BlurDM: A Blur Diffusion Model for Image Deblurring
Authors: Jin-Ting He
Fu-Jen Tsai
Yan-Tsung Peng
Min-Hung Chen
Chia-Wen Lin
Yen-Yu Lin
Venue: NeurIPS 2025 poster ()
Keywords: Low-level Vision
Image Deblurring
Primary area: applications
TL;DR: We propose BlurDM, a diffusion-based framework that integrates the physics of motion blur formation into the diffusion processes to improve image deblurring.

Abstract: Diffusion models show promise for dynamic scene deblurring; however, existing studies often fail to leverage the intrinsic nature of the blurring process within diffusion models, limiting their full potential. To address it, we present a Blur Diffusion Model (BlurDM), which seamlessly integrates the blur formation process into diffusion for image deblurring. Observing that motion blur stems from continuous exposure, BlurDM implicitly models the blur formation process through a dual-diffusion forward scheme, diffusing both noise and blur onto a sharp image. During the reverse generation process, we derive a dual denoising and deblurring formulation, enabling BlurDM to recover the sharp image by simultaneously denoising and deblurring, given pure Gaussian noise conditioned on the blurred image as input. Additionally, to efficiently integrate BlurDM into deblurring networks, we perform BlurDM in the latent space, forming a flexible prior generation network for deblurring. Extensive experiments demonstrate that BlurDM significantly and consistently enhances existing deblurring methods on four benchmark datasets. The project page is available at https://jin-ting-he.github.io/BlurDM/.

BlurDM: A Blur Diffusion Model for Image Deblurring
Jin-Ting He
Fu-Jen Tsai
Yan-Tsung Peng"
Min-Hung Chen
Chia-Wen Lin
Yen-Yu Lin-
National Yang Ming Chiao Tung University
"National Tsing Hua University
National Chengchi University "NVIDIA
jinting. cs cs12@@ycu.... edu. tw
sjtsai@gapp nthu. edu.tw ytpengåcs necu. edu. tw cwlin@ee. atu..edu.ttt tw linβes nycu. edu.
minhungednvidia.. com
Abstract
Diffusion models show promise for dynamic scene deblurring; however, existing studies often fail leverage the intrinsic nature of the blurring process within diffusion models, limiting their full potential. To address it, we present a Blur Diffusion Model (BlurDM), which seamlessly integrates the blur formation process into diffusion for image deblurring. Observing that motion blur stems from contin- uous exposure, BlurDM implicitly models the blur formation process through a dual-diffusion forward scheme, diffusing both noise and blur onto sharp image. During the reverse generation process, we derive dual denoising and deblurring formulation, enabling BlurDM to recover the sharp image by simultaneously de- noising and deblurring, given pure Gaussian noise conditioned on the blurred image input. Additionally, to efficiently integrate BlurDM into deblurring networks, we perform BlurDM the latent space, forming a flexible prior generation network for deblurring. Extensive experiments demonstrate that BlurDM significantly and consistently enhances existing deblurring methods on four benchmark datasets. The project page is available at https://jin-tingg- github.io//llrDM//
1 Introduction
Camera shake or moving objects frequently introduce unwanted blur artifacts in captured images, severely degrading image quality and hindering downstream vision applications, such as object detection [1] 41 semantic segmentation (3), and face recognition [12] 23). Dynamic scene
image deblurring aims restore sharp details from a single blurred image, highly ioo-po problem due to the directional and non-uniform nature of blur.
With the advancement of deep learning, CNN-based models 6 24 26
39
have demonstrated 50
remarkable success in data-driven deblurring. Additionally, Transformer-based approaches 2 13 21
42 49] have been introduced effectively capture long-range dependencies, further enhancing deblurring performance by leveraging global contextual information. Although previous methods have successfully improved deblurring performance, the inherent constraints regression loss typically lead to over-smoothed results with limited high-frequency details.
Recent advances in diffusion models 10 37 have demonstrated remarkable success in image generation, producing high-quality images with rich details and sharp textures through a forward noise diffusion followed by reverse denoising. Building on the success of diffusion models, several studies 4 17 28 29 47 have incorporated them into deblurring models to produce restored images. However, standard diffusion models are not specifically designed for deblurring. Thus, directly applying them to deblurring networks limits their potential, leading to suboptimal performance.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Forward Diflusion Process
Forward Diflasion Process
Blet Rocdued
Revense Generation Process
FULE Reciliel
Reverse Generation Process
Thl Rosiieal
Figure 1: BlurDM is a diffusion seffusiio-bbased network that leverages inductive bias of blur formation for dynamic scene deblurring. It progressively adds noise and blur in the forward process and iteratively estimate and removes them the reverse process to recover sharp images.
The limitation arises from a fundamental discrepancy between the diffusion process and the mo- tion blur formation process. Unlike random noise in the standard diffusion process, motion blur results from continuous exposure process during image capture, where blur intensity accumulates progressively along a motion trajectory. As result, motion blur exhibits structured and directed patterns, rather than the random noise perturbations modeled in conventional diffusion processes. To bridge this gap, we propose diffusion model that mimics the physical formation of motion blur, highlighting the continuous and progressive characteristics of blur formation. Instead of solely relying on standard noise diffusion, our approach incorporates blur diffusion mechanism, which gradually introduces motion blur to sharp images in structured manner. By leveraging the iterative nature of diffusion models, the proposed framework integrates the inductive bias of continuous blur formation, enhancing its ability to recover fine details and preserve image structures.
In this paper, we propose the Blur Diffusion Model (BlurDM), novel approach that aligns the diffusion process with the physical principles of blur formation to enhance deblurring performance. BlurDM adopts a dual diffusion strategy, combining noise and blur diffusion truly reflect the progressive nature of blur formation, as illustrated in Fig In the forward diffusion process, BlurDM progressively adds both noise and blur sharp images to generate blurred and noisy images. To achieve gradual increase in blur, it is crucial to gauge the blur residual, representing the incremental blur added as the exposure time extends. However, existing deblurring datasets primarily consist of shared-sharp image pairs without ground-truth blur residuals. To address this, BlurDM employs continuous blur accunulation formulation to implicitly represent the blur residual without relying on ground-truth blur residuals. This enables BlurDM to gradually blur images to align with the principles of the blur formation process.
In the reverse generation process, BlurDM aims to simultancously remove noise and blur to restore sharp images. To overcome the challenge of unavailable ground-truth blur residuals, we derive dual denoising and deblurring formulation that follows the principles of the blur formation process to implicitly approximate noise and blur residuals through dedicated noise and blur residual estimators. By effectively reversing the blur formation process, BlurDM can generate high- realistic, sharp images. However, when applied reconstruct details with high content fidelity due to their inherent stochastic nature [48). To address the limitations, inspired by 4 47 we utilize BlurDM as prior generation network to flexibly and
image deblurring, diffusion models may struggle to accurately
efficiently enhance existing deblurring models. Guided by the priors learned via BlurDM, deblurred models can achieve more accurate and visually consistent results. Key contributions of this work are summarized follows:
We present BlurDM, a novel iiffusion-based network that incorporates the inductive bias of blur formation to enhance dynamic scene image deblurring.
We propose a dual noise and blur diffusion process and derive a dual denoising and deblurring formulation, which allows BlurDM to implicitly estimate blur residuals instead of relying on the ground truth.
Extensive experiments demonstrate that BlurDM significantly and consistently improves four deblurring models on four benchmark datasets.
2 Related Work
2.1 Image Deblurring
Image deblurring has made substantial progress with the development deep learning. Numerous studies have explored CNN-based deblurring using recurrent architectures, such multi-scale 24

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391. multi-patch [51 52 and multi teltorlemporal [26] recurrent networks. For example, Tau et al. B39 develop a scale-recurrent network accompanied by a coarse-to-fine strategy for deblurring. Zamir et al. [SI] introduce multi-stage patch-recurren network that splits an image into non-overlapping patches for hierarchical blurred pattern handling. Park el. 26 designs temporallreeurren network that progressively recovers sharp images through incremental temporal training.
Transformer- methods 22 13
have recently garnered considerable attention for deblurring due to their capacities to model long-range dependencies. However, the substantial training data and memory requirements of Transformers motivate the development of efficient specifically tailored for deblurring. For instance, Zamir et al. [49 introduced channel-wise wise attention mechanism to reduce memory overhead. Tsai et al. [40] proposed a strip-wise attention mechanism to handle blurred patterns with diverse orientations and magnitudes. Kong et al. [3 presented frequency attention to replace dot product operations in the spatial
variants [13] 21 40
42 49]
domain with element-wise multiplications in the frequency domain. Mao et al. [2]] incorporated
local channel- attention in the frequency domain to capture cross-covariance in the attention mechanism.
Although the aforementioned advances have improved deblurring performance through various archi- tectural and algorithmic designs, the inherent constraints of using the regression loss 4 frequently lead to over-smoothed results with limited figh-freueency details, producing suboptimal deblurred images.
2.2 Diffusion Models
Diffusion models [10 38 have demonstrated remarkable capability in generating high-fidelity images with rich details through forward noise diffusion and reverse denoising, They have been leveraged to synthesize high-quality images under a
in numerous studies 20 22 25
27
3
variety of conditioning schemes.
For instance, Xia et al. [47 employed diffusion models to generate prior representations for clean image recovery. Liu et al. 19 utilized text prompts to compile task-specific priors across various image restoration tasks. Zheng et al. (54) proposed a selective hourglass mapping strategy to learn shared information between different tasks for universal image restoration.
Diffusion models have been applied to low-level vision tasks 9
14
19
47 54).
Recognizing the advances of diffusion models in low-level vision, researchers have extended their
Specifically, Whang al. 44 introduced a stochastic refinement diffusion model for deblurring. Ren et al. 30 incorporated a multi-scale structure guidance network within the diffusion model recover sharp images. Furthermore, several studies 3 28) employed diffusion models as prior generation networks and perform diffusion in the latent space to improve deblurring efficiency. For instance, Chen al. [4] proposed a hierarchical integration module to fuse diffusion priors for deblurring, while Chen et al. K incorporated these priors into window-based transformer blocks. While these methods effectively reduce diffusion model latency for deblurring, they overlook the intrinsic characteristics of the blurring process within the diffusion framework, limiting their full potential.
to image deblurring [3] 4 15 16
17
28
30
44
Although Liu et al. 17 proposed residual diffusion by computing the difference between sharp and blurred images using subtraction operation, the blur formation process is inherently convolutional process rather than direct additive difference, making this approach insufficient for accurately capturing blur characteristics. In contrast, propose novel framework that incorporates the blur formation process into the diffusion model, leading to significant deblurring performance improvements.
3 Proposed Method
We propose Blur Diffusion Model (BlurDM), novel diffusion framework for image deblurring. Unlike existing methods 4 28 29 47). which rely only on noise diffusion, BlurDM integrates a blur diffusion process, incorporating blur formation into diffusion to improve deblurring performance.
As shown in Fig. D we progressively add both noise and blur to a sharp image through a dual noise and blur diffusion process during forward diffusion. In the reverse process, BlurDM jointly denoises
3

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Figure 2: Overall framework of the proposed method. (a) Stage I: Pre-train the Sharp Encoder (SE), Prior Fusion Module (PFM), and the deblurring network obtain the sharp prior 25. (b) Stage Optimize the Blur Encoder (BE) and BlurDM to learn the diffusion prior Zo from blurred image. (c) Stage Jointly optimize the BE, PFM, BlurDM, and deblurring network generate the final deblurred image.
and deblurs the image, starting from Gaussian noise conditioned on the blurred input. Ultimately, we BlurDM prior generation network to retain the diffusion model's 's ability to learn high-quality, realistic image content while embedding the learned prior into the latent space of a deblurring network for effective and high-fidelity restoration, as shown in Fig. 2 Next, detail the key components of BlurDM, including the dual noise and blur diffusion process, dual denoising and deblurring formulation, and network architecture.
3.1 Dual Noise and Blur Diffusion Process
Motion blur in the image capture process is introduced from continuous exposure, where the camera sensors accumulate light over the exposure duration, causing the blending of moving elements along motion trajectories and leading to gradual build-up in blur. This process can mathematically represented as B œσ - H(T) dr, where B E H x3 H(T) E IR. H x3 and denote the blurred image, the instantaneous scene radiance at each moment and the total exposure time, respectively. This models the blur formation process, showing how continuous light integration during exposure results in the accumulation of motion blur. Building on the understanding of the blur formation process, we propose dual-diffusion framework that incorporates the blur formation framework into noise diffusion. That is, sharp image is progressively corrupted by both noise and blur the forward diffusion, capturing the concept blur degradation introduced during continuous exposure.
To differentiate between images captured at varying exposure periods, define sharp and clean image lo. obtained within a short, proper exposure time ag (Ap < (r),, as 4o = 1 f H (T) dr, where Io E Nx represents the sharp image captured with minimal blur. The contrast between B and To indicates the effect of exposure duration on motion blur, meaning longer exposure or introduces more blur, whereas a proper exposure ag yields a sharp image. Our objective to progressively add noise and blur to To based on the blur formation process. The dual noise and blur diffusion process the next time step can be defined as
oo
1 01
( H(T) + + H(T)dT) + Biel
11
H(T) di + BiEi
(I)
a1
7=00
0k Lo
H(r)dr + 316 00 To + G1 3161,
+ 01 T=as
(2)
where 1 represents the intermediate blurred and noisy image, corresponding to the exposure period from 01 (og < 01 < orr), 61 pure Gaussian noise, 81 denotes the noise scaling coefficient, and = - (r))dr is the blur residual that accumulates from ag to 01. Based on (2) the forward transition at time t is defined as
I 0:-1 I--1+
es + Bic.
(3)
an

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where E, ~ (O, I) and e₁ = --= dT) denotes the blur residual accumulating from d;-1 to Og during the exposure process.
From ( each forward step from 4--1 to I is Gaussian transition, where the blur residual et introduces deterministic mean shift to the distribution. Specifically, the transition distribution is
ale ex) = .V ~N(+ 0t-1 4-1
at
at
By iterating we generate a sequence of progressively blurred and noisy images (T1 I2.... Ir] through 7tepp diffusion process. The complete forward sampling probability is therefore given by
T
q(her lo, C1.T) = II a(2 1.) t=1
(5)
However, existing deblur datasets typically consist of blurry-sharp image pairs without providing corresponding blur residuals. address this limitation, we reparameterize (5) to obtain the conditional probability distribution g(Tr /o. e1:T) [00], as shown in (6). The full derivation is provided in Appendix A.T
(1
(o (1:)) - N'
Iri
et
3
where Br
Σ
at
8
(6)
OT
OT
OTT
The final blurred and noisy image IT can be sampled from the distribution q(Tr) To, Ci.r) as
000 + OT Σ e Bre Bre=
1 or GT J H( + B B BTE,,
IT
TT
where the final blurred and noisy image IT can be generated in a single step by adding noise to the input blurred image B. This formulation preserves the Gaussian nature of the diffusion process while embedding the blur information directly into the mean of the distribution through a physically grounded shift. Next, detail the dual denoising and deblurring formulation for the reverse generation process.
3.2 Dual Denoising and Deblurring Process
In the reverse generation process, we aim to progressively remove both noise and blur from the degraded image IT recover the sharp image Ig. based on our dual denoising and deblurring framework. Unlike standard diffusion models that start from pure Gaussian noise, our method samples the terminal observation Ir from Gaussian distribution. (Ir;; B, 3 I), where blurred input image. To reconstruct In. we use a blur residual estimator e (L,, B) and noise estimator e (L,, t, B3) approximate the respective components, e; and er, each step.
a fully
Inspired by the deterministic sampling formulation in DDIM we define the reverse transition distribution
pe (1 1)) = (4--1 Le 18: ei,), where 1
at ex0
1e
04
3
(8)
an
1=1
The transition probability is defined as
9o (Ie- Le. cqx)
(9)
I
Ct T
+
5 Σi=
c
=N 1-11
ao 0,11
To
+
72
0
B
8: 6
with variance term of simplifies the reverse step to
1)
When r = 0. this yields a deterministic sampling, which
c (I,, B) - an
T = 0t
I
B.-)) (I,,)
(10)
a111
0
5

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(b)
(c)
Pluch Liskatte
Con 3 X
Cony 3 x
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Axg Poil
Linear
Leaky Reiu
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2
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Figure 3: Architecture of the Sharp/Blur Encoders (a), Blur/Noise Estimators (b), and the Prior
Fusion Module (c).
Complete derivations of the variational lower bound, optimization objectives, and sampling formula- tion are provided in Appendix [A.2] and Appendix [A.3] In the following, we detail the optimization of the blur residual estimator e" and the noise estimator e" in the latent space for BlurDM.
3.3 Latent BlurDM
To efficiently integrate BlurDM into deblurring networks, we develop it in the latent space, where it serves a prior generator enhance existing deblurring methods. Inspired by previous work 44 47), we adopt three-stage training strategy for effective integration, as illustrated in Fig, 2 This process guides the latent features to capture physically meaningful representations for blur residuals, allowing the model to encode exposure-awen aware information in the latent space.
First Stage. We begin by pre-training the deblurring networks with the Sharp Encoder (SE) and the Prior Fusion Module (PFM). Specifically, given a blurred image E R. W xs and its sharp counterpart S I "x x3 concatenate them to feed into the SE to obtain the sharp prior as 2 SECCCccte S)) E RIxIxC Subsequently, we fuse z with the decoder features F; E RA, Xw, each scale of deblurring network using PFM, generating the fused features F of the i-th scale. Specifically, PFM generates the affine parameters 75.0; R Rixxxe and S E IR gxxxx: from 2 by linear transformation, and modulate F as
(7 2.. Linear(Z*)
F = x F. + S
(11)
where x and + denote channel-wise multiplication and addition, respectively. Thus, we can generate deblurred image O E Hx enhanced by the sharp prior 75, by supervising O with the sharp image S.
Second Stage. Since sharp images are unavailable during testing, we estimate the sharp prior 2 from the blurred image using the proposed BlurDM, treating 2 as the ground-truth prior at this stage. To achieve this, we employ Blur Encoder (BE), structurally identical to SE, to generate 78 E I xxxx from B. Next, we introduce noise into 7B following (6) to obtain 7,B. defined as z B + Bre, aligning the diffusion process with blur formation. Finally, we iteratively remove both noise and blur from Zí using (10) to generate the diffusion prior 2/ via
B
Ott zB
e (ZB 112B,,220 23
B.-1)e 22
(12)
at
ar-1
which is used to estimate the sharp prior 5 Recent studies 35 reveal that supervision on the final output effectively influence the entire diffusion trajectory. We define latent-prior loss Cprior = - - where 2 is obtained by recursively removing estimated blur and noise residuals from I, via the shared estimators e" and ca. By back preck-roppggaing Eprirr gradients are distributed across all reverse steps, furnishing amortised trajectory-Iewe supervision without step-wise labels. Further details are provided in Appendix A.2
Third Stage. We jointly optimize the pre-trained BE, BlurDM, PFM, and deblurring networks from the first and second stages to generate the deblurred image O, ensuring that the learned diffusion prior 2 effectively enhances deblurring performance. We supervise with using the loss function originally designed for the given deblurring network. After this stage, the final model we obtain consists of BE, BlurDM, and the deblurring network for inference. We further provide the theoretical justification of latent BMurDM in Appendix A.4

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Table 1: Quantitative results GoPro, HIDE, RealBlur-J, and RealBlur-R datasets, where "Baseline" and "BlurDM" denote the image deblurring performances without and with BlurDM, respectively. Arrows indicate the direction of improvement (PSNR1, SSIM1, LPIPS4).
GoPro
HIDE
RealBlur-J
RealBlur-R
Method
PSNRT
SSIMt LPIPS_
PSNR1
SSIM
LPIPS
PSNR
SSIM LPIPS
PSNRT SSIM LPIPS.
Bascline 32.44
0.957 0,0115
30.00
0.930
0.0217
31.59
0.918
0.0345
39.03
0,968 0.0215
MIMO-UNet
BlurDM 32.93
0.961 0.0091
30.73
0.939 0.0168
32.13
0.926
0.0264
39,63
0.972 0.0172
Baseline 33.09
0.962
0.0085
31.03
0.940
0.0147
32.48
0.929
0.0222
39.84
0.974
0.0138
Stripformer
BlurDM 33,53
0.966
0.0074
31.36
0.944
0.0122
33.53
0.938
0.0175
41.00
0.977 0.0115
Baseline 34.21
0.969
0.0067
31.62
0.946
0.0153
32.62
0.933
0.0220
40.11
0.973
0.0149
FFTformer
BlurDM 34.34
0.970
0.0060
31.76
0.947
0.0145
32.92
0.939 0.0195
40.55
0.975
0.0136
Baseline 33.54
0.966
0.0084
31.18
0.943
0.0176
32.23
0,932
0.0223
40.36
0.974
0.0148
LoForner
BlurDM 33.70
0.967
0.0073
31.27
0.944
0.0158
33,47
0.941 0.0189
40.92
0,976 0.0127
Average Gain
+0.31
+0.003
-0,0013
+0.32
+0,004
-0.0025
+0.78
+0.008
-0.0047
+0.69
+0.003
-0.0025
4 Experiments
4.1 Experimental Setup
Implementation Details. Fig. illustrates the architectural design of the four components in BlurDM: the Sharp Encoder (SE), Blur Encoder (BE), BlurDM, and Prior Fusion Module (PFM). Specifically, SE and BE have the same network architecture, each with six residual blocks, four CNN layers, and two MLP layers. BlurDM contains noise and blur residual estimators, each comprising six MLP layers. PFM consists of one MLP layer. We empirically set T = 5 in BlurDM, with 31.T increasing uniformly from 0 to 0.02 and og:: increasing uniformly from 0 to 1. The overall framework (Third Stage) is optimized using the default training settings of each deblurring model, including learning rate, number of epochs, batch size, optimizer, etc., ensure fair comparisons.
Deblurring Models and Datasets. We adopt four prominent deblurring models, including MIMO- UNet (5) Stripformer 40. FFFformer [13 and LoFormer (21), to validate the effectiveness of BlurDM. Following previous work [5] 13, 21 40), we adopt the widely used GoPro [24] and
HIDE (36) datasets. The GoPro dataset contains 2, 103 image pairs for training and 1, 111 image pairs
for testing, while the HIDE dataset contains 2, 025 image pairs used only for testing. Additionally, we
utilize the real-world RealBlur (3] dataset, which contains RealBlur-J and RealBlur-R subsets. Each subset contains 3, 758 training pairs and 980 testing pairs, with RealBlur-J in JPEG and RealBlur-R in Raw format.
4.2 Experimental Results
Quantitative Analysis. As shown in Tab. [ we compare the deblurring performance of four base- lines and their BlurDM-enhanced versions, where "Baseline" and "BlurDM" refer to the deblurring performance without and with BlurDM, respectively. The results indicate that BlurDM consistently and significantly enhances deblurring performance, yielding average PSNR improvements of 0.31 dB, 0.32 dB, 0.78 dB, and 0.69 dB on the GoPro, HIDE, RealBlur-J, and RealBlur-R R test sets, respectively. Additionally, BlurDM achieves average PSNR improvements of 0.59 dB, 0.75 dB, 0.25 dB, and 0.51 dB on MIMO-UNet, Stripformer, FFTformer, and LoFormer, respectively. Notably, BlurDM achieves substantial performance gains, up to 0.73 dB, 1.16 dB, 0.44 dB, and 1.24 dB for MIMO-UNet, Stripformer, FFTrrrerr, and LoFormer, respectively. On average across all backbones and datasets, BlurDM achieves overall gain of 0.53 dB in PSNR, 0.004 in SSIM, and reduction of 0.0028 in LPIPS. These comprehensive quantitative results demonstrate that BlurDM substantially enhances the performance of deblurring models across diverse datasets, highlighting BlurDM's effectiveness and robustness as a flexible prior generation network for image deblurring.
Qualitative Analysis. We provide qualitative comparisons of four baselines and their BlurDM- enhanced versions on the GoPro and HIDE test sets in Fig. 4 and RealBlur-J test set in Fig. 5 The results show that BlurDM consistently produces sharper and more visually appealing deblurred results than "Baseline." By integrating BlurDM into the latent space of deblurring network, we leverage its ability to learn rich and realistic image priors while preserving the network's 's fidelity to sharp image contents.
7

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Figure 4: Qualitative results the GoPro (left) and HIDE (right) datasets.
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Figure 5: Qualitative results on the RealBlur-J dataset.
ISTAIBUL BATUM
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Figure 6: Deblurred results 15 to 4o from latent features 2 to 2 showing reduced blur as reverse
steps increase.
4.3 Ablation studies
To evaluate the effect the proposed components in BlurDM, we adopt MIMO-UNet as the baseline deblurring model and analyze its performance under various ablation settings. Specifically, we analyze the effectiveness of noise and blur residual estimators, compare different prior generation methods, analyze blur residual modeling in the latent space, inspect the effect of iteration counts, ie., T, BlurDM, compare different diffusion- bassed methods, analyze the effectiveness of each training stage, and measure the computational overhead introduced by BlurDM. All experiments conducted with 1, 000 training epochs used.
Effectiveness of Noise and Blur Estimators. We evaluate the effectiveness of the noise and blur estimators in BlurDM through ablation study shown in Tab. 2 "Netl" denotes the baseline deblurring model. "Net2" represents a conventional DDPM-based design using only the noise estimator. "Net3" denotes BlurDM variant that estimates blur residuals but omits the noise component. "Net4" is our complete BlurDM design incorporating both estimators. As can be seen, both "Net2" and "Net3" improve performance over the baseline, which demonstrates the individual benefit of noise and blur estimation. Moreover, "Net4" achieves the best result, showing that combining both estimators yields complementary gains. These findings further confirm the importance of explicitly modeling blur residuals to improve deblurring effectiveness.
Comparison of Prior Generation Methods. We evaluate the performance of the baseline deblur- ring model enhanced by various prior generation methods, including MLP, DDPM [0) RDDM [17], and our proposed BlurDM, on the GoPro and RealBlur-J datasets (see Tab. 3 "Netl" denotes the baseline model without guidance by prior generation network. "Net2" denotes the deblurring model enhanced with MLP layers, without using diffusion process. "Net3", "Net4", and "Net5" corre-

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Table 3: Comparison of different prior generators on GoPro and RealBlur-J datasets in PSNR.
Table 2: Effectiveness of the noise estimator and the blur estimator on the GoPro test set.
Prior Generators
GoPro
RealBlur lurJ
Noise Estimator Blur Estimator PSNR
Netl
N/A
31.78
31.59
Netl
31.78
Net2
MLP
31.90
31.84
Net2
31.91
Net3
DDPM
31.91
31.85
Net3
32.20
Net4
RDDM
32.03
31.90
Net4
32.28
Net5
BlurDM
32.28
32.13
spond to the deblurring models enhanced by different diffusion-bal based priors, including DDPM [0] RDDM [17. and the proposed BlurDM, respectively.
While integrating the standard diffusion process (DDPM) into the deblurring model improves per- formance compared to the baseline ("Net3" vs. "Netl"), the gain is comparable to that of "Ner2," which uses the same MLP structure without diffusion. This suggests that the standard diffusion process alone contributes little to deblurring performance. In contrast, BlurDM explicitly incorporates the blur formation process into diffusion, leading superior performance over both the standard diffusion- baadd prior ("Net5" vs. "Net3") and the residual diffusion prior (RDDM), which lacks the proposed blur-aware diffusion mechanism ("Net5" "Net4").
Analysis of Blur Residual Modeling in Latent Space. To verify whether BlurDM models blur formation in the latent space, we analyze outputs at different reverse diffusion steps during inference. While the model is trained with T 5 steps, we evaluate intermediate latent representations by 00, 1, 2, 3, 4. 5. reverse steps from the fully blurred latent Z,"², yielding a sequence performing ... Each Z, is decoded into an image I via the deblurring network. As illustrated in (25 " ...., Z
Fig. [ the outputs transition progressively from blurred (Iş) to sharp (4n), confirming that BlurDM's latent representation captures a progressive blur-to-sharp structure and enables interpretable modeling
in latent space.
Effect of Iteration Counts in BlurDM. Compared to the standard diffusion model 10] which requires thousands of iterations in the reverse generation process, applying diffusion networks in the latent space has proven effective in reducing the number of iterations examine the effect of different iteration counts used in BlurDM on deblurring performance, as shown in Fig. Specifically, we test eight iteration settings T E {0, 1, 2, 4, 5, 6, 8, 10] and evaluate their deblurring performances on the GoPro test set. The results show that BlurDM significantly improves performance with two iterations and reaches peak performance after five, showcasing its ability to achieve substantial and stable performance gains with only few iterations.
47). Therefore, we
Analysis of BlurDM's Computational Overhead. We present the computational overhead in- troduced by BlurDM in Tab. 4 measuring FLOPs and inference time on 256 x 256 image using an NVIDIA GeForee RTX 3090. The results show that BlurDM introduces only a slight increase in computational complexity while significantly improving deblurring performance. Specifically, BlurDM adds an average of just 4.16G FLOOPs, 3.33M parameters, and 9 milliseconds across four deblurring models, demonstrating its effectiveness with minimal overhead. Note that the number of parameters varies across different deblurring models, as PFM must adapt to the varying channel dimensions of each decoder.
Comparison of different diffusion-based methods. We compare BlurDM with recent diffusion-basee approaches, HI-Diff (4 and RDDM (18), in Tab. 5) HI-Diff follows the conventional diffusion process in the latent space, where Gaussian noise progressively injected directly into the latent until it becomes pure noise, and a Iearned reverse process is used to reconstruct a clean latent for deblurring. RDDM first forms an image space residual by subtracting the clean image from the degraded one, then performs diffusion on the clean image that jointly models this residual and Gaussian noise. Both HI-Diff and RDDM neglect the physics of blur formation. BlurDM addresses this gap by explicitly integrating the blur formation process with diffusion, executing dual noise and blur diffusion that matches the physics of blur accumulation. With comparable parameter counts and FL.OPs, BlurDM consistently delivers higher PSNR and SSIM. As plug-and-play module, it integrates seamlessly with diverse backbone architectures, demonstrating both strong performance and broad generalizability.

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PSNR Time Step
Table Computational overhead comparison be- tween baseline deblurring models and their BlurDM- enhanced versions.
21
30.21 12
20.2
10104
2014
PSNS
22.1
Method
FLOPs (G)
Params (M)
Time (ms)
33.0
Baseline
153.93
16.11
MIMO-Unet+
+BlurDM
158. 10 (+4.17)
18.29 (+2.18)
(211)
Sosetion
Psta Seadine
Stripformer
Baseline
170.02
19.71
+BluDDM
174.18 (+4.16)
24.33 (+4.62)
55 (+7)
Time Step
Baseline
131,53
14.88
131
FFTformer
+BlurDM
135.69 (+4.16)
18.6 (+3.78)
141 (+10)
Figure 7: Effect of the number of iterations in BlurDM on deblurring performance in PSNR on the GoPro dataset.
Baseline
52.19
16.35
93
LoFormer-S
+BluDMM
56.35 (+4.16)
19.08 (+2.73)
(+6)
Table 6: Effect of each training stage on the GoPro dataset.
Table 5: Comparison of different diffusion- based
methods on the GoPro dataset.
Model
Stage
Stage 2
Stage 3
PSNK
Method
PSNK
SSIM
Farams (M)
FLOP's (G)
Net1
31,78 (baseline)
33.35
0.355
23.59
125.47
Net2
32.69 (upper bound)
RDDM
12.40
0.963
15.49
134.20
Net3
3
BlurDM (Stripformer
174.18
BlurDM (HFToomme)
34.34
0.700
18.66
135
Ne14
32.01
BluDDM (LoFormer)
33.70
0.967
19.08
56.35
Net5
31.95
Net6
32.28
Effectiveness of Each Training Stage. We evaluate the effectiveness of the three-stage training strategy, as shown in Tab. []] "Netl" denotes the baseline deblurring performance without incorpo- rating BlurDM. In "Net2". , the ground-truth sharp image passed through the Sharp Encoder to obtain the sharp prior 75, which is then used by BlurDM. This setting serves an upper bound on achievable deblurring performance with an ideal prior. In "Net3", we jointly optimize BlurDM and the deblurring model without pretraining through Stage 1 and Stage 2, serving as a baseline for a purely data-driven approach. In "Net4" and "Net5", after completing pre-training in Stage 1, we apply either Stage 2 or Stage alone to optimize BlurDM and the deblurring model. "Net6" employs the full three-stage training pipeline, achieving the highest PSNR among all settings. These results clearly demonstrate the effectiveness and necessity of the proposed three-stage training strategy in improving deblurring performance.
Limitations
Since BlurDM is designed based on the motion blur formation process, it effectively handles blur caused by camera motion and moving objects. However, may not be well-suited for handling defocus blur, which arises from optical aberrations due out-of-focus issues. Unlike motion blur, defocus blur is depth-dependent and does not exhibit the same temporal accumulation properties, making it fundamentally different in nature. Addressing defocus deblurring would require distinct approach, potentially incorporating depth estimation or optical defocus modeling, which remains an open direction for future research.
6 Conclusion
We proposed Blur Diffusion Model (BlurDM), a novel diffusion-basel framework for image deblur- ring. BlurDM integrates the blur formation process into the diffusion framework, simultancously performing noise diffuusin and blur diffusion for more effective deblurring. In the forward process, BlurDM progressively degrades sharp image by introducing both noise and blur through a dual noise and blur diffusion process. Conversely, in the reverse process, BlurDM restores the image by removing noise and blur residuals via its dual denoising and deblurring process. To enhance the performance of existing deblurring networks, we incorporated BlurDM into their latent spaces as prior generator, seamlessly integrating the learned prior into each decoder block via our proposed Prior Fusion Module (PFM) to generate higher-quality deblurring results. Extensive experimental results have demonstrated that our method effectively improves deblurring performance across four deblurring models four deblurring datasets.
10

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7 Acknowledgments
This work was supported in part by the National Science and Technology Council (NSTC) under grants 002-008, and
112-222-EEA44--000-V 133-2221-E-004-001-110 113-2634-F. and by the NVIDIA Taiwan Al Research & Development
Center (TRDC).
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Question:
Answer: [Yes]
Justification: We use publicly available datasets (GoPro, HIDE, RealBlur-J/R) and existing models (e.g., MIMO-UNet, Stripformer), all of which properly cited in our paper with corresponding references. The datasets are commonly used the community and their licenses respected as per the information provided by the original sources.
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20

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A Appendices
A.1 One-Step Diffuusio Derivation for BlurDM
In the dual noise and blur diffusion process of BlurDM, the forward process is defined as:
T
(lrrr (1,T))) = II
alle 1-)
1=1
(13)
dle 6x) =- N
1::
0tt1 1--
1 - Ct. 3
where e, = = (T) dr is the blur residual accumulated during the exposure interval [a--1, az]
We now expand the full forward process by recursively substituting the previous states. Starting from the last time step as
I QT-1
I--1
+ 7T
+
STCT
at
0T-1
0T-22 IT-2
1
Cr-1 + 11-16T-1 +
cr + Brer
QT
QT-1
QT-1
QT-2 Ir 1r-2 +
1
QT-1 BT 16T BTeT
(ET-1
+ eT)
OT
oT
oT
T
To
Ct
(14)
OT
OT
OT
Since ~ No,, I) are independent for all t, their weighted sum remains Gaussian with zero mean. The resulting variance of this sum is
Ol2
82
CT
which allows to reparameterizee as:
Biq Bre, c~ N(O.I).
QT
Moreover, combining the clean image component and the accumulated blur residuals, we observe that
T c
= ao
or
αT H(T) dr 1 1 or H(T) B,
10
H(T) dr
QT
OT
ag
where B denotes the fully blurred image formed by integrating the instantaneous scene radiance ([T) over the total exposure time interval [0, ar).
Thus, the forward process simplifies to single-step form:
T= B + 3rc.
(15)
with the corresponding marginal distribution
(Ir To, 1o,e -N N
[Ti
Cto
3
(16)
CT
aT
This one-step form is mathematically equivalent to the full forward process, while providing more computationally efficient approximation that captures both accumulated blur and noise in a single Gaussian transition.
21

---

A.2 ELBO and Optimization for BlurDM
To reconstruct the sharp image Ig from the degraded observation Ir, we adopt the variational inference framework of DDPM [10] and derive an evidence lower bound (ELBO) that explicitly
incorporates the blur residuals CT. The joint ELBO is expressed as
log po 2 (.((hrll
pollor)) log q(1r (1:11/)
LELBO-
(17)
By unrolling the Markov chain formulation in DDPM, we can rewrite the objective as:
po (Jo.T)
CELRO
B
log
(18)
(11.T
To: (1:)
log Po Pa(1111 Tt)
Eg
log po (Tr)
(19)
q 11-1.0))
log po (1(11-1 [c)
log Po 11)
Eg
log po (IT)
(20)
q11 11-1,
7(11 [o)
po(11-1 10)
q(I111 Lo, c1a)
Po 11)
Eg
log po (Tr)
log q(1--1 It, C1:t)
log
g(I e1:))
q(1 lo. e1)
(21)
(I(
po ( II) log [1)
Eug
log (T
log q( I,s lo, e1:))
(22)
To, (1T)
Rewriting (2 in terms of KL-divergence yields:
LELBO
(23)
g
DKL (
(q([r)
To: (1:1) Po(T)) Dkalo(te-1 16 Jos e1a) po 4)) - Po 11)
(24)
Unlike standard diffusion models, which assume standard Gaussian prior at the terminal state, our model defines the prior distribution
po(1))=N(1) - N(T; B, B.
where B denotes the physically blurred image obtained from full exposure integration. This for- mulation ensures that the terminal distribution is aligned with the mean of the forward marginal q(T To, c.r). Therefore, our prior is not an arbitrary isotropic Gaussian but a noise-perturbed version of a blurry observation, consistent with the forward process structure.
Given this structural alignment between the forward marginal and the prior, we follow the standard ap- proach of DDPM [10] and DDIM [38), and omit the terminal KL divergence DKI. (g(T To, e1:r) (T)))) during training. Instead, we retain only the stepwise KL. divergence terms, which capture the discrepancy between the reverse and forward transitions each timestep as:
DKL(g(h--1 4, lo. e14) po(4-1 12)).
(25)
To compute these terms, we derive the posterior q(T,-1 Te, To, EIa) using Bayes' rule:
q(r-1 Je, eIx)) = g(Te J--1, To, e1x) gih-1 e1a)
(26)
q(T To, G1:))
From 6
we have
I1-11 -1-15_11 lo
q(1-1 [1,) =
Eis B-11
04-1
ax-1
22

---

From 5 the forward transition is expressed as
g 6x) 14 0t-1 T-- + -e; Bi
-N(1::
1
04
a8
Combining the above, we obtain the posterior
g(te-1 Ie, To: 612) =N (
To, (1s), (T To G1+
(27)
x exp (-1 (1e of 4-- 4 e.)?
+ (T--1 m lo 7
E1st e.
(1
1o a1 _ 62)
(28)
which can be further simplified to
x exp (-
t-1
8787
8
1
-
0t-1 0.
It
at 00
Ci
+
2c-188_1
To +
a1-18
Σ
cj 4
1m
(29)
where C = C= Jo, eia) denotes the terms not involving 1--1-
From 29 the posterior parameters are given by
0r a
Tt
aP Ce
+
18.
To
187
2i=1 Ci
(T, e1.)
=
(30)
a1 1
0t
B
-
Et
E,
(31)
0t-1
Ot
0r-1
B1
8 32
(e ela)
(32)
where TT, To, (1:)) denotes the posterior mean, which is derived by combining (6 and 4 through
the product of Gaussian densities. of (Te, To: C1a) represents the corresponding posterior variance.
We model the reverse process beginning at po (TT = (Tr; B, 141,
and define
p0 4.) = = Le 12.)
In our setting, since the variances of the two Gaussian distributions are matched exactly, the KL divergence reduces to squared difference between their means, as is standard in DDPM [10]. Accordingly, the KL divergence term in (5) reduces
D I, Iu, 61:)) ( I0) [| --11111
(33)
where the mean of the true posterior is given by
at 1t
1
at B2
Ut 04-1
-6t
E,
Ot
04-1 Be
and the modelepredicted mean is
Cx I
Ot O B).
Oz
a-1 Br
where e t, B) and e" (le, t, B) denote the learned blur residual and noise estimators, respectively.
Based
and 333
we can derive the following optimization objectives
Tee, (0) E
Ae llez eg (T,, B3
(34)
L. (0) Nr ( ( - BB|
(35)
Thus, the optimization of BlurDM reduces to minimizing the combined loss in 34 and 35 which directly supervises both the blur residual estimator and the noise residual estimator through their respective ground-truth signals.
23

---

End-to-End Trajectory Supervision via Final Reconstruction Although per-step ground-trute blur residuals are unavailable, recent diffusion- based research the generated results is sufficient to train the diffusion model.
47 has shown that supervising
Following this line of evidence, train BlurDM with the reconstruction objective
Fre 1 loll
(36)
where rø is obtained by successively denoising the degraded observation IT through T learned reverse steps.
At each step & E [f,....,], 1}, the network predicts blur residual es = e (L,,t B) and noise residual ?i = e (1,, t, B), then reconstructs the previous latent state via
Ot 3 3
1
Ot 1.
E
c
(37)
0x-11
0x
at
This yields the unrolled trajectory
1 IT,
(38)
1 98 (1.))
t= I,..,
(39)
1 of of
(40)
where every step operator oeº shares parameters 0. Backpropagating Lie supplies gradients to all intermediate residual predictions - allowing amortized trajectory level optimization despite the lack of explicit stepwise supervision. This strategy mirrors the unrolled inference paradigm in generative models based on variation and scores and empirically produces stable convergence with strong deblurring fidelity.
A.3 Deterministic Implicit Sampling for BlurDM
In this section, we provide formal derivation to demonstrate that our deterministic reverse process go (T=- Ie, Cia),
preserves the forward process distribution defined in i...
q(A lo. G1.)) N
Ie: Ok
Lo
C 3
at
ai
We follow the approach of DDIM 38 and proceed by mathematical induction from t T to 1. Assuming that the marginal distribution q4, Io, e1.0) is valid step t, aim to prove that sampling T from g (J=1 Te, Tg, exa) yields distribution consistent with
1-1 ao t Σ cs (e.PP_1)) [1-1:
a(h-1 To, (1a-1)= = WN
a-1 ar- i=1
Let us begin by rewriting the marginal at time t
qI To, 61.1)
.V
0g Ie:
+
1
3
(41)
ag
ai
We define the reverse transition distribution using the deterministic implicit sampling formulation: Go (T.-1 Jr, To, e1a)
(42)
I
1 an
a +
5 El
e
N
I--1 Ct-1
a0
1o +
Σ es +
32 1-1
o
(43)
04-11
3
Bt-1
24

---

We now compute the implied marginal distribution over 1 by integrating out It, using the properties
of marginal and conditional Gaussians. Let the conditional be p(4--1 1,) and the marginal g(x)). then the marginal of 7-11 becomes
14-1 lo, e1a) e0, - - (0 1 1,, lo era)) (44 lo, eva)
By applying the formula for Gaussian marginalization linear Gaussian transformations, obtain
Mean:
c0 lo + 1 Ei=res) 00
e.) -
Io
1
Ei= Ej
M4-1
00
Io
+
C +
32
of
as
04-1
04-1
Br
(44)
00
To +
Bi1
(45)
04-1
0tt-1
i=1
2237__ 88
with variance term of
=1-
When 1) = 0, this yields a deterministic sampling.
Variance:
2
P of
0 I I,I+
311
(46)
3r
Bh of B3
I +
B.I
(47)
B
(48)
Hence, the marginal distribution at step 1-1 becomes
q(T--1 I To, 012-1) 11) [1-1:
00
To+
+
Σ ess 11-11
04-1
04
which confirms that 6 holds at step 1-
By induction, the deterministic sampling formulation maintains consistency with the original forward process distribution every timestep.
Derivation from to 100
Based on we can sample T,-1 from go (T:-1 Te, T8, ci.) as
4 - an
+ 1 Σi= c!)
1--1 1
0k0
B
of
tr, (49)
04-1
Ot
B
where of
we set 1) O for the deterministic sampling. In addition, we substitue 7 can be rewritten
7
with (8. Therefore,
49
Ck0
04 T
at
7-1
B
+
(50)
a:-1
ag
00
Cp
at-1
i=1
1=1
I
(0g
I
1 nt
c
ar
B,c
1 1 01
+ 3
Liz
(51)
31
04
I
8
( B)
3
dc (J,,t, B).
(52)
Q4-1
a1-1
04-1
25

---

Table 7: Comparison of training cost and performance between baseline and BlurDM.
Method
Training epoch
Training time [h]
PSNR [dB]
Baseline
3000
66.7
32.44
Baseline
6000
133.4
32.51
BlurDM
1500
(Stage + 500 (Stage 2) 1500 (Stage 3)
70.7
32.62
BlurDM 2 3000 (Stage 1) 500 (Stage 2) + 1500 (Stage 3)
104.1
32.71
BlurDM 3 3000 (Stage 1) 500 (Stage 2) + 3000 (Stage 3)
141.4
32.93
A.4
Theoretical justification of latent BlurDM.
Let & E(4) be the latent feature produced by encoder E(-) from the blurred image 1; at exposure step t. A first-order Taylor expansion E' around 4--1 gives
24 - Jg(4-1) (4: 1--1),
(53)
where J (Tt1) is the Jacobian of the encoder at I--1. From the image-space exposure model,
I 4--4- 1--1 06-1 - 1) 4-- de + E,
(54)
de
with e the blur residual and E, the stochastic noise at step t. Substituting yields
2, ~ - + J=((6-1) [( as-1 - 1) 4-11 + + + BeSt
(55)
When exposure increments are small so that de-1 ~ 1, the term proportional to I-11 vanishes and we obtain the latent-space dynamics
Or-1
2-1 21
Bee
4 -=JJ((h-1)e)) J Ets
(56)
where 0 denotes the learnable parameters of E. Thus, blur residuals and stochastic noise in image space projected into the latent space with the same coefficients, justifying that the two estimators in BlurDM can learn these terms directly in the latent domain.
A.5 Training cost of the three-stage training strategy
In Tab. 7, we report the training complexity introduced by BlurDM in terms of training time, using MIMO-UNet as the backbone on GoPro dataset. The default number training epochs for MIMO- UNet is 3, 000, denoted as "Baseline 1". Increasing the training epochs to 6, 000, denoted as "Baseline 2", results in only a marginal improvement of 0.07 dB in PSNR. For BlurDM, we experiment with different training configurations, including using 1, 500 or 3, 000 epochs for Stage and Stage 3, with 500 epochs for Stage Both "BlurDM 1" and "BlurDM 2" outperform Baseline while requiring less training time, demonstrating the effectiveness of the proposed three-stage training strategy. Finally, we adopt "BlurDM 3" as our final model, which increases training time by only 8% while achieving 0.42 dB improvement in PSNR compared to "Baseline 2".
A.6 Performance comparison of the number of steps used in RDDM and BlurDM
Tab. presents comparison of RDDM and BlurDM under varying numbers of diffusion steps. where PSNR and LPIPS are used to assess performance. RDDM achieves its peak PSNR of 32.08 dB step 4 and the best L.IPS score of 0.0121 step 10. In comparison, BlurDM achieves higher peak PSNR of 32.28 dB at step 5 and lower LPIPS score of 0.01 13 at step 10. These results demonstrate that BlurDM consistently outperforms RDDM in terms of both distortion-based (PSNR) and perceptual (LPIPS) quality metrics.
A.7 Visualizations of Blur Residual
In Fig. 9
we compare blurred images at different exposure times synthesized from the GoPro 24 dataset and the corresponding blur residuals obtained using BlurDM. Here, we depict T as sharp images. As 7 increases (i.e., as the exposure time increases), the images gradually become more blurred, and the corresponding blur residuals evolve accordingly. Eventually, = 1, the images correspond to the fully blurred versions.
26

---

Table 8: Performance under different step counts on the GoPro dataset.
Method
2
5
6
8
10
PSNR1 / LPIPS]
PSNRT LPIPS]
/LPISI,
PSNR /LPIP.)
PSNR LPIPS]
PSNRT/LPIPS.
RDDM
32.07 0.0130
32.08 /0.0131
32.03/0.0125
32.01 0.0122
32.02 0.0112
32.02 / 0.0121
BlurDM
32.21 0.0116
32.27 0.0114
32.28/ /.0114
32.22/ 0.0115
32.24 0.0113
32.24 0.0113
Sharp
RDDM
BlurDM
Blur
et+2
et+1
et
Figure 8: Comparison of blur residuals between RDDM and BlurDM at 1.
A.8 Comparison between BlurDM and RDDM in Blur Residuals.
We visualize and compare blur residuals generated by BlurDM and RDDM 18 in Fig. g RDDM computes blur residuals using simple subtraction operation followed by linear scaling to adjust intensity, resulting residuals that primarily capture direct differences between blurred and sharp images, progressively magnified over time steps (e, to c.42). In contrast, BlurDM addresses the inductive bias of blur formation process to estimate blur residuals, capturing the non-linear progressive accumulation of blur. Unlike RDDM. BlurDM models how blurred residuals spatially diffuse and evolve over time, simulating the blur spread as exposure time extends. As a result, BlurDM more accurately represents the physical characteristics of motion blur within the diffusion process, leading to significantly improved deblurring performance.
A.9 Deblurred Results on Real-world Datasets
We provide additional deblurred results for deblurring models using BlurDM, compared to those without using our method, referred to Baseline. These deblurring models are trained on the GoPro (24] and RealBlur-J [31] training sets, and tested on the RealBlur-J testing set. We demonstrate qualitative comparisons based on four image deblurring models, including MIMO-UNet
Fig
Stripformer [40] in Fig. FFTformer [13 in Fig. and LoFormer in Fig. B
A.10
Broader Impacts
Our work improves the capability of image deblurring by introducing a diffusion-based framework that mimics the physical formation of motion blur. This has potential benefits in applications such as autonomous driving. medical imaging, and restoration of historical media. However, with many image enhancement technologies. there exists a risk of misuse, such as reconstructing intentionally blurred faces or sensitive content, which may raise privacy concerns. We encourage the responsible and ethical use of deblurring models and suggest deploying them in contexts with appropriate privacy safeguards and user consent.
27

---

T=0
T = 0.25
T= 0.50
T 0.75
T
Figure 9: Visualizations of blur residual from GoPro 24 dataset
28

---

Blurred Image
Baseline
BlurDM
Sharp Image
Figure 10: Qualitative results of MIMO-UNet on the RealBlur-J 31 dataset.
29

---

16-
Bluned Image
500
24CH
500
1,50
16-1588
22,000
2,500
Baseline
1,500
&ICH WIC!!!
2,000
511 9114
2,500
1,500
16-1581
w2,000
22,500
BluDDM
w1,500
2IcH WIC!!!
22,000
611 9154
#2,500
1,500
16-1581
W2,000
w2,500
Shapp Image
w1,500
2ICH WCF!! 611-9154
Shapp
Tor
w2,000
W w2,500
w1,500
Figure 11: Qualitative results of Stripformer 40
on the RealBlur-] 31 dataset.
30

---

Bluned Immag
Baseline
R
BLuDDM
Shapp Shapp Image
Figure 12: Qualitative results of FFTformer
13 on the RealBlur-J 31 dataset.
31

---

Bluned Image
Lél
616
048
USE CON
E0
Baseline
SHE
T. L616-00
616 048
USE CON
BM
Te1o
616 048
Sharp Image
USE CONS
28
Figure 13: Qualitative results of LoFormer (1 on the RealBlur-J 31
dataset.
32


Reviews:
[{"review_id": "RjCbVtdwY3", "reviewer": "Reviewer_zo6Q", "summary": "The paper proposes BlurDM, a diffusion-based model for image deblurring that integrates a dual noise and blur diffusion process to align with the physical principles of motion blur formation. The authors introduce a blur residual estimator and a noise estimator to reverse the degradation process, alongside a Prior Fusion Module (PFM) to enhance existing deblurring networks. Experiments on four datasets (GoPro, HIDE, RealBlur-J/R) demonstrate consistent PSNR/SSIM improvements over baseline methods.", "questions": "1. Why is BlurDM’s blur residual estimator superior to RDDM’s additive residuals? Provide theoretical or empirical comparisons.\n2. How does BlurDM scale to ultra-high-resolution images? Report metrics (e.g., memory, speed) for 4K inputs.\n3. Could the framework handle hybrid blur (e.g., motion + defocus)? Address limitations in Section 5 more concretely.\n4. Why omit statistical significance tests? Even simple bootstrapped confidence intervals would strengthen claims.", "limitations": "The authors acknowledge BlurDM’s limitation to motion blur but overlook broader issues:\n1. Ethical risks: Potential misuse for deblurring sensitive/private content (e.g., surveillance footage).\n2. Computational cost: No comparison to lightweight deblurring methods (e.g., CNNs) for edge devices.", "rating": 2, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 1, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. Novelty:The dual diffusion process (noise + blur) is a creative adaptation of diffusion models for deblurring, addressing the structured nature of motion blur.\n2. Technical Rigor:Detailed derivations (Section 3, Appendix A) justify the dual diffusion framework and reverse process.\n3. Practical Integration:BlurDM’s latent-space implementation minimizes computational overhead (Table 4), making it feasible for deployment.\n\nWeaknesses:\n1. Incremental Contribution:The core idea (diffusion for deblurring) builds heavily on prior work (e.g., DDPM, RDDM). The dual diffusion process, while novel, offers modest gains (~0.3–0.8 dB PSNR in Table 1).\n2. Limited Generalizability:Focused solely on motion blur; performance on defocus blur (Section 5) or other degradation types is unexplored.\n3.No analysis of real-world edge deployment (latency, power consumption).", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for recognizing BlurDM’s novelty, technical rigor, and practical integration. We greatly appreciate these encouraging comments. We address each concern in detail below and will revise the paper accordingly.\n\n**Weaknesses 1 and Question 1: Fundamental Differences between DDPM, RDDM, and our BlurDM**\n\nAs explained in Lines 35–45 and 108–117 of the paper, motion blur results from a continuous exposure process during image capture, where blur intensity accumulates progressively along a motion trajectory. This leads to highly structured and directionally consistent patterns, which differ significantly from the random noise perturbations modeled by conventional diffusion-based frameworks like DDPM.\n\nPrevious works [3, 4, 15, 30] that directly apply DDPM to image deblurring neglect this critical physical aspect of blur formation, thereby limiting their capacity to model the real-world blur formation process and ultimately resulting in suboptimal deblurring performance.\n\nAs mentioned in Lines 118–123, RDDM [17] models the blur residual by \"linearly'' computing the difference between the blurred and sharp image via subtraction, which is inconsistent with real-world blur formation, typically caused by a \"convolutional'' process.\n\nIn contrast, we drive a dual denoising and deblurring formulation in Equation (4) that adheres to the principles of the blur formation process, aligning with the natural characteristics of blur. In Table 3, BlurDM outperforms DDPM by $0.37$ dB on GoPro and $0.28$ dB on RealBlur-J, and outperforms RDDM by $0.25$ dB on GoPro and $0.23$ dB on RealBlur-J, clearly demonstrating the effectiveness of our blur-aware design.\n\n**Weaknesses 1: Significant Performance Gains and Field Impact**\n\nTable A presents the performance of annually SoTA image deblurring methods over the past three years, including Stripformer, FFTformer, and LoFormer. Taking the representative and real-world dataset RealBlur-R as an example, annual improvements on this dataset typically remain within 0.3 dB in PSNR. As shown in Table 1 of the paper, our proposed BlurDM significantly improves Stripformer, FFTformer, and LoFormer by 1.16 dB, 0.44 db, and 0.56 dB, respectively, on this dataset, which represents a significant and consistent advancement in image deblurring. Furthermore, the other four reviewers recognize BlurDM’s performance improvement as a key strength.\n\n| **Method**                | **Venue**      | **GoPro** | **HIDE** | **RealBlur-J** | **RealBlur-R** |\n|--------------------------|----------------|-----------|----------|----------------|----------------|\n| Stripformer              | ECCV’ 22       | 33.09     | 31.03    | 32.48          | 39.84          |\n| FFTformer                | CVPR’ 23       | 34.21     | 31.62    | 32.62          | 40.11          |\n| Loformer                 | ACM MM’ 24     | 33.54     | 31.18    | 32.23          | 40.36          |\n| **BlurDM (Stripformer)** | -              | 33.53     | 31.36    | 33.53          | 41.00          |\n| **BlurDM (FFTformer)**   | -              | 34.34     | 31.76    | 32.92          | 40.55          |\n| **BlurDM (Loformer)**    | -              | 33.70     | 31.27    | 33.47          | 40.92          |\n\n**Table A**: PSNR comparison on GoPro, HIDE, RealBlur-J, and RealBlur-R datasets using various backbones.\n\n**Weaknesses 2 and Question 3: Generalizability of the BlurDM for defocus blur**\n\nWe appreciate the comment regarding the generalizability. While we agree that this paper primarily focuses on motion deblurring, we wish to emphasize that motion blur stands as a persistently challenging and unresolved research topic in the literature of image restoration. It is evidenced that recent impactful works such as MIMO-UNet, Stripformer, FFTformer, and LoFormer focus exclusively on motion deblurring, highlighting the importance of addressing motion blur in this field. Additionally, although BlurDM is specifically designed to address motion blur, it can be flexibly integrated into general-purpose deblurring backbones capable of handling both motion and defocus blur. This allows BlurDM to provide motion-aware guidance that effectively enhances motion deblurring performance.\n\nAs a future direction, we plan to extend BlurDM by incorporating the physical characteristics of defocus blur into the diffusion process, similar to what we did in Equation (1) for motion blur. It enables a unified prior that addresses more complex and mixed blur types.\n\n**Weaknesses 3 and Limitation 2: Analysis of real-world edge deployment**\n\nOur primary focus is to develop a novel and effective deblurring algorithm, like prior works, such as MIMO-UNet, Stripformer, FFTformer, and LoFormer. Our method achieves SoTA per- formance (reported in Table 1 of the paper) with reasonable computational costs in model size, FLOPs, and runtime (Table 4 of the paper). Despite its importance and practical implications, real-world deployment on edge devices is beyond the scope of this work.\n\n**Question 2: Memory usage and inference time of BlurDM on 4K images**\n\nBlurDM works in the latent space with the diffusion prior injected into the deblurring backbone in a channel-wise manner. This allows its efficient application to an arbitrary resolution input. In Table B, we evaluate the memory usage and inference time of BlurDM on 4K images using an NVIDIA RTX 3090 GPU, with MIMO-UNet as the backbone. BlurDM introduces minor overheads, increasing memory usage by only 7.4% and runtime by just 2.3%.\n\n| **Method**               | **Memory (MB)** | **Speed (sec)** |\n|--------------------------|------------------|------------------|\n| MIMO-UNet                | 20357.81         | 2.559            |\n| BlurDM                   | 1502.65          | 0.058            |\n| MIMO-UNet + BlurDM       | 21860.46         | 2.617            |\n\n**Table B**: Memory usage and inference time on 4K images using an NVIDIA RTX 3090 GPU.\n\n**Question 4: Bootstrapped confidence intervals**\n\nFollowing recent deblurring methods such as MIMO-UNet, Stripformer, FFTformer, and LoFormer, we report average PSNR and SSIM scores for evaluation in the paper. As suggested, we conducted a bootstrapped confidence interval analysis using 1000-sample resampling from the test sets. Table C reports the 95% confidence intervals for PSNR values over four benchmark datasets (GoPro, HIDE, RealBlur-J, and RealBlur-R) using four different backbones (MIMO-UNet, Stripformer, FFTformer, and LoFormer). For each combination, we present the [min, max] bounds computed from the bootstrapped distribution of PSNR scores. BlurDM consistently yields better performance compared to the baseline, validating the robustness and significance of the improvements.\n\n| **Backbone**   |          | **GoPro**           | **HIDE**            | **RealBlur-J**       | **RealBlur-R**       |\n|----------------|----------|---------------------|----------------------|-----------------------|-----------------------|\n| MIMO-UNet      | Baseline | [32.28, 32.59]       | [29.84, 30.09]       | [31.40, 31.77]        | [38.67, 39.41]        |\n|                | BlurDM   | [32.78, 33.07]       | [30.61, 30.87]       | [31.96, 32.31]        | [39.28, 39.99]        |\n| Stripformer    | Baseline | [32.93, 33.22]       | [30.89, 31.16]       | [32.29, 32.64]        | [39.48, 40.20]        |\n|                | BlurDM   | [33.38, 33.68]       | [31.24, 31.51]       | [33.36, 33.70]        | [40.63, 41.35]        |\n| FFTformer      | Baseline | [34.06, 34.36]       | [31.48, 31.76]       | [32.46, 32.79]        | [39.72, 40.46]        |\n|                | BlurDM   | [34.19, 34.48]       | [31.59, 31.86]       | [32.75, 33.08]        | [40.20, 40.90]        |\n| LoFormer       | Baseline | [33.32, 33.60]       | [31.05, 31.31]       | [32.05, 32.38]        | [39.99, 40.76]        |\n|                | BlurDM   | [33.57, 33.84]       | [31.14, 31.41]       | [33.31, 33.66]        | [40.59, 41.30]        |\n\n**Table C**: 95% confidence intervals of PSNR values across different backbones and datasets.\n\n**Limitation 1: Ethical risks**\n\nWhile deblurring techniques could potentially be misused on sensitive content (e.g., surveillance footage), our method is designed for beneficial applications such as autonomous driving, medical imaging enhancement, and video communication. We support responsible use and plan to explore safeguards to prevent potential misuse."}, {"review_id": "XmbqPjbMFO", "reviewer": "Reviewer_a5f6", "summary": "This paper presents BlurDM, diffusion-based framework for image deblurring that models the forward degradation as a combination of noise and motion blur accumulation, reflecting the `physical' process during exposure. BlurDM employs a dual diffusion process to estimate a sharp prior in the latent space, which then guides a separate restoration network for final deblurring. The framework is trained in three stages: 1) pretraining a Sharp Encoder and Prior Fusion Module, 2) training BlurDM to generate sharp priors from blurred inputs, and 3) jointly optimizing all components. BlurDM is compatible with existing deblurring architectures, and consistently improves performance across various benchmarks with minimal computational overhead.", "questions": "1. It would be valuable for the authors to conduct ablation studies by omitting individual stages to quantify each stage’s contribution and evaluate the potential risk of error propagation throughout the pipeline.\n\n2. Real-world scenarios often involve diverse and complex blur patterns, rather than averaging interpolated blurs. It would strengthen the paper to evaluate BlurDM under varying conditions—such as different blur intensities and in the presence of noise or JPEG compression—to better demonstrate its practical robustness.\n\n3. Regarding the evaluation using Z^s: Since Z^s can be an upper bound on achievable deblurring performance, it would be insightful to provide further analysis or comparison to clarify its role and justify its use within the framework.", "limitations": "No.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1) BlurDM is grounded in the physical modeling of motion blur, offering intuitive insights into the deblurring process.\n\n2) Experimental results clearly demonstrate the effectiveness of the proposed dual diffusion strategy.\n\nWeaknesses:\n\n1) The validity of the Equations (1) to (3) within the latent space is questionable. While noise is explicitly added, the treatment of blur as simply added lacks theoretical justification or explanation. \n\n2) Further evidence or analysis is needed to confirm that motion blur in the latent space indeed converges to a Gaussian distribution, as assumed in the proposed model.", "was_revised": true, "final_justification": "The rebuttal has addressed many of my concerns; however, the reviewer still finds the main validation somewhat uncertain.\nNonetheless, the additional ablation studies have effectively resolved the primary concern. the reviewer will therefore raise the score.\nPlease include all ablation studies and the related discussion in the final version.", "rebuttal": "We thank the reviewer for recognizing the strength of our core idea and the clear effectiveness of the proposed dual diffusion strategy in experiments. We greatly appreciate these encouraging comments. We address each concern in detail below and will revise the paper accordingly.\n\n**Weaknesses 1: Theoretical justification of latent-space BlurDM**\n\nPrevious works, such as Stable Diffusion [a], HI-Diff [15], and Swin-diff [16], have demonstrated the effectiveness and efficiency of shifting the diffusion process from the image space to the latent space by leveraging latent space encoders such as VAEs and CNNs. Inspired by these methods, we employ sharp and blur encoders to learn latent representations for performing the diffusion process in the latent space through a three-stage training strategy. Specifically, in the forward diffusion process, instead of adding blur step by step to images, we derive a one-step blur addition process, as described in Appendix A.1. This process demonstrates that the final blurred and noisy image can be obtained by directly adding noise to the blurred image provided in the original dataset. In the reverse generation process, we utilize Equation (12) to iteratively remove blur features, following the principles of the blur formation process in the forward diffusion stage. Moreover, to demonstrate that our latent-space diffusion process performs deblurring in an iterative manner, we present deblurred results at different time steps in Figure 5, confirming that blur residuals are effectively addressed in the latent domain.\n\nReference:\n\n[a] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer. High-Resolution\nImage Synthesis with Latent Diffusion Models. In CVPR, 2022.\n\n**Weaknesses 2: Question about the convergency of motion blur to the Gaussian distribution**\n\nAs described in Equation (6), we model motion blur as a mean shift of the standard Gaussian diffusion process, where the mean is shifted by $\\frac{1}{\\alpha_T} \\sum_{t=1}^{T}e_t$ that captures the degradation characteristics introduced by motion blur. This mean-shift interpretation is conceptually aligned with the idea presented in Figure 1 of the RDDM paper [17].  However, as mentioned in Lines 118–123, RDDM models the blur residual by \"linearly'' computing the difference between the blurred and sharp image via subtraction.  In contrast, we drive a dual denoising and deblurring formulation that follows the principles of the blur formation process to align with the natural characteristics of blur, typically caused by a \"convolutional'' process. \n\n**Question 1 and Question 3: Effectiveness of the three-stage training strategy**\n\nIn Table D, we present ablation studies to evaluate the effectiveness of the three-stage training strategy. We use MIMO-UNet as the backbone, and all models are trained for 1000 epochs to ensure a fair comparison. “Net1” denotes the baseline deblurring performance without incorporating BlurDM. In “Net2”, we directly use the sharp prior $Z^S$ in BlurDM, serving as an upper bound for the achievable deblurring performance. In “Net3”, we jointly optimize BlurDM and the deblurring model without pre-training through Stage 1 and Stage 2, serving as a baseline for a purely data-driven approach. In “Net4” and “Net5”, after completing pre-training in Stage 1, we apply either Stage 2 or Stage 3 alone to optimize BlurDM and the deblurring model. In “Net6”, we adopt the complete three-stage training strategy, which achieves the best performance compared to the alternative combinations used in “Net3” to “Net5”.\n\n| **Model** | **Stage 1** | **Stage 2** | **Stage 3** | **PSNR**             |\n|-----------|-------------|-------------|-------------|----------------------|\n| Net1      |             |             |             | 31.78 (Baseline)     |\n| Net2      | ✓           |             |             | 32.69 (Upper bound)  |\n| Net3      |             |             | ✓           | 31.80                |\n| Net4      | ✓           |             | ✓           | 32.01                |\n| Net5      | ✓           | ✓           |             | 31.95                |\n| Net6      | ✓           | ✓           | ✓           | **32.28**            |\n\n**Table D**: Ablation study of each training stage in BlurDM.\n\n**Question 2: Evaluation of BlurDM on real-world blur scenarios under varying conditions**\n\nIn Table 1 of our main paper, we evaluate BlurDM on the challenging real-world dataset RealBlur, which contains naturally captured blur under low-light conditions, often accompanied by severe blur intensity and sensor noise. BlurDM consistently improves the performance of existing methods on the RealBlur dataset, achieving gains of up to 1.24 dB on RealBlur-J and 1.16 dB on RealBlur-R in PSNR, thereby verifying the robustness of our approach under diverse degradation conditions."}, {"review_id": "PpC7MwSTRg", "reviewer": "Reviewer_EKwB", "summary": "This paper proposes BlurDM, a diffusion model for dynamic scene deblurring that integrates the physical process of motion blur formation (specifically, its temporal integration over exposure time) directly into the diffusion steps. It derives a dual denoising and deblurring formulation, implicitly estimating the blur residual while progressively restoring a clear image. A latent variant, Latent BlurDM, is also developed for efficient integration. Experiments across multiple datasets demonstrate competitive deblurring performance, particularly in recovering high-frequency details.", "questions": "My major confusion is about the training of the blur residual estimator. Specifically, as can be seen in Eq. (34), the blur residual estimator is supervised by $e_t$, which is theoretically the blur residual from $\\alpha_{t-1}$ to $\\alpha_t$. However, it is not clear how to obtain it in practice, at least to me. Please make it more clearly clarified, if I miss something.", "limitations": "The authors have discussed the limitations of their method.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n1. The core idea of embedding continuous blur dynamics into the diffusion process is theoretically well-motivated and novel. It captures the essence of deblurring by explicitly linking blur formation and diffusion, offering a more principled approach than directly applying existing diffusion models.\n\n2. Experimental results are promising, especially in restoring high-frequency details.\n\n### Weaknesses\n\nThere is no major issue for me.", "was_revised": true, "final_justification": "The authors have addressed my concern. So I keep my initial rating.", "rebuttal": "We thank the reviewer for recognizing our idea as theoretically well-motivated and novel, and for acknowledging the promising experimental results. We greatly appreciate these encouraging comments and address the concern in detail below.\n\n**Questions 1: Supervision of blur residual $e_t$ during training**\n\nAs detailed in Lines 512–525, we do not explicitly supervise the individual blur residuals $e_t$ during training. Instead, BlurDM is optimized using gradients derived from the reconstruction loss between the deblurred result $I^{\\theta}_0$ and the ground-truth sharp image $I^0$ through Equation (36).  Since BlurDM is a physics-guided module that estimates blur residuals based on the principles of blur formation, as formulated in Equation (12), it can effectively estimate these residuals without requiring ground-truth supervision for intermediate blur residuals. Despite the absence of intermediate ground-truth supervision, the deblurred results at different time steps shown in Figure 5 demonstrate that BlurDM effectively captures blur residuals throughout the reverse process."}, {"review_id": "HvWhnuy1Dl", "reviewer": "Reviewer_AFXA", "summary": "This work offers a unique image deblurring approach named the Blur Diffusion Model (BlurDM), which incorporates blur process into the diffusion model. Its primary advances are: 1. a dual-diffusion forward scheme; 2. a dual denoising and deblurring formulation; and 3. the incorporation of BlurDM into the latent space of current deblurring architectures. Experiments demonstrate that BlurDM can increase the performance of deblurring algorithms on a variety of benchmark datasets.", "questions": "1. Why aren't there direct comparisons to existing diffusion-based deblurring methods?  Such comparisons would assist to isolate the benefits of the proposed blur-aware diffusion formulation.\n\n2. Can the authors explain the increased complexity brought on by BlurDM in terms of training time and convergence behavior?  The three-stage pipeline is successful but may be difficult to replicate; extra training information would be beneficial.", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. Theoretical derivations are robust, with a thorough definition of forward and reverse processes, and deterministic DDIM-style sampling assures efficiency and efficacy.\n2. BlurDM is modular and flexible, acting as a prior module that can be integrated into existing deblurring networks.\n3. Extensive testing with four datasets and four approaches show consistent performance benefits at low computational expense.\n\nWeaknesses:\n\n1. The technique's application in more complicated real-world circumstances is limited because it is mainly designed for motion blur and cannot handle defocus blur.\n2. Since there is no direct comparison with current diffusion-based deblurring models, it is challenging to determine the actual value of the suggested blur-aware formulation.\n3. The entire framework's complexity—which includes many modules and multi-stage training—increases implementation costs and diminishes clarity.", "was_revised": true, "final_justification": "The authors have provided detailed responses that address my concerns. I am satisfied with their clarifications and will maintain my original score.", "rebuttal": "We thank the reviewer for recognizing the robustness of our theoretical derivations, the modularity and flexibility of BlurDM, and the consistent performance gains demonstrated across extensive experiments. We greatly appreciate these encouraging comments. We address each concern in detail below and will revise the paper accordingly\n\n**Weakness 1: Generalizability of the BlurDM for defocus blur**\n\nWe appreciate the comment regarding the generalizability. While we agree that this paper primarily focuses on motion deblurring, we wish to emphasize that motion blur stands as a persistently challenging and unresolved research topic in the literature of image restoration. It is evidenced that recent impactful works such as MIMO-UNet, Stripformer, FFTformer, and LoFormer focus exclusively on motion deblurring, highlighting the importance of addressing motion blur in this field. Additionally, although BlurDM is specifically designed to address motion blur, it can be flexibly integrated into general-purpose deblurring backbones capable of handling both motion and defocus blur. This allows BlurDM to provide motion-aware guidance that effectively enhances motion deblurring performance.\n\nAs a future direction, we plan to extend BlurDM by incorporating the physical characteristics of defocus blur into the diffusion process, similar to what we did in Equation (1) for motion blur. It enables a unified prior that addresses more complex and mixed blur types.\n\n**Weakness 2 and Question 1: Direct comparison with current diffusion-based deblurring models**\n\nAs mentioned in Lines 62–64, prior studies have shown that applying diffusion models directly in the image space for deblurring often struggles to recover fine details due to the inherent stochasticity of the generative process. Moreover, image-space diffusion is computationally expensive and memory-intensive, particularly for high-resolution datasets like GoPro and RealBlur. Therefore, we focus on latent-space diffusion and conduct a fair comparison by evaluating different diffusion algorithms, including DDPM and RDDM, within the latent space using the same deblurring backbone, as shown in Table 3. \n\nAdditionally, we directly compare BlurDM with two recent diffusion-based deblurring methods, including HI-Diff [4] and RDDM [17], on the GoPro dataset, as shown in Table E. HI-Diff applies DDPM to the deblurring network in the latent space, whereas RDDM performs the residual diffusion process in the image space. BlurDM consistently achieves better performances in PSNR and SSIM under comparable parameter counts and FLOPs. Moreover, as a plug-and-play module, BlurDM can be flexibly integrated into various backbone architectures. This demonstrates not only its superior performance but also its strong generalizability across different network designs.\n\n| **Method**              | **Venue**     | **PSNR** | **SSIM** | **Params (M)** | **FLOPs (G)** |\n|-------------------------|---------------|----------|----------|----------------|----------------|\n| HI-Diff                 | NeurIPS’ 23    | 33.33    | 0.955    | 23.99          | 125.47         |\n| RDDM                    | CVPR’ 24       | 32.40    | 0.963    | 15.49          | 134.20         |\n| BlurDM (Stripformer)    | -             | 33.53    | 0.966    | 24.33          | 174.18         |\n| BlurDM (FFTformer)      | -             | 34.34    | 0.970    | 18.66          | 135.69         |\n| BlurDM (LoFormer)       | -             | 33.70    | 0.967    | 19.08          | 56.35          |\n\n**Table E**: Comparison of different deblurring methods in terms of PSNR, SSIM, number of parameters, and FLOPs.\n\n\n**Weakness 3 and Question 2: Training cost of the three-stage training strategy**\n\nIn Table F, we report the training complexity introduced by BlurDM in terms of training time and convergence behavior, using MIMO-UNet as the backbone. The default number of training epochs for MIMO-UNet is 3, 000, denoted as “Baseline 1”. Increasing the training epochs to 6, 000, denoted as “Baseline 2”, results in only a marginal improvement of 0.07 dB in PSNR. For BlurDM, we experiment with different training configurations, including using 1, 500 or 3, 000 epochs for Stage 1 and Stage 3, with 500 epochs for Stage 2. Both “BlurDM 1” and “BlurDM 2” outperform Baseline 2 while requiring less training time, demonstrating the effectiveness of the proposed three-stage training strategy. Finally, we adopt “BlurDM 3” as our final model, which increases training time by only 8% while achieving a 0.42 dB improvement in PSNR compared to “Baseline 2”. \n\nAs for convergence behavior, according to our observation of the learning curves, the optimization process does not suffer from any convergence issue. Furthermore, we will release all our code upon the paper’s acceptance to ensure reproducibility.\n\n| **Method**   | **Training epoch**                                              | **Training time** | **PSNR** |\n|--------------|------------------------------------------------------------------|-------------------|----------|\n| Baseline 1   | 3000                                                             | 66.7 h            | 32.44    |\n| Baseline 2   | 6000                                                             | 133.4 h           | 32.51    |\n| BlurDM 1     | 1500 (Stage 1) + 500 (Stage 2) + 1500 (Stage 3)                  | 70.7 h            | 32.62    |\n| BlurDM 2     | 3000 (Stage 1) + 500 (Stage 2) + 1500 (Stage 3)                  | 104.1 h           | 32.71    |\n| BlurDM 3     | 3000 (Stage 1) + 500 (Stage 2) + 3000 (Stage 3)                  | 141.4 h           | 32.93    |\n\n**Table F**: Comparison of training cost and performance between baseline and BlurDM."}, {"review_id": "4S8fcCHjsb", "reviewer": "Reviewer_BwW8", "summary": "This work integrates the blur formation process into the diffusion framework by introducing a dual-diffusion scheme with forward and reverse stages. In the forward pass, both noise and blur are applied to a sharp image, while the reverse pass simultaneously performs denoising and deblurring to recover the original image. The proposed method, BlurDM, operates in the latent space to provide a flexible prior for image deblurring. Experimental results show that BlurDM consistently improves the performance of existing deblurring methods.", "questions": "Please address comments under weaknesses, and also the below pointers\n\n1. While the introduction and formulation sections suggest that the method aims to incorporate natural blur formation into the diffusion process, the role of noise in naturally blurred images is neither discussed nor modeled. The presented dual noise and blur insertion is contradictory to real-world blur formation, where increased blur typically leads to reduced noise. It would strengthen the paper to acknowledge this distinction and clarify how diffusion-based modeling aligns with or diverges from natural blur formation processes.\n\n2. In Table 1, the combination of MiMo-UNet and BlurDM achieves a PSNR of 32.93 on the GoPro dataset. However, in Tables 2 and 3, the same MiMo-UNet baseline with BlurDM reports a PSNR of only 32.28. The source of this discrepancy is not explained and warrants clarification.", "limitations": "As I mentioned under the questions, it'd good to clarify how diffusion-based modeling aligns with or diverges from natural blur formation processes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n1. Proposed method is able to significantly enhance the performance in terms of PSNR and SSIM compared to four different baseline architecture.\n2. Proposed method presents the novel idea of inserting the blur formation into diffusion and showcase the advantages of doing so via experimental results and extensive evaluations.\n\nWeaknesses\n\n1. Although the method is built upon four existing architectures, its standing among prior works remains unclear. For instance, [17] reports that Uformer [42] outperforms their method, yet Uformer is neither directly compared nor included in enhancement or ablation studies. This omission is particularly notable given that [17] achieves performance levels close to the proposed method, as shown in Table 3. It would be informative to see how [42] compares in the same context.\n\n2. Referring to Table 9 in [17], it is evident that PSNR tends to decrease as the number of diffusion steps increases. Therefore, for a fair PSNR-based comparison, consistent or reduced step counts should be maintained. Unfortunately, the number of steps used in the presented comparisons is not specified, making the reported improvements harder to interpret. Additionally, including LPIPS comparisons would help assess perceptual quality and reveal any trade-offs between perceptual fidelity and PSNR gains. Without this, the risk of over-optimizing solely for PSNR cannot be ruled out.", "was_revised": true, "final_justification": "Rebuttal has addressed all of my concerns, hence I am would like to retain my original rating.", "rebuttal": "We thank the reviewer for recognizing the novelty of our approach and its significant performance gains shown in experiments. We greatly appreciate these encouraging comments. We address each concern in detail below and will revise the paper accordingly.\n\n**Weakness 1: About the standing among prior works and the performance of BlurDM to Uformer**\n\nRegarding the concern about the standing among prior works, we would like to clarify that our proposed method, BlurDM, is a plug-and-play framework with strong generalization ability, designed to integrate into arbitrary image deblurring backbones. To demonstrate this, we conducted comparisons in Table 1 against four widely recognized deblurring methods, including FFTformer, which was the state-of-the-art at the time of paper submission. These comparisons clearly validate that our framework can effectively enhance strong baseline architectures. \n\nAdditionally, as you suggested, we conduct experiments using Uformer as the deblurring backbone. As shown in Table G, our method consistently improves Uformer’s performance on the GoPro and HIDE datasets, verifying the effectiveness and robustness of our proposed approach across different architectures.\n\n| **Backbone**         | **GoPro** | **HIDE** |\n|----------------------|-----------|----------|\n| Uformer              | 32.97     | 30.83    |\n| Uformer + BlurDM     | 33.17     | 30.97    |\n\n**Table G**: Performance comparison between Uformer and Uformer + BlurDM on GoPro and HIDE datasets.\n\n**Weakness 2: Performance comparison of the number of steps used in RDDM and our method**\n\nTo ensure a fair evaluation in Table 3, we set the diffusion steps to five for all models. For additional insights, Table H presents a comparison of RDDM and BlurDM under varying numbers of diffusion steps, where PSNR and LPIPS are used to assess performance. RDDM achieves its peak PSNR of 32.08 dB at step 4 and the best LPIPS score of 0.0121 at step 10. In comparison, BlurDM achieves a higher peak PSNR of 32.28 dB at step 5 and a lower LPIPS score of 0.0113 at step 10. These results demonstrate that BlurDM consistently outperforms RDDM in terms of both distortion-based (PSNR) and perceptual (LPIPS) quality metrics.\n\n| **Method** | **2**       | **4**       | **5**       | **6**       | **8**       | **10**      |\n|------------|-------------|-------------|-------------|-------------|-------------|-------------|\n|            | PSNR↑ / LPIPS↓ | PSNR↑ / LPIPS↓ | PSNR↑ / LPIPS↓ | PSNR↑ / LPIPS↓ | PSNR↑ / LPIPS↓ | PSNR↑ / LPIPS↓ |\n| RDDM       | 32.07 / 0.0130 | 32.08 / 0.0131 | 32.03 / 0.0125 | 32.01 / 0.0122 | 32.02 / 0.0122 | 32.02 / 0.0121 |\n| BlurDM     | 32.21 / 0.0116 | 32.27 / 0.0114 | **32.28** / 0.0114 | 32.22 / 0.0115 | 32.24 / 0.0113 | 32.24 / **0.0113** |\n\n**Table H**: PSNR↑ / LPIPS↓ under different step counts on the GoPro dataset. BlurDM shows superior performance in both fidelity and perceptual quality.\n\n\n**Question 1: The role of noise in blurred images**\n\nWe would like to clarify that the “noise” in our dual diffusion framework does not refer to sensor noise in captured images. Instead, motivated by recent studies (Lines 108–123) highlighting the strong generative capabilities of diffusion models in reconstructing missing high-frequency details, we leverage the noise diffusion process to stochastically synthesize these lost high-frequency details in heavily blurred regions. This is conceptually different from modeling physical sensor noise. The discussion above will be incorporated into the paper.\n\n**Question 2: The performance of MiMo-UNet baseline reported in Tables 2 and 3**\n\nTo ensure a fair comparison, we adopt different training schedules for different experimental purposes. Specifically, in Table 1, MIMO-UNet + BlurDM is trained with 3000 epochs for Stage 1, 500 for Stage 2, and 3000 for Stage 3. In contrast, Tables 2 and 3 use a standardized schedule of 1000/500/1000 epochs for the three stages to ensure consistency across methods in the ablation and component analysis. We will clarify it in the paper."}]

Author rebuttal:

Meta-review: This paper presents BlurDM, which integrates the physics of motion blur into both the forward and reverse processes of diffusion models through a modular design compatible with various deblurring backbones. The method demonstrates consistent improvements across four benchmark datasets and multiple architectures. While there are initial concerns regarding the empirical validation of its core contribution, the authors provide targeted experiments, confidence interval analyses, and perceptual metrics during the rebuttal. Moreover, they include mathematical derivations to support the theoretical formulation of the proposed blur modeling process. These additions address most reviewer concerns. Overall, BlurDM is theoretically grounded and empirically validated, with clear potential for both academic and real-world impact. Thus, the AC recommends accepting this paper.
Decision: Accept (poster)