 5.1). It is also noteworthy that reverse transition is not exclusive, lever- aging the reverse process of the original image restoration bridge represents a viable alternative (Section 5.4).
1)
if we choose the minimum value of = 0, the coefficient of zo in Eq 7 remains 8 2 O. Since the noise term is zero, Eq 7 simplifies to deterministic transformation dependent on rg. We refer to this transition as the reverse transition.
The proposed framework obviates the need to train a distinct image restoration bridge model for each type of degradation from scratch and avoids explicitly modeling the degradation process. Instead, it only requires conditional generative model to solve the image restoration bridge. Since IRBiddge leverages pretrained generative diffusion models, which are typically trained on large-scale image datasets, benefits from improved robustness and generalization performance compared to bridge models trained from scratch.
3.3. IRBridge Framework
Building on the preceding concepts, we propose a novel image restoration framework, IRBridge, which leverages pre-trained generative models to solve image restoration

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IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
4.2. Results and Analysis
Tenestog SchoMii
Teresoop Scheodee
OTEII Tinestep &
Ditia Tincstop
CCPN Teeeepp
CCPY Trreeropa
Table 1 present the quantitative comparison results of our IRBridge with other methods on the aforementioned tasks. We employ PSNR. SSIM, LPIPS, and FID evaluation met- rics. PSNR quantifies pixel-level discrepancies, SSIM as- sesses structural similarities (Wang et al., 2004), and LPIPS (Zhang et al., 2018) measures perceptual similarity based on high-level semantic features. In contrast, FID (Heusel et al., 2017) evaluates the distance between the distributions of generated and real images, emphasizing overall image realism rather than individual pixel accuracy. Consistent with prior studies (Luo et al., 2023; Yue et al., 2023a), we report PSNR and SSIM scores specifically on the Y channel within the YCbCr color space. To eliminate potential in- terference introduced by the VAE employed by pre-trained stable diffusion model, these metrics are computed using label images decoded by the same VAE.
R-22E Terecaps 1) Image Deraining
2:5 Image Dehazing
Tenesics SchoMes
ieeeicp SchoMee)
DCPM
RU50 crngs
Tireings Raindrop Removal
Image
Tinestop Schedlej
Terespop Scheldej
oftic Tiresteo
a Timesto 6
3CH Treeneos
Outperforming bridge models trained from scratch. As shown in Table 1, although IRBridge is not directly train models to estimate the conditional score of image restoration bridges, it still outperforms image restoration bridge models trained from scratch DeesShift(Yue et al., 2023b), IRSDE (Luo et al., 2023), GOUB (Yue al., 2023a), RDDM(Liu et al., 2024), DifeUI et al., 2024)) on most image restoration tasks. These results, particularly the comprehen- sive improvement in FID scores, highlight the advantages brought by integrating pretrained generative priors into IR- Bridge. Compared to these bridge models trained from scratch on limited-scale image restoration datasets, genera- tive models learned through pretraining on large-scal image datasets provide stronger feature representation capabilities, leading to enhanced performance. Moreover, leveraging pretrained models also significantly reduces training bur- den. For example, training GOUB from scratch takes about 2.5 days on an Nvidia 3090 GPU (see Appendix E of (Yue et al., 2023a)), while the ControlNet we employed achieves similar results in just about day.
R.50C Tiratipp light Enhancment
R-0cc 6) Image Inpuinting
Figure 4: Timestep schedules selected for different image restoration tasks (showing only the cases for IR-SDE). The red lines represent the critical timesteps, while the blue lines indicate the chosen DDPM timesteps.
4. Experiments
4.1. Settings
We utilize Stable Diffusion v1-5 (Rombach et al., 2022) as the pretrained generative diffusion model for IRBridge. For the image restoration bridge, we choose IR-SDE (Luo et al., 2023) and GOUB (Yue al., 2023a) for experiments, as they use distribution and fixed points, respectively, the endpoints of their diffusion processes. Unless otherwise specified, the hyperparameter À in Eq (4, 6) of image restora- tion bridge models is set to 2. To introduce conditions into the generative model, we employ ControlNet (Zhang et al., 2023), which is known for its efficiency in integrating guid- ance conditions without compromising the model's original capabilities. For all tasks, degraded images are directly used as conditions for ControlNet. We directly use empty text as the textual conditions and classifier-free free guidance is not employed. For more details, please refer to Appendix E.
Enhanced generalization performance. Figure 7 presents visual comparison between IRBridge and bridge model trained from scratch (GOUB) on inpainting tasks for var- ious other scenes, including both indoor and outdoor im- ages. All models are trained exclusively on facial image data. As observed in the highlighted regions of the fig- ure, IRBridge consistently maintains superior visual quality, whereas GOUB fails to fully inpaint the missing areas and delivers unsatisfactory details (e.g., noticeable blurred re- gions in the staircase area and book pages). These results intuitively demonstrate that the pretrained generative priors endow IRBridge with the ability to generalize to unseen data distributions during training. Moreover, the results also indicate that the employed ControlNet primarily learns to utilize existing guidance information in the low-quality
Our experiments cover major image restoration tasks, in- cluding image deraining, dehazing, desnowing, raindrop removal, low-light enhancement, and image inpainting. These degradations stem from diverse causes and vary in their impact, influencing attributes such as layout, illumina- tion, and texture Figure 4 illustrates the timestep schedules adopted for different image restoration tasks. It should be noted that these timestep schedules are not theoretically optimal. We will further discuss this in Section 5.1.
5

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IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
Table 1: Quantitative comparison across 6 mainstream image restoration tasks. We use 'BM' to denote restorative bridges, "RM" to represent regression-biee based methods, and 'DGP" to denote methods leveraging generative diffusion priors. All results are evaluated a resolution of 512. We choose IR-SDE and GOUB as the diffusion processes during inference.
Deraining
Dehazing
Method
Type
Method
Type
PSNRT
SSIM
LPIPS
FID
PSNRT
SSIM
LPIPS
FID
ResShift
BM
27.54
0.8305
0.054
30.87
ResShift
BM
24.93
0.8107
0.097
17.89
IR-SDE
BM
31.65
0.9041
0.047
18.64
TAO
DGP
20.07
0.7980
0.105
21.84
GOUB
BM
31,96
0.9028
0.046
18.14
WGWS-Net
RM
26.40
0,9015
0.051
14.15
RDDM
BM
28.97
0.9050
0.058
28.80
RDDM
BM
25.33
0.8773
0.088
20.87
DifTUIR 1
BM
0.9619
0.029
10.98
DiffUIR
BM
25.99
0.8669
0.057
24.87
IRBridge-IRSDD
28.27
0.9583
0.032
9.21
IRBridge-IRSDD
26.85
0.8755
0.065
15.73
BrBridge-GGr GOUdge-GGOU
30.39
0.9639
0.027
8.64
IRBridge-GOUR
26.37
0.8752
0.067
15.82
Desnowing
Raindrop Removal
Method
Type
Method
Type
PSNRT
SSIM
LPIPS
FID
PSNR
SSIM
LPIPS.
FID
ResShift
BM
24.14
0.7551
0.1697
29.87
ResShif
BM
25.78
0.7958
0.204
58.79
WGWS-Net
RM
20.34
0.7023
0.2424
35.87
WGWS-Net
RM
28.28
0.8686
0.102
54.09
WeathderDiff
BM
21.79
0.6721
0.2286
33.96
WeathderDiff
BM
27.06
0.8473
0.089
49.06
RDDM
BM
24.81
0.7810
0.1634
31.84
RDDM
BM
27.07
0.8048
0.102
51.86
DifUUIR! !
BM
25.07
0.7812
0.1593
22.89
DifTUIR
BM
26.88
0.8247
0.114
55.84
IRBridge-IRSDE
25.78
0.7832
0.1574
13.17
IRBridge-IRSS BIRdgge-RRSEE
27.08
0.8165
0.098
48.39
IRBridge-Gor GOUB
25.87
0.7813
0.1569
10.38
IRBridge-Gor GOUB
26.91
0.8132
0.098
49.15
Low light Enhancement
Image Inpainting
Method
Type
Method
Type
PSNR1
SSIM
LPIPS
FID
PSNR
SSIM
LPIPS.
FID
ResShift
BM
0.7982
0.119
50.54
DDRM
DGP
27.16
0.8893
0.089
37.02
GDP
DGP
14.55
0.6432
0,217
48.67
IR-SDECNN
RM
29.22
0.9218
0.065
38.35
DGP
16.57
0.7823
0.186
45.74
IR-SDE
BM
28.37
0.9166
0.046
25.13
RDDM
BM
21.89
0.8251
0.124
46.70
GOUB
BM
28.98
0,9067
0.037
4,30
DITUIR
BM
24.29
0.8853
0.100
49.88
PrompuIR
RM
30.22
0.9180
0.068
32.69
IRBridge-IRSDEE
25.52
0.9232
0.059
36.26
IRBridge-IRSDE
30.41
0.9159
0.066
3.34
IRBridge-GOUR
25.59
0.9236
0.059
36.11
IRBridge-GOUU
30.42
0.9158
0.067
3.37
image rather than the process of removing degradation.
discuss the impact of the diffusion process in Section 5.3.
In addition, we provide a quantitative comparison between IRBridge and other methods in real-world degradation sce- narios the Appendix G. The results no-reference im- age quality assessment (NR-IQA) indicate that the pro- posed IRBridge achieves better performance. These results demonstrate that incorporating pretrained generative pri- ors enhances the generalization ability of image restoration bridges across different image domains and under complex real-world degradations.
Comparison with regression-based image restoration methods. As shown in Table 1, although IRBridge is not explicitly trained for image restoration tasks, it achieves per- formance comparable the regression-basee model WG- WSNett (Zhu et al., 2023a), which follows end-to-end supervised learning paradigm. Remarkably, IRBridge even outperforms WGWSNet in the image desnowing task. It is also important to highlight that the current results do not reflect the theoretical performance limits of IRBridge (see Section 5.1). These findings underscore the effectiveness of IRBridge in leveraging conditional generative models for image restoration
Comparison with methods utilizing pretrained genera- tive priors We also compare IRBridge with other methods (GDP (Fei et al., 2023), TAO (Gou et al., 2024)) that lever- age pretrained generative priors but follow the standard generative diffusion paradigm. As shown in Table 1, IR- Bridge consistently outperforms these methods across all metries. Unlike these methods that start their diffusion pro- cess from pure noise, IRBridge initiates its iterative process directly from the degraded image. This allows IRBridge to anchor itself to the degraded image throughout its inference process, ensuring better data consistency. We will further
5. Discussions
5.1. Timestep Selection Strategy
The timestep selection strategy for the forward transition process IRBridge primarily task-specific and relies on empirical choices. In this section, we provide a high-level analysis of this strategy.
6

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IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
= 100
== 86
i= 72
i 58
i= 44
= 30
i 16
i ==1
j = 550
= 472
j 394
j == 317
j = 239
j = 161
j = 84
j=1
Pics
Renatoos
850
j 729
609
489
j= 369
j= 249
j 129
j=1
Figure 5: The impact of timestep scheduling on the model's prediction zo- We simply select linear timestep schedule, with the top row showing the schedule for lower values (i E [550, 1J) and the bottom row showing the schedule for higher values (j E [850, 1). On the right, the changes in PSNR and SSIM with iterations are shown synchronously.
DDPM Timestep Setting.
Forward Transition
Trinsition
Figure 5 illustrates the impact of different timestep sched- ules on the low-light enhancement task, presenting both PSNR/SSIM curves and corresponding visual results. We evaluate two simple linear scheduling strategies. The top and bottom rows of the figure correspond timestep ranges j E [550, 1] and j E [850, 1]. respectively, showcasing in- termediate model predictions io across iterations along with their associated performance curves.
2-
Figure 6: The variation of the forward/reverse transition coefficients (a, B, a in Eq 7) with iterations under the linear timestep scheduling.
The results indicate that using fewer timesteps leads to initial state estimations with richer visual details, but the images remain underexposed until the final stages. In contrast, schedules with larger timesteps (e.g..j 850) produce less accurate initial predictions but progressively improve brightness and detail, ultimately achieving better overall enhancement performance.
Ixputt
GOLD
Label IRlridee
We explain that low-light images typically have a lower mean compared to regular images, and the low-level noise added by a lower j during the forward transition is insuf- ficient to disrupt the statistical properties of the degraded distribution. As result, the final output still suffers from poor lighting conditions. Therefore, we recommend using higher timesteps j for low-quality images with more severe degradation.
Figure 7: We evaluate the generalization ability of other bridge models on other scene images. All models are trained on facial data for image inpainting.
Temporal Dynamic. Simply using linear scheduling does not achieve satisfactory performance across all tasks. Figure 6 provides key insights by visualizing the coefficient curves for both forward and reverse transitions. It can be observed that the coefficient 3 of zo in the reverse transition (blue line in Figure 6 right) rises rapidly as the iterations progress. Therefore, we argue that maintaining higher timestep j in the carly stages is appropriate, as the estimated fo has a relatively small influence on a;_, but provides a solid initialization. In the middle stage, the timestep should be adjusted based on task characteristies: for tasks with severe degradation (e.g., low-light enhancement), higher
reduces the impact of degradation. In contrast, tasks with minor degradation (e.g., dehazing) can use lower j to achieve a more stable inference process. In the final stage, lower j values are preferred, as ân significantly influences " making the final output more sensitive to prediction errors.
Appendix D provides detailed experimental results on timestep configurations for various image restoration tasks. We must acknowledge that the timestep scheduling shown in the Figure 4 is not the optimal choice, but the results pre- sented in Table still demonstrate satisfactory performance.
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IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
continuous conditional constraints, making it hard to anchor the degraded image, which causes it to gradually diverge from the target image the inference progresses. In con- trast, the restoration bridge provides continuous conditional constraints, resulting in images that are more consistent with the desired outcome. This result further validates that, compared to generative diffusion starting from pure noise, the bridge trajectory starting from a low-quality image leads to a more effective diffusion process.
w/. Cond Figure 8: Ablation of Condition for IRBridge. We visualize the output images before and after applying the condition.
Input
Label
w/o Cond
5.4. Discussion about Reverse Transition
It should be noted that the proposed reverse transition is not the only possible option. After obtaining an estimate for the initial sample zo. the next sample can be obtained through the original reverse process of the image restoration bridge. In general, the sample x;-1 obtained by the reverse transition used in IRBridge is not the optimal reverse state that minimizes the negative log-likelihood. Therefore, the iterative path of IRBridge not the optimal path. However, the results reported in the experiments of this paper all based on the proposed reverse transition, aiming to pro- vide a more comprehensive understanding of the proposed transition equation.
16.0
Beratione
24
Reradore
8) Standard Generative Diffusion
b) Image Restoration Bridge
Figure 9: Comparison of the standard generative process starting from noise and the image restoration bridge in terms of PSNR and SSIM.
5.2. Discussion about Conditional Guidance
A natural question is whether IRBridge can directly em- ploy unconditional generative models. Figure 8 presents visualization results generated with and without conditional guidance. As shown in the figure, images generated without conditional guidance preserve the overall layout but fail to produce realistic details. The theoretical explanation lies in the transition equation described in Eq 7, which requires given sample Wo Paata to achieve transitions between states. However, due to the lack of conditional constraints, unconditional models cannot ensure that the estimates to across all timesteps correspond to the same sample. These inconsistencies among these estimates lead to conflicts dur- ing the iterative process, resulting in cumulative errors and causing the final output to converge toward the average of all samples. Visually, as shown in the figure, this produces images that retain only the overall layout but lose nearly all texture and detail. Therefore, introducing conditional guid- ance in IRBridge is indispensable, as it guides the model in predicting a consistent sample across different timesteps, thereby avoiding the aforementioned issues.
6. Limitations and Future Work
Limitations. IRBridge allows for task-specific customiza- tion of hyperparameters, but determining optimal values is often complex and empirically driven process. A re- cent work(Qiu et al., 2025) propose optimizing diffusion coefficients via local Schrödinger bridges to enhance the performance of pretrained models, which similarly offers a promising path for IRBridge to obtain optimal hyper- parameters. However, we argue that empirically chosen coefficients are often sufficient, and conducting complex post-optimization may not be cost-effective.
Future work. The ControlNet used in this study is rela- tively coarse implementation. We do not optimize the condi- tional guidance module or introduce task-specific objectives for image restoration. Future work could enhance perfor- mance by tailoring the guidance mechanism for IR tasks, as adopted in recent studies (Ai et al., 2024; Rajagopalan et al., 2024). Moreover, further optimization of the VAE can help reduce encoding losses and mitigate the ever-smoothig effect caused by the loss of high-frequency details.
5.3. Discussion about Diffusion Process
In Figure 9, we present the PSNR and SSIM curves for sam- ples predicted during the diffusion process using standard generative diffusion (starting from pure Gaussian noise) and the image restoration bridge (starting from degraded images). It can be observed that the curve for standard dif- fusion first rises and then falls, while the diffusion process with the bridge stcadily increases. More importantly, the PSNR/SSIM of the generative diffusion process consistently remains lower than that of the diffusion bridge. Our expla- nation is that the paradigm starting from pure noise lacks
We believe that IRBridge is just an early application of the transition equation proposed in Proposition 3.1. With this equation, we can explore directly using the pre-trained gen- erative model an initialization model for further training with image restoration bridge objectives. We hope this work will contribute to the further application of bridge models in the field of image restoration.
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IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
7. Related Works
also be applied to tasks such image translation, the main ethical concerns focus on the potential misuse of this tech- nology for creating or manipulating deepfakes or inappro- priate content. Such misuse may result in the spread of misinformation, violations of privacy, and other harmful outcomes. To reduce these risks, it is essential to establish sound ethical guidelines and maintain continuous oversight. These concerns are not specific to our method but are widely present across many generative tasks.
Diffusion Generative Prior in Image Restoration. Im-
age restoration methods that integrate diffusion priors typ- ically condition the generation process on degraded im- ages. Some approaches (Zhu et al., 2023b; Feng et al., 2023; Chung et al., 2022; Kawar et al., 2022; Peng et al., 2024) achieve image restoration by solving or approximat- ing log Va.P(y zz), but they often require specific prior knowledge of the degradation process, thereby losing uni- versality. Other efforts (Fei et al., 2023; Gou et al., 2024) aim for unified image restoration by training degradation model and guiding the process through its gradient, but the introduction of adversarial training leads to instability.
We suggest that embedding invisible watermarks in the gen- erated images or implementing safety-checking modules can effectively deter misuse and help ensure that proper attribution or confirmation is provided when the images are used.
Bridge Models. Bridge models construct a stochastic diffu- sion process between two distributions (Bernton et al., 2019; Liu et al., 2022a; De Bortoli et al., 2021; Liu et al., 2023; Shi et al., 2024; Tang et al., 2024) or fixed endpoints (Li al., 2023a; Zhou et al., 2023), thereby eliminating the need for prior knowledge about the distributions. Some methods (Delbracio& & Milanfar, 2023; Yue et al., 2023b; Luo al., 2023; Yue et al., 2023a; Liu et al., 2024; Zheng et al., 2024) have proposed applying bridge models to image restoration to learn the transport mapping from degraded images to clear images. However, these approaches still require train- ing a model from scratch for each type of degradation. Our IRBridge primarily addresses this issue.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 62222211, No. No.U24420326).
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IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
A. Proof of Proposition 3.1
Proof. Given two diffusion processes with same endpoint distribution Paatos and their forward diffusion processes can be expressed in the reparameterized form:
o ff xo + 6j + c1
(8a)
3j 1j xo + bij + oje2:
(8b)
where b3. are known parameters, and NO, 1) is standard Gaussian noise. Considering that their respective diffusion processes are independent of each other, we can write that z; and aj are are independent when given sample Zo ~ Pdota. That means p(a5 rо) p(æ3 ro). We assume the following reparameterization:
fj = a x; +
t.
(9)
Eq 9 and Eq 8b are essentially equivalent. We substitute Eq 8a into Eq 9, and by using the method of undetermined coefficients, we obtain the following equality:
fj = a: f + 3,
b3 = a: bg +
(10)
(07)² = (ax o; 0²
Therefore, we can obtain that:
(07)2 - 0²
(0:)2
(0;)² 2-22 02
8 = fj
(0g)2 fi.
(11)
[o})2 0² (o])2
7 7=
Conditions for validity. We need to emphasize that Eq 7 is still a reparameterization. Therefore, its validity is subject to the following conditions:
0 05050;
(o3) (07)² 0²
(12)
. 20,
(0
Through further simplification, we can get that max(0, [(07))
1 1² S 0² - (07)
At this point, we have completed the proof of Proposition 3.1.
B. Reparameterization of the Diffusion Coefficients
In this section, we present the reparameterization of both image generative diffusion and image restoration diffusion coefficients, rewriting them in a unified linear combination form as x2 = figo + b + dtc. As shown in Table 2, although the objectives of generative and restorative diffusion differ, their defined diffusion paths can both be uniformly expressed as linear Gaussian paths. This creates favorable conditions for applying the transition equation proposed in Eq 7.
is important to note that, beyond differences in the definitions of diffusion paths, image restoration bridges also differ in their training objectives. For instance, both IRSDE and GOUB adopt the score of their respective reverse SDEs as training targets, which are consistent with those used in generative diffusion models. However, RDDM decouples the residual and noise components, estimating them with separate networks. At this stage, IRBridge does not leverage these differentiated objectives, but instead directly adopts the training objective of generative diffusion to train a conditional generation model. Future work could explore incorporating training objectives specific to restoration bridges to further improve model performance.
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IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
Method
ft
b
Ot
DDPM (Ho et al., 2020)
Va
0
vI az
Rectified Flow (Liu et al., 2022b)
1
0
IR-SDE (Luo et al., 2023)
B
(1 --) -1q
X² - -e- -220)
GOUB (Yue al., 2023a)
0. oig Tf
[[1 -
9) cig
+
20ter
ai
af oi
o oT
07
RDDM (Liu et al., 2024)
1 - 0tt
Õtt Fig
B
DiffUIR (Zheng et al., 2024)
1 1- ā
(ā: 6c) F2gg
B
Table 2: Reparameterization of different diffusion processes, where Tig denotes the input low-quality image. For detailed definitions of the symbols shown, please refer to the corresponding original paper.
C. Critical Timsteps across Different Generative Paradigms
In Figure 10, we present the critical timesteps corresponding to representative generative diffusion models, including DDPM(Ho et al., 2020), Stable Diffusion v v1-5(Rombach et al., 2022), and Recited Flow(Liu al., 2022b). Due to cost considerations, we conduct experiments only on Stable Diffusion v1-5. However, the figure shows that the critical timesteps for other models also lie within the typical range covered by pre-trained generative diffusion models. This theoretical consistency supports the general applicability of IRBridge to a broader class of generative diffusion models.
D. Exploratory Study on Timestep Scheduling Strategies
We first predefined timestep schedules and evaluated the effectiveness of IRBridge on a small batch (batch size of 4) of image samples to determine the optimal timestep selection strategy for different restoration tasks, In Figure 11, we visualize these predefined timestep schedules. The schedules are categorized into three types: a) One Stage (Setting 1-2): This strategy employs single-stage linear decay for the timestep schedule, with two maximum timestep values tested: 850 and 550. b) Two Stage (Setting 3-5): This strategy utilizes a two-stage linear timestep schedule, with the primary breakpoints set at 850 and 550. c) Additive (Setting 6-8): This strategy adds offsets to the critical timesteps. We tested constant offsets (100 and 300) as well as linearly decreasing timesteps (from 300 to 100).
soóc Thraiugs
8-60 Timerogs
Bew Tw meaps
oook Theaios
Nesch Time
(a) DDPM and IRSDE
(b) Stable Diffusion v1-5 and IRSDE
(c) Rectified Flow and IRSDE
col Tenedray
couR Thresaay
Mesting
(d) DDPM and GOUB
(e) Stable Diffusion v1-5 and GOUB
(f) Rectified Flow and GOUB
Figure 10: Critical timesteps across different paradigms. We cover commonly used generative models (Stable Diffusion vl-5, DDPM, and Rectified Flow) as well as image restoration bridge models (IR-SDE and GOUB).
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IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
Timette /chaode Ortical medeto
Tirswpp Scnddae Tineste
Theanpp Schedaa! Orticel Tinwete E
Thaap Sowe.a Cricce Trreess
COF'T
Pice Sett 1go
Riiee Setting
Setting
Settine
Tireites Schadde/
Thmsrag Scredae
Tinerap Schedae! Oriical Timettes
Troase Sowe Crtice Traeess
Crtical mettho
Officel Tiwedep
Figure 11: Predefined timestep schedules for exploratory study on timestep scheduling strategies.
We present the results of using 8 predefined timestep schedules across various tasks in Figure (12,13114115,16116111)) including the best and final PSNR and SSIM during inference, well as their complete evolution curves (for brevity, we only show the curves for IR-SDE) Based on the above observations, we progressively fine-tune the timestep schedules according to the macro-analysis introduced in Section 5.1 to achieve relatively better configurations.
important to note that we use small batches (batch size of 4) in these exploratory experiments to save costs, which leads to slight discrepancies between the listed results and the final results shown in Table There are two reasons for this: small-batch samples cannot fully represent the distribution of the entire dataset, and the results listed in Table 1 are obtained with fine-tuned timestep schedules, which are inherently better than the predefined schedules used in the exploratory experiments.
We argue that using small-batch samples in exploratory studies is feasible because our primary focus is on observing the evolution curves of PSNR and SSIM rather than their final values. Therefore, the errors caused by small-batch samples do not significantly affect our conclusions. The timestep schedules we select are typically those that result in steadily increasing metric curves, ensuring a more stable inference process.
Type
Setting
Method
last-PSNR
last-SSIM
best-PSNR
best-SSIM
IRSDE
26.2638
0.8166
27.7116
0.8147
Setting1
GOUB
25.5185
0.8156
26.9044
0.8149
One Stage
IRSDE
25.9933
0.8155
27.3833
0.8081
Setting2
GOUB
26.0375
0.8160
27.2455
0,8086
IRSDE
26.7463
0.8175
27.9550
0.8154
Setting3
GOUB
26.8282
0.8175
27.4642
0.8119
IRSDE
26.6423
0.8166
27.9748
0,8121
Two Stage
Setting4
GOUB
26.8906
0.8165
27.9497
0.8136
ISSDE
26.0475
0.8168
26,8019
0.7965
Setting5
GOUB
25.6752
0.8154
26,8029
0.7965
IRSDE
25.3299
0.8105
25.3299
0,8105
Setting6
GOUB
26.8587
0.8177
27.1057
0,8180
IRSDE
25.3820
0.8139
27,8038
0.8166
Additive
Setting7
GOUB
24.9189
0.8124
27.6537
0,8180
IRSDE
27.0663
0.8167
27.1319
0.8175
Setting8
GOUB
26.7459
0.8181
27.9865
0.8188
(b) Experimental results of mini-batch samples using predefined timestep schedul- ing in the image deraining task.
(a) The curves of PSNR and SSIM.
Figure 12: Exploratory study on timestep scheduling strategy for the image deraining task.
15

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IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
Type
Setting
Method
last-PSNR
last-SSIM
best-PSNR
best-SSIM
IRSDE
23,9948
0.8317
25.1858
0.8482
Settingl
GOUB
23,9132
0.8300
23.9460
0,8305
One Stage
IRSDE
23.6813
0.8274
25.0897
0.8328
Setting2
GOUB
23.7265
0.8277
24.8072
0.8322
IRSDE
23.8970
0.8243
25.1280
0.8458
Setting3
GOUB
23.9113
0.8252
24.1815
0.8266
IRSDE
24.0643
0.8321
25.5087
0.8407
Two Stuge
Setting4
GOUB
24.0983
0.8340
24.4485
0.8320
IRSDE
24.0563
0.8286
25.8331
0.8419
SettingS
Mynn
GOUB
24.0445
0.8304
24,2596
0.8166
IRSDE
24.8325
0.8431
24.8327
0.8427
Setting6
GOUB
22,5133
0.8273
22.5676
0.8244
IRSDE
23.6825
0.8265
25.1649
0.8410
Additive
Setting7
GOUB
23.5327
0.8255
24.2897
0.8267
IRSDE
23.9262
0.8285
25,5893
0.8432
Setting8
GOUB
23.8193
0.8252
24.3383
0.8339
(b) Experimental results of mini-batch samples using predefined timestep schedul-
(a) The curves of PSNR and SSIM.
ing in the image dehazing task.
Figure 13: Exploratory study on timestep scheduling strategy for the image dehazing task.
Type
Setting
Method
last-PSNR
last-SSIM
best-PSNR
best-SSIM
IRSDE
24.7619
0.8200
25.1127
0.8163
Setting1
GOUB
24.8466
0.8205
25.1788
0.8167
One Stage
IRSDE
24.5954
0.8189
24.9139
0.8113
Setting2
GOUB
24.5977
0.8192
24.9417
0,8026
IRSDE
24.8803
0.8198
25.0266
0.8143
Setting3
GOUB
24.8851
0.8196
25.0398
0.8158
IRSDE
24.7594
0.8189
24.9132
0.8179
Two Stage
Setting4
GOUB
24.6576
0.8177
24.7896
0.8194
IRSDE
24.9924
0.8193
25.0615
0.8193
Setting5
GOUB
24.9203
0.8183
25.0298
0.8188
IRSDE
23.3574
0.7916
23.6550
0.7918
Setringó
GOUB
24.0109
0.7985
24.9109
0.8078
IRSDE
23,7726
0.8089
24,6086
0.8124
Additive
Setting7
GOUB
24.1235
0.8097
25.1149
0,8117
IRSDE
24.1872
0.8162
24.5922
0.8137
Setting8
GOUB
24.3540
0.8152
24.8247
0.8149
(b) Experimental results of mini-batch samples using predefined timestep schedul- ing image desnowing task.
(a) The curves PSNR and SSIM.
Figure 14: Exploratory study timestep scheduling strategy for the image desnowing task.
16

---

IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
Type
Setting
Method
last-PSNR
last-SSIM
best-PSNR
best-SSIM
IRSDE
26.0548
0.7923
26.8779
0.7784
Settingl
GOUB
25,9791
0.7917
26.6805
0.7789
One Stage
IRSDE
26,2883
0.7878
26.6549
0.7821
Setting2
GOUB
26.3697
0.7892
26.6449
0.7826
IRSDE
26.5094
0.7884
26.7336
0.7837
Setting3
GOUB
26.5697
0.7887
26.7599
0,7849
IRSDE
25.7990
0.7876
26.0884
0.7713
Two Stuge
Setting4
GOUB
26. 1403
0.7876
26,2381
0.7854
IRSDE
25.1104
0.7800
25.1705
0,7802
SettingS
GOUB
24.8601
0.7762
24,8668
0.7572
IRSDE
26.7129
0.7935
26.7129
0.7935
Setting6
GOUB
25,4206
0.7916
25.6417
0.7913
IRSDE
25.9174
0.7783
27.2205
0,7852
Additive
Setting7
GOUB
25.6085
0.7790
26,8346
0.7792
IRSDE
27.0162
0.7966
27.1633
0.7928
Setting8
GOUB
26.5046
0.7965
26.6997
0.7958
(b) Experimental results of mini-bated batch samples using predefined timestep schedul-
(a) The curves of PSNR and SSIM.
ing in the image raindrop removal task.
Figure 15: Exploratory study timestep scheduling strategy for the image raindrop removal task.
Type
Setting
Method
last-PSNR
last-SSIM
best-PSNR
best-SSIM
IRSDE
23.1250
0.8931
23.1894
0.8915
Setting1
GOUB
25.5623
0.9031
25.8327
0.9027
One Stage
IRSDE
24.6687
0.8999
25,9736
0.8989
Setting2
GOUB
24.6308
0.9003
26.0017
0,8964
IRSDE
23.6494
0.8971
24.4669
0.8978
Setting3
GOUB
23.9677
0.8975
25.2930
0.9003
IRSDE
24.1875
0.8990
26,2098
0.8948
Two Stage
Setting4
GOUB
24.6070
0.8995
26.1871
0.8970
IRSDE
24.2217
0.9015
25.7966
0,8990
Setting5
GOUB
24.4615
0.9021
25.6628
0.8864
IRSDE
13,8463
0.7655
13.8463
0.7655
Setring6
GOUB
24.5219
0.9018
24.6309
0.9010
IRSDE
24.7898
0.9007
24.7898
0.9007
Additive
Setting7
GOUB
25.1895
0.8986
25,2851
0.8977
IRSDE
21.7411
0.8845
21.7752
0.8825
Setting8
GOUB
24.4582
0.9001
25.7583
0.8974
(b) Experimental results of mini-batch samples using predefined timestep schedul- ing low light enhancement task.
(a) The curves PSNR and SSIM.
Figure 16: Exploratory study on timestep scheduling strategy for the low light enhancement task.
17

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IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
Type
Setting
Method
last-PSNR
last-SSIM
best-PSNR
best-SSIM
IRSDE
28.4661
0.8906
28.8653
0.8920
Setting1
GOUB
28,5011
0.8904
28.9114
0,8903
One Stage
IRSDE
27.2414
0.8894
27.9769
0.8905
Setting2
GOUB
27.3087
0.8899
27.9763
0.8907
IRSDE
28.5806
0.8902
28.6749
0,8914
Setting3
GOUB
28.6183
0.8910
28.6480
0.8915
IRSDE
27.5063
0.8765
27.6593
0.8773
Two Stuge
Setting4
GOUB
27.4750
0.8775
27,4750
0,8775
IRSDE
28,9435
0.8908
29.3078
0.8922
SettingS
GOUB
28.9642
0.8924
29,2503
0.8923
IRSDE
27.8617
0.8897
18,1424
0,8855
Setting6
GOUB
18,1664
0.8911
28.6311
0,8931
IRSDE
27.5545
0.8837
27.8692
0.8846
Additive
Setting7
GOUB
27.7256
0.8840
27.8662
0.8846
IRSDE
27.9057
0.8822
28.1693
0.8865
Setting8
GOUB
28.6027
0.8840
29.3940
0.8905
(b) Experimental results of mini-batch batch samples using predefined timestep schedul-
(a) The curves of PSNR and SSIM.
ing in the image inpainting task.
Figure 17: Exploratory study on timestep scheduling strategy for the image inpainting task.
E. Experimental details
E.1. Training Details.
As introduced in Section 4, we train ControlNet to integrate conditional guidance into the pre-trained Stable Diffusion model. We follow the standard training paradigm of ControlNet, fitting the noise added during the forward process. The same settings are applied across all tasks. We use the AdamW optimizer with a learning rate of 5.0 x 10-5 and train for a total of 10k steps. The Adam optimizer is used with 31 0.9 and 3g 0.999 to maintain balance between the momentum term and the variance estimate. A weight decay of 1.0 10 is applied for regularization, while small epsilon value of 1.0 X 10- ensures numerical stability.
The model is trained on an Nvidia RTX 3090 GPU with a batch size of 12. To reduce GPU memory usage during training, mixed-precision training is employed. To manage the learning rate, a constant schedule is employed with 500 warmup steps to gradually ramp up the learning rate at the start of training. The scheduler includes a single cycle with polynomial decay power of allow steady updates.
E.2. Datasets.
We provide details of the datasets used this section. During both training and testing, all image samples are resized and cropped to resolution of 512 to match the input requirements of Stable Diffusson. Although we adopt unified resolution setting, method can also support higher-resolution inputs, thanks a patchify strategy similar to that used in WeatherDiff (Özdenizci & Legenstein, 2023).
Image Deraining. The image deraining task aims to restore images degraded by rain streaks and similar effects. Due to the difficulty of obtaining real-world paired datasets, most image deraining datasets are synthetically generated. For this task, we used the Rain I 100H dataset (Yang et al., 2019), which provides 1,800 paired images for training and 100 for testing. The degradations in Rain 10H are artificially synthesized as light, thin lines that simulate natural rain streaks.
Image Dehazing. To evaluate IRBridge on image dehazing, we used the RESIDE dataset (Li et al.. 2019). The model was trained on the Outdoor Training Set (OTS) subset, containing 72,135 images, and tested on the Synthetic Objective Testing Set (SOTS) subset, which includes 500 images. These subsets feature synthetically generated hazy images designed to mimic real-world atmospheric conditions.
Image Desnowing. For the image desnowing task, we used the Snow100K dataset (Liu et al., 2018), which includes 100k synthesized snowy images paired with corresponding snow-free ground truth images. Specifically, 50K images are used for training and 50K for testing. The degradations in this dataset simulate snow particles and atmospheric scattering effects,
18

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IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
providing diverse and challenging scenarios for evaluation.
Image Raindrop Removal. We evaluated image raindrop removal using the RainDrop dataset (Qian al., 2018), which comprises 861 training images and 58 testing samples from its Testing Set A subset. The degradations in this dataset simulate the refraction and distortion effects caused by raindrops of varying sizes on camera lenses, presenting unique challenges compared to other weather-related tasks.
Low light enhancement. The low-light enhancement task was evaluated using the LOL dataset (Wei et al., 2018). This dataset includes 485 paired images for training and 15 for testing, with each pair consisting of low-light and normal -ight versions of the same scene. The images contain noise from low-light capture conditions, with significantly lower mean pixel values than normal images. These characteristics pose challenges for generation models, which must accurately enhance brightness while preserving details.
Image Inpainting, Image inpainting experiments were conducted on the CelebA-HQ (Liu et al., 2015) 256x256 dataset. We trained the model using 20,000 images with randomly generated brush masks. This task is particularly challenging due to the random nature of the masks, which disrupt the original image layout and cause significant shifts in mean and variance. The task evaluates the model's ability to generate plausible content while maintaining consistency with the surrounding image context.
F. Inference Efficiency
Since IRBridge leverages pretrained diffusion models and adopts an iterative diffusion-style inference process, we acknowl- edge that its inference efficiency is not a primary advantage. Nevertheless, IRBridge provides considerable ficxibility by decoupling model training from inference, allowing independent configuration of diffusion coefficients and the number of inference steps. In Table 3, we present the inference time and quantitative results of IRBridge under different step settings. It is worth noting that even with 75% reduction in inference steps, IRBridge still maintains comparable performance, demonstrating its potential for more efficient inference.
Inference Steps
100
50
25
PSNRT
29.63
29.59
29.24
SSIM
0.876
0.877
0.858
LPIPS
0.173
0.179
0.178
Inference Time
14.2s
7.3s
3.4s
Table 3: Quantitative results under different inference step settings.
G. Comparison on Real-world Degraded Images
We compare IRBridge with other related methods on real-world degraded images, and the results are presented in Table 4. Specifically, we used real-world datasets, including RealRain- 1K (Li et al., 2022) for deraining, realistic snowy images from Snow 100K (Liu et al., 2018) for desnowing, and RTTS (Li et al., 2019) for dehazing. Since these datasets lack ground-truth references, we adopt widely-used no-reference image quality metrics, including MUSIQ(Ke et al., 2021), BRISQUE (Mittal al., 2011), 1), and NIQE(Mittal al., 2012), evaluate the performance of different methods.
As shown in Table 4, IRBridge consistently outperforms other woaoingg-fo----eeee Even when compared with the recent state-of-the-art method DCPT, IRBridge achieves superior performance, demonstrating its effectiveness under realistic conditions. We attribute this improvement to the integration of pretrained generative priors, which endow the model with enhanced generalization capabilities.
approaches in real-world scenarios.
In Figure (18, 19, 20), we present the visual results of IRBridge on real-world deraining, dehazing, and desnowing tasks, providing an intuitive demonstration of its generalization ability in real-world scenarios.
19

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IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
Table 4: Comparison with other methods on real-world datasets.
RealRain- Ik
RealSnow subset of Snow 100K
RTTS BRISQUEL
Method
MUSIQI
BRISQURL
NIQEJ
MUSIQT
BRISQURJ
NIQE]
MUSIQU
NIQE]
IRSDE(Luo et ul., 2023)
33.187
46.882
12.551
39.854
40.872
10.874
45,256
52.825
9.852
GOuB(Yue et al., 2023a)
39.758
42.261
10.641
48.289
34.824
4.987
47,952
53.578
11.587
RDDM(Liu al., 2024)
41.975
41.870
9.587
47,586
33.879
6.881
49.888
51.574
10.054
Dife et al., 2024)
41.758
43.821
9.001
49.812
32.987
5.027
50.517
50.954
8,805
DCPT(Hu et al., 2025)
44.915
39.843
8.147
45.812
35.871
6.871
48.109 10,100
54.641
9.847
IRBridge
43.439
39.915
8.393
51.969
32.347
4.002
52.076
41,315
5,003
Figure 18: Visualization results IRBridge real-world rainy images.
Figure 19: Visualization results of IRBridge in real-world hazy images.
Figure 20: Visualization results of IRBridge in real-world snowy images.
H. Visualization results for different tasks.
In this section, we present the visualization results of IRBridge on image deraining, dehazing, desnowing, raindrop removal, low-light enhancement, and image inpainting tasks. It is important to note that these results are unselected to rigorously evaluate the performance of IRBridge.
20

---

IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
IRBridge (IRSDE)
IRBridge (GOUB)
IRBridge (IRSDE)
IRBridge (GOUB)
Input
Label
Input
Label
Figure 21: Visualization results of IRBridge in the image deraining task.
IRBridge (IRSDE)
IRBridge (GOUB)
IRBridge (IRSDE)
IRBridge (GOUB)
Input
Label
Input
Label
Figure 22: Visualization results of IRBridge in the image dehazing task.
21

---

IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
IRBridge
IRBridge (GOUB)
IRBridge (IRSDE)
IRBridge (GOUB)
Imput
Label
Input
Label
(IRSDE)
Figure 23: Visualization results of IRBridge in the image desnowing task.
IRBridge (IRSDE)
IRBridge (GOUB)
IRBridge (IRSDE)
IRBridge
Input
Label
Input
Label
(GOUB)
Ulbwn
Ulene
ihawn
Ulone
Figure 24: Visualization results of IRBridge in the image raindrop removal task.
22

---

IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
IRBridge (IRSDE)
IRBridge (GOUB)
IRBridge (IRSDE)
IRBridge
Input
Label
Input
Label
(GOUB)
Figure 25: Visualization results of IRBridge in the low light enhancement task.
IRBridge (IRSDE)
IRBridge (GOUB)
IRBridge (IRSDE)
IRBridge (GOUB)
Input
Label
Input
Label
Figure 26: Visualization results of IRBridge in the image inpainting task.
23

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IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
I. Feasible Improvements and Potential Applications
Future Work. As previously mentioned, the current implementation of IRBridge represents only a preliminary attempt applying the proposed transition equation. Future work can focus on improving the guidance module, adapting VAEs image restoration tasks, and incorporating additional training objectives tailored for the bridge diffusion process used during inference. These improvements can further enhance the performance of IRBridge several existing works (Ai et al., 2024; Rajagopalan et al., 2024; Qiu et al., 2025; Lin et al., 2024b; Wang et al., 2025b; Liu et al., 2024; Wang et al., 2025a) may offer valuable insights in these directions. Moreover, the current version of IRBridge does not incorporate textual conditions, leaving the powerful text-guidance capabilities of pretrained diffusion models underutilized. Some existing studies have already demonstrated the feasibility leveraging additional modalities to improve the performance of image restoration tasks. Building upon prior research (Lin et al., 2023a;b; Huang al., 2024a; Fu et al., 2024), incorporating multimodal information can further enhance the performance of IRBridge. Additionally, leveraging a broader variety of datasets (Rajagopalan et al., 2024; Huang al., 2024b; Feng et al., 2024) could also contribute significantly to its improvement.
image restoration tasks, and
Potential Applications. We highlight that the proposed transition equation is not limited to bridging restoration diffusion and generative diffusion; is applicable to a wider range of stochastic diffusion processes, as long as the endpoint distributions are consistent. Therefore, we believe this work can also benefit a broader range of applications, such as image editing(Li al., 2023b; Ju al., 2024), image translation(Zhou et al., 2023), visual generation(Lin al., 2024a) and multimodal generation (Ji et al., 2024).
24


Reviews:
[{"review_id": "zIE4pFk64V", "reviewer": "Reviewer_S4oL", "summary": "Traditional image restoration bridge models require training from scratch for each degradation type, limiting efficiency and generalization. Meanwhile, pretrained generative diffusion models are underutilized due to mismatched intermediate states between generative and restorative diffusion processes. This work proposes a transition equation that connects two diffusion processes with the same endpoint distribution. Based on the equation, the IRBridge framework is introduced to directly utilize generative models for image restoration tasks.\n\n## update after rebuttal\nI appreciate the authors’ detailed response. My main concern regarding the experiments, including the baseline methods and reported performance gains, still remains. Therefore, I maintain my score of 2.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We are grateful for your valuable comments, which are helpful for improving our manuscripts. Below, we will address your concerns point by point.\n\n**Q1: Test image resolution and supported resolution.**\n\nTo ensure a fair comparison, we conducted all comparison experiments at a 512×512 resolution in our paper. However, it should note that the resolution supported by IRBridge depends on the model it employs. For SD1.5’s UNet, it supports resolutions in multiples of 64. Inspired by WeatherDiff, we developed an overlapping patches partitioning scheme to support inputs of arbitrary resolutions. We present [VISUAL EXAMPLES](https://anonymous.4open.science/r/IRBridge-4181/asserts/real/show.md) of IRBridge processing arbitrarily high-resolution real-world images, highlighting its ability to effectively handle high-resolution images without causing performance degradation.\n\n**Q2: Inference efficiency of IRBridge.**\n\nWith the default setting of 100 inference steps, IRBridge takes about 14.2 seconds to process a 512 resolution image, running at 7.18 steps per second. Notably, since IRBridge decouples model training from inference, it allows flexible specification of both the diffusion coefficients and the number of inference steps. In [THIS TABLE](https://anonymous.4open.science/r/IRBridge-4181/asserts/table3.md) , we present the inference time and quantitative results of IRBridge under different step settings. It is worth noting that even with a 75% reduction in inference steps (14.2s -> 3.4s of inference time), IRBridge still maintains comparable performance (<3% average drop), demonstrating its potential for more efficient inference.\n\n**Q3: The performance gains from pretrained diffusion priors.**\n\nThe performance gains brought by pretrained priors can be attributed to three main aspects: \n\n+ **1) Improved Generalization.** Pretrained models provide general knowledge about a wide range of image distributions, enhancing the model’s ability to generalize to various scenarios. IRBridge demonstrates better generalization across different data domains compared to methods trained from scratch (as shown in Fig.7).  Furthermore, we present the results of IBridge on real-world datasets (see **Q4**), showing its superior performance compared to other methods, further supporting our claims. \n+ **2) Faster Convergence.** As shown in Section 4.2, IRBridge benefits from a favorable initialization provided by the pretrained model, leading to nearly twice the training efficiency compared to training-from-scratch methods such as GOUB. \n+ **3) Better Robustness.** Even though we simply adopted a ControlNet trained with a DDPM objective, its performance still outperforms methods trained specifically to estimate the score of a particular SDE. This demonstrates that the pretrained generative prior provides IRBridge with more robust representations, thereby enhancing its overall performance.\n\n**Q4: Comparison on more datasets and with the latest methods.**\n\nWe provide [QUANTITATIVE RESULTS](https://anonymous.4open.science/r/IRBridge-4181/asserts/table1.md) of IRBridge compared with recent methods, including ResShift [1], RDDM [2], DiffUIR [3], and DCPT [4], across different tasks. Additionally, we present the [QUANTITATIVE RESULTS](https://anonymous.4open.science/r/IRBridge-4181/asserts/table2.md) of IRBridge and the aforementioned methods on real-world degraded datasets, covering image deraining, desnowing, and dehazing. \n\nIRBridge outperforms other bridging methods, achieving an average improvement of approximately 3.23% over the latest bridge method DiffUIR, demonstrating that the pretrained generative prior significantly enhances model robustness. Furthermore, IRBridge achieves an overall improvement of about 5.60% over the recent method DCPT on real-world scenarios, confirming that the introduced prior in IRBridge improves the model's generalization across different degradation conditions. We present [VISUAL EXAMPLES](https://anonymous.4open.science/r/IRBridge-4181/asserts/real/show.md) of IRBridge processing real-world degraded images.\n\n **Q5: The size of the test data is not specified.**\n\nWe present the complete number of training and testing samples for the datasets used in the table below. \n\n| Datasets | Training samples | Test Samples |\n| - | - | - |\n| Rain100H | 1800 | 100 |\n| RESIDE | 72,135 | 4,322 |\n| Snow100K | 50,000 | 50,000 |\n| Raindrop | 861 | 58 |\n| LOL | 485 | 15 |\n| CelabA-HQ | 20,000 | 20,000 |\n\nWe thank the reviewer for the thorough review. These detailed suggestions are invaluable for improving the quality of our manuscript.\n\n[1] ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting. NeurIPS 2023\n\n[2] Residual Denoising Diffusion Models. CVPR 2024\n\n[3] Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model. CVPR 2024\n\n[4] Universal Image Restoration Pre-training via Degradation Classification. ICLR 2025.", "claims_and_evidence": "Yes, the claims made in the submission are supported by evidence.", "methods_and_evaluation_criteria": "Overall, the proposed method and evaluation criteria, including the benchmark dataset, are reasonable. However, the size of the test data is not specified. Additionally, based on the images in the paper, it appears that the method only supports fixed-size inputs. The authors should provide more detailed explanations on this aspect.", "theoretical_claims": "Yes, I checked the correctness of the theoretical proofs presented in the paper. I did not identify significant errors.", "experimental_designs_or_analyses": "Yes, I have reviewed the validity of the experiment design and analysis.", "relation_to_broader_scientific_literature": "The core contribution of the paper further enhances the effectiveness of using pretrained diffusion models to complete image restoration.", "essential_references_not_discussed": "Yes, the related works are essential to understand of the context for the key contributions of the paper.", "other_strengths_and_weaknesses": "Strengths:\n1. The proposed transition equation concept remove restrictive assumptions in prior work. Eliminating per-degradation training aligns with real-world needs for scalable solutions, making the approach both practical and original in application design.\n2. Demonstrates state-of-the-art performance on 6 restoration tasks, with potential for immediate use in photography and autonomous vehicles.\n3. The paper is well-structured, with method descriptions presented in a logical and layered manner.\n\nWeaknesses:\n1. The method is based on IR-SDE and GOUB, with pretrained diffusion priors introduced to enhance performance. However, the performance gains from these priors are unclear. Results on some datasets are missing, and the performance improvement in Deraining and Image Inpainting is very limited.\n2. The authors should provide the resolution of the test images and the inference efficiency, as these are important for image restoration tasks.\n3. Although the authors have tested six image restoration tasks, the comparisons on these tasks are not sufficiently comprehensive. The authors should compare their method with the latest works.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "Please see the weaknesses.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "343u1BhPWo", "reviewer": "Reviewer_AyHU", "summary": "This paper introduces a new approach for leveraging pre-trained generative diffusion models in image restoration bridges. Traditional image restoration bridge models require training from scratch for each degradation type, making them computationally expensive. This work aims to eliminate that requirement by integrating generative priors into the restoration process.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your insightful comments! We will address your concerns point by point.\n\n**Q1: Evaluations on real-world scenarios.**\n\nWe present the [EVALUATION RESULTS](https://anonymous.4open.science/r/IRBridge-4181/asserts/table2.md) on real-world datasets (RealRain-1K for deraining, RealSnow for desnowing, and RTTS for dehazing). As these datasets lack ground truth, we utilized widely-used no-reference image quality metrics (MUSIQ, BRISQUE, NIQE) to evaluate the performance of the methods. As shown in the table, IRBridge demonstrates a clear advantage over other training-from-scratch methods in real-world scenarios. Compared to the latest method, DCPT (ICLR 2025), IRBridge still achieves a better performance (with average 5.6% performance improved), highlighting its superior performance under real-world conditions. We attribute these improvements to the pretrained generative prior that equips the model with stronger generalization capability. Visual examples are provided [HERE](https://anonymous.4open.science/r/IRBridge-4181/asserts/real/show.md).\n\n**Q2: Inference efficiency.**\n\nWe acknowledge that the iterative nature of IRBridge makes it less suitable for real-time processing. However, its high flexibility allows for improving efficiency by reducing the number of inference steps. We present the performance and inference time of IRBridge under different numbers of inference steps (on a 3090 GPU processing standard 512×512 resolution images). The [TABLE](https://anonymous.4open.science/r/IRBridge-4181/asserts/table2.md) shows that even with a 75% reduction in inference steps, the performance degradation remains minimal (< 3%), demonstrating it’s potential for lowering inference overhead.  Additionally, future work could focus on distillation and compression of the pretrained generative model to further reduce the computational complexity of IRBridge and improve its inference efficiency.\n\n**Q3: Manual Hyperparameter Tuning.**\n\nWe acknowledge that it is a limitation of IRBridge. To address this, we offer two insights: \n\n+ **1) Manual configuration as a practical solution.**  In Appendix C, we show the impact of timestep selection strategies across different tasks. Our experiments indicate that although timestep selection impacts intermediate states, it has a minimal effect (~5%) on final performance, suggesting manual tuning is both sufficient in most scenarios. Based on our experience, the timestep schedule used in inpainting task tends to work well for most tasks, which is referred as the default setting.\n\n+ **2) Systematic hyperparameter selection.** Several approaches can provide more principled strategies for selecting optimal parameters under specific considerations. For example, the recent work LDSB [1] proposes using a  path prediction network to estimate more suitable diffusion coefficients via a local Schrödinger bridge, thereby improving the performance of pretrained DMs. Similarly, for IRBridge, one could explore optimal timestep scheduling under the context of minimizing relative entropy.\n\nFor a trade-off between cost and performance, we still recommend using manually selected parameters. Pursuing complex optimization for marginal performance gains is not cost-effective.\n\n**Q4: How does the transition equation differentiate IRBridge from other approaches, and in what ways does it offer an advantage over diffusion-based IR methods?**\n\nWe would like to elaborate on the contributions of IRBridge from both theoretical and practical perspectives.\n\n+ **1) Theoretically.** IRBridge supports bridging two distinct diffusion processes that share the same endpoints. In contrast to previous IR bridge methods, IRBridge does not define a specific diffusion process tailored for restoration. Instead, it leverages the transition equation to ”substitute“ a pretrained DDPM model in place of a specially trained score network for solving the corresponding SDE. To our knowledge, this is the first work to introduce a pretrained generative model into such bridge-based restoration frameworks, breaking the limitation of prior approaches that require training models from scratch.\n+  **2) Practically.** The incorporation of pretrained generative priors endows IRBridge with stronger generalization capabilities, especially in complex scenes or across different data domains. In **Q1**, we demonstrated the superior generalization ability of IRBridge compared to other bridging methods in real-world scenarios. Additionally, as discussed in Section 4.2, the pretrained model provides a strong initialization, enabling IRBridge to achieve nearly twice the training efficiency compared to methods trained from scratch (e.g., GOUB).\n\n**Q5: Correction of some typos.**\n\nWe will thoroughly check for spelling errors in the subsequent revision to improve the quality of our manuscript. Your feedback is invaluable for improving our work!\n\n[1] Finding Local Diffusion Schrodinger Bridge using Kolmogorov-Arnold Network, CVPR 2025", "claims_and_evidence": "The claims are generally well-supported by evidence, with mathematical analysis and empirical validation.", "methods_and_evaluation_criteria": "Yes, the proposed methods and evaluation criteria are well-aligned with the problem of image restoration.", "theoretical_claims": "The proof for Proposition 3.1 appears to be correct and complete.", "experimental_designs_or_analyses": "The designs and analyses appear sound, but some evaluations rely on synthetic degradations, which might not capture the full complexity of real-world scenarios.", "relation_to_broader_scientific_literature": "The paper synthesizes and extends prior work in diffusion-based image generation and restoration bridges by introducing a transition equation that leverages pre-trained generative priors—building on foundational ideas from diffusion models (e.g., Sohl-Dickstein et al., Ho et al.) and restoration frameworks (e.g., IR-SDE, GOUB) to eliminate the need for training separate models for each degradation type.", "essential_references_not_discussed": "None.", "other_strengths_and_weaknesses": "It combines ideas from diffusion-based generative modeling and image restoration bridges by introducing a transition equation that leverages pre-trained generative priors.\n\nIts empirical evaluations across multiple restoration tasks demonstrate improved performance and generalization, and the theoretical derivations are presented rigorously.", "other_comments_or_suggestions": "The method relies on manual hyperparameter tuning for timestep scheduling, and further evaluations on real-world degradations would strengthen its practical relevance.\n\nIn my view, the main novelty of this paper lies in the transition equation. How does the proposed transition equation differentiate IRBridge from existing image restoration approaches, and in what ways does it offer an advantage over prior diffusion-based restoration methods?\n\nSome typos, for example \"Critial timsteps\" should be \"Critical timesteps\" in several figures and sections.\n\nThe iterative nature of IRBridge may lead to slower inference, making it essential to quantify its efficiency. Assessing the trade-off between accuracy and speed would offer valuable insights for practitioners when choosing between IRBridge and other restoration methods.", "questions_for_authors": "Please see the above comments.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "6YGg2YjA31", "reviewer": "Reviewer_1Y3v", "summary": "This paper argues that the existing diffusion-based restoration methods are based on the standard diffusion process and cannot intuitively simulate the transition from low-quality images to high-quality images. To solve this problem, the bridge model is used. Subsequently, the paper extends the bridge model to the all-in-one image restoration task and conducts experiments.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your insightful review and valuable comments! We will carefully address your concerns below.\n\n**Q1: Insufficient comparative experiments.**\n\nWe have added a [QUANTITATIVE COMPARISON](https://anonymous.4open.science/r/IRBridge-4181/asserts/table1.md) between IRBridge and the suggested methods across different tasks. Since the training code for DCPT has not been released, we directly used the pretrained model provided by the authors for comparison. All results are reported at a unified resolution of 512×512 to ensure a fair comparison. \n\nThe proposed IRBridge outperforms existing methods on most tasks. While it may underperform on deterministic metrics like PSNR and SSIM due to its generative nature and output diversity (e.g., slight tonal shifts from ground truth), these variations rarely affect perceptual quality, though they impact sensitive metrics. In contrast, IRBridge consistently achieves **better FID scores** (lower average ~4.97), highlighting its strength in capturing data distribution. Moreover, we present the quantitative results of IRBridge on real-world datasets in [THIS TABLE](https://anonymous.4open.science/r/IRBridge-4181/asserts/table2.md). IRBridge outperforms the latest method DCPT, achieving an average performance improvement of approximately 5.6%. These results demonstrate the generalization ability of IRBridge.\n\n**Q2: The problem tackled in this paper appears somewhat gradual.**\n\nWe thank the reviewer for their thoughtful comments, and we provide insights from both theoretical and practical perspectives. \n\n+ **Theoretically.** The mentioned methods, similar to IRSDE,  can be regarded as defining a stochastic differential equation (SDE) that describes the transformation from LQ images to HQ counterparts. They typically train models to estimate the score of the corresponding reverse SDE for image restoration. **In contrast**, our method explores the use of score estimators from *generative SDEs* (pretrained DMs) to estimate the score of these SDEs. By leveraging a generative model capable of estimating $p(x_{0} | x_t^*, x_{lq})$, IRBridge can directly solve the reverse SDE defined by methods like IRSDE and GOUB.\n+ **Practically.** Our method is the first to enable bridge methods to directly leverage pretrained DMs, whose priors not only reduce the training burden but also provide better generalization (+5.6% vs DCPT in real-world scenarios) , as demonstrated in **Q1**. Moreover, IRBridge effectively decouples training and inference: by leveraging a DM conditioned on LQ images, IRBridge can solve alternative forms of restorative SDEs, without being constrained to specific diffusion coefficients, enabling greater flexibility.\n\n**Q3: The role of ControlNet and training issues.**\n\nThe employed ControlNet integrates guidance information from the degraded image, ensuring that the model is directed to generate the desired x0. From another perspective, ControlNet can indeed be seen as performing implicit image restoration, as it models $p(x_{hq} | x_t^*, x_{lq})$. **However, we emphasize a fundamental distinction:** ControlNet is trained via conditional score matching, following standard diffusion. Our ControlNet is trained to estimate the score of the SDE associated with a *generative diffusion*, whereas restoration bridge methods like GOUB are trained to estimate the score of their *restorative diffusion* . Moreover, these methods also use LQ images as condition for the model to enhance performance.\n\nWe clarify that IRBridge does not focus on the training of the model itself, yet we acknowledge that its performance depends on the underlying model. The ControlNet we used is a relatively coarse implementation, and other conditional control methods (like UniCon[1]) remain applicable to IRBridge, for fine-tuning the generative model. Future work can explore more effective conditional guidance strategies and guidance modules tailored for IR tasks.\n\n**Q4: Training an all-in-one image restoration bridge cannot be considered a highlight contribution.**\n\nWe agree that training an all-in-one model on multiple degradation datasets can achieve good results. However, we want to clarify that the main contribution of IRBridge lies in enabling the solution of IR bridges through pre-trained DMs for the first time, rather than training an all-in-one model. Typically, training an all-in-one model requires merging multiple datasets to handle diverse image types, leading to high computational costs. In contrast, IRBridge allows direct leverage of generative priors, significantly lowering the training effort (discussed in Section 4.2).\n\n**Q5: Table quality and missing details.** \n\nThe desnowing dataset contains 50k samples in both the training and testing sets. We will correct the visual quality of the tables and figures and address the missing details in the revised version. Thanks pointing that out!\n\n[1] A Simple Approach to Unifying Diffusion-based Conditional Generation, ICLR 2025", "claims_and_evidence": "This paper highlights that, it:\n\n1. connects the diffusion model and bridge model in the context of image restoration.\n2. trains an all-in-one image restoration bridge.\n3. demonstrates its effectiveness.\n\nI agree that the first point is the contribution and highlight of this paper, but I disagree with the last two points. The reasons are as follows:\n\n- This paper does not clearly show what difficulties must be solved in training an all-in-one image restoration bridge. A model concurrently trained on multiple datasets can achieve somewhat good restoration performance. I don't think `training an all-in-one image restoration model` can be regarded as a contribution.\n- This paper only compared its method with very few methods. The experiments are seriously insufficient.", "methods_and_evaluation_criteria": "Using pre-trained generation models makes sense in the all-in-one image restoration task. The test benchmark selected in this paper is also representative.", "theoretical_claims": "The proof of Proposition 3.1 is correct in my view.", "experimental_designs_or_analyses": "The ablation experiments in this paper are extremely comprehensive, but the comparison with other methods in the main experiment is seriously insufficient.\n\nFor example,\n\n- RDDM [1] and DiffUIR [2]. They modified the forward and reverse formulas of the Diffusion Model and were able to transition from low quality images to high quality images. This paper should compare with them.\n- DCPT [3]. As a concurrent work, the performance of this paper should be on par with DCPT. (However, based on the results in the main experiment, I strongly suspect that the method proposed in this paper will not be able to achieve the effect of DCPT.)\n\n[1] Residual Denoising Diffusion Models. CVPR 2024.\n\n[2] Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model. CVPR 2024.\n\n[3] Universal Image Restoration Pre-training via Degradation Classification. ICLR 2025.", "relation_to_broader_scientific_literature": "I believe the method proposed in this paper is helpful for other ill-posed inverse problems.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "**Weakness**\n\nI question the significance of the problem addressed in this paper. ResShift [1], RDDM [2], and DiffUIR [3] have effectively achieved diffusion from low-quality images, providing a better alignment with the physical dynamics of the image restoration process. Furthermore, DiffUIR [3] has also been tested for all-in-one image restoration. The problem tackled in this paper appears somewhat gradual.\n\n[1] ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting. NeurIPS 2023.\n\n[2] Residual Denoising Diffusion Models. CVPR 2024.\n\n[3] Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model. CVPR 2024.", "other_comments_or_suggestions": "Table 1. Why the `Type` is vertical???", "questions_for_authors": "1. In lines 319-320, this paper claims that \"although IRBridge is not explicitly trained for image restoration tasks\". However, Sec.4.1 also claims that \"degraded images are directly used as conditions for ControlNet\". ControlNet is not trained? or ControlNet is not trained for image restoration? What is the function of this ControlNet? (I notice that Appendix D.1 says that \"integrate conditional guidance into the pre-trained Stable Diffusion\". However, it just trains image restoration. )\n2. Appendix D.2 does not describe how the training and test sets of the desnowing dataset are divided.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7QVhQNomxH", "reviewer": "Reviewer_7WdJ", "summary": "This work presents an image restoration framework IRBridge. IRBridge connects the bridge model with the diffusion process of the generative diffusion model by introducing a transition equation, thereby enabling direct utilization of the pre-trained generative model for image inpainting. This methods transforms the state of the bridge model into that of the generative diffusion model through forward and backward transitions, and then exploits the powerful prior knowledge of the generative model for image inpainting. The experiments in the paper demonstrate that IRBridge performs better in various image restoration tasks and exhibits superior robustness and generalization capabilities.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We are grateful for your positive review! We will carefully address your concerns below.\n\n**Q1: Empirical selection of hyperparameters**\n\nWe acknowledge that this is a limitation of IRBridge, and we offer two key insights.\n\n+ As shown in Appendix C, while different choices of timesteps can significantly affect the intermediate states of the diffusion trajectory, their impact on final results is generally limited.  This suggests that although we may not adopt theoretically optimal inference hyperparameters, the actual performance loss is often acceptable (~5%). Based on our experience, the hyperparameters used for the inpainting task are effective for most tasks, and we refer it as a default setting.\n+ Recent works have provided ways to systematically optimize hyperparameters under certain metrics. For example, LDSB [1] recently proposed using local Schrödinger bridge to optimize the diffusion coefficients. IRBridge can benefit from it to obtain optimal hyperparameters in the context of relative entropy minimization.\n\nWhile optimal hyperparameters are attainable, we believe that the cost of complex optimization is not cost-effective, as manually designed parameters are typically sufficient.\n\n**Q2: Inference efficiency and computational resources.**\n\nIn our implementation, we use the SD1.5 model, which processes approximately 7.14 steps per second on a 512×512 image using an RTX 3090 GPU, consuming around 6300MB of VRAM. In [THIS TABLE](https://anonymous.4open.science/r/IRBridge-4181/asserts/table3.md) , we present the performance and inference time of IRBridge under different inference steps. Encouragingly, even with a 75% reduction in inference steps, IRBridge shows minimal performance drop (less than 3%) while significantly reducing inference time (14.2s → 3.4s), demonstrating its potential for improving inference efficiency.  Future work can also focus on compression of the pretrained model to further enhance the inference efficiency.\n\n**Q3: Generalization for real-word degradation.**\n\nWe present the evaluation results on real-world datasets in [THIS TABLE](https://anonymous.4open.science/r/IRBridge-4181/asserts/table2.md). We utilized no-reference image quality metrics to evaluate the performance of the methods. As shown in the table, IRBridge demonstrates a clear advantage over other training-from-scratch methods in real-world scenarios. Compared to the latest method, DCPT (ICLR 2025), IRBridge still achieves a better performance (with average 5.6% performance improved), highlighting its superior performance under real-world conditions. We attribute these improvements to the pretrained generative prior that equips the model with stronger generalization capability. Visual examples are provided [HERE](https://anonymous.4open.science/r/IRBridge-4181/asserts/real/show.md).\n\n**Q4: Has the work considers reducing the dependence on the initial state estimation by introducing additional prior information from different perspectives?**\n\nWe would like to clarify that, mathematically the dependence on the initial state is solely determined by β in Eq.7. In principle, we only consider the case where β  takes its minimum value, as this theoretically implies minimal reliance on x0. \n\nHowever, we are open to the idea of retaining some prior information to reduce dependence on x0. For example, given a known degradation model, a plug-and-play (PnP) [2] style approach can be adopted to enforce observation consistency at each iteration step, thereby reducing the degrees of freedom in estimation. Similarly, in the case of image inpainting, we can follow the strategy of RePaint [3] by directly replacing the corresponding pixels in the generated x0 with the uncorrupted pixels from the original LQ sample, thus lowering the reliance on the model’s estimate of initial state.\n\n**Q5: Can the estimation errors of the initial state be gradually corrected through iterative optimization?**\n\nDiffusion-based image generation and restoration methods can generally be viewed as progressively refining their estimate of the initial state during the reverse iteration process. IRBridge, by nature, leverages a pretrained generative model to simulate the reverse process of a restoration bridge. As such, its inference procedure inherently serves to iteratively correct the estimation error of the initial state.\n\n**Q6: Inconsistences in expression and the quality of images and tables.**\n\nWe sincerely appreciate the reviewer’s thoughtful suggestions! We will address these issues in the revised version. These constructive suggestions are invaluable for enhancing the overall quality of our work.\n\n[1] Finding Local Diffusion Schrodinger Bridge using Kolmogorov-Arnold Network, CVPR 2025\n\n[2] Denoising Diffusion Models for Plug-and-Play Image Restoration, CVPR2023\n\n[3] RePaint: Inpainting using Denoising Diffusion Probabilistic Models, CVPR 2022", "claims_and_evidence": "Yes, this work presents a rather detailed theoretical derivation process of the transition equation and a reasonably well-structured introduction to the framework construction of the IRBridge.", "methods_and_evaluation_criteria": "Yes, this work presents a relatively rich set of comparative experiments to verify the performance of IRBridge and conducts a certain degree of analysis on the experimental details.", "theoretical_claims": "Yes, I perused the introduction of the bridge model in the article and the theoretical derivation process of the transition equation thus elicited. Apart from some minor issues (detailed in suggestions), the argumentation is relatively sound.", "experimental_designs_or_analyses": "Yes, I perused the work's elaboration on the setup of the experiment, the implementation method of the comparative experiment, and the analysis of other details of the experiment. Specific issues regarding the experimental section can be found in the \"weaknesses\" part.", "relation_to_broader_scientific_literature": "In response to the task of image restoration, previous research aimed to more intuitively simulate the transition from low-quality images to high-quality ones. The advancements in generative diffusion models, such as various types of diffusion models, have demonstrated remarkable potential in this tasks. The bridge model has attracted attention as it simulates the stochastic process between two distributions or fixed points.", "essential_references_not_discussed": "No, the paper makes a relatively comprehensive citation of the literature.", "other_strengths_and_weaknesses": "Strengths: \n1. The IRBridge framework is novel. It obviates the need to train models from scratch for each degradation type and directly leverages pre - trained generative diffusion models, remarkably reducing the training cost.\n2. Through the proposed transition equation, IRBridge can adapt to diverse image restoration tasks and permits the adjustment of hyperparameters during inference to optimize performance.\n3. The paper validates the performance and robustness of IRBridge through reasonable comparative experiments, and its performance surpasses that of off-the-shelf methods. Weeknesses: \n1. In this work, the selection of hyperparameters is based on empirical methods rather than systematically determining the optimal choices. This brings certain limitations when dealing with some complex degradations or in practical applications. \n2. In Figure 3, the innovative points of the article are not well highlighted within the framework of the IRBridge. For instance, the role played by the transition equation in the IRBridge could be emphasized.", "other_comments_or_suggestions": "1. There are some inconsistencies in expression. For instance, the vast majority uses ‘timesteps’, while in some places ‘time steps’ is used. It is recommended to maintain consistency here.\n2. In the left-hand image of Figure 5, it is rather difficult to discern the values on the abscissa of the two pictures. The numerical range of the timesteps here is of crucial importance.", "questions_for_authors": "1. Although the IRBridge reduces the need to train models from scratch, multiple iterations are still required during the inference process, especially when dealing with high-resolution images. Is there any comparison or analysis regarding the consumption of computational resources?\n2. Is there a test on the image restoration effect of the IRBridge in more complex degradation scenarios? For example, the results of tasks such as real-world degradation tasks to further validate the practicality of the model. \n3. Has the work considers reducing the dependence on the initial state estimation by introducing additional prior information from different perspectives (such as degradation models or image structural information)? Or can the estimation errors of the initial state be gradually corrected through iterative optimization? \nIf the weeknesses and questions be adequately resolved, I would be delighted to increase my score.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: In order to better utilize generative models within image restoration bridges, this paper proposes a transition equation that bridges two diffusion processes with the same end point distribution. The paper originally received 3xWeakReject and 1xWeakAccept. The main concerns include lack of discussions on inference efficiency, insufficient evaluations, manual hyperparameter tuning, missing details, etc. The authors have provided rebuttals and addressed most concerns of reviewers. Afterward, two reviewers raise their ratings. In the discussion phase, Reviewer S4oL still keeps negative about the performance and efficiency of the proposed method. The authors have clarified these concerns in their second rebuttal. Considering the rebuttal and discussions from all reviewers, ACs recommend accepting this paper. The authors are suggested to carefully revise the paper and incorporate newly conducted experiments according to the comments and discussions.
Decision: Accept (poster)