FideDiff: Efficient Diffusion Model for High-Fidelity Image Motion Deblurring
Authors: Xiaoyang Liu
Zhengyan Zhou
Zihang Xu
Jiezhang Cao
Zheng Chen
Yulun Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Image Motion-Deblurring
Diffusion Model
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Recent advancements in image motion deblurring, driven by CNNs and transformers, have made significant progress. Large-scale pre-trained diffusion models, which are rich in real-world modeling, have shown great promise for high-quality image restoration tasks such as deblurring, demonstrating stronger generative capabilities than CNN and transformer-based methods. However, challenges such as unbearable inference time and compromised fidelity still limit the full potential of the diffusion models. To address this, we introduce FideDiff, a novel single-step diffusion model designed for high-fidelity deblurring. We reformulate motion deblurring as a diffusion-like process where each timestep represents a progressively blurred image, and we train a consistency model that aligns all timesteps to the same clean image. By reconstructing training data with matched blur trajectories, the model learns temporal consistency, enabling accurate one-step deblurring. We further enhance model performance by integrating Kernel ControlNet for blur kernel estimation and introducing adaptive timestep prediction. Our model achieves superior performance on full-reference metrics, surpassing previous diffusion-based methods and matching the performance of other state-of-the-art models. FideDiff offers a new direction for applying pre-trained diffusion models to high-fidelity image restoration tasks, establishing a robust baseline for further advancing diffusion models in real-world industrial applications. Our dataset and code will be available at https://github.com/xyLiu339/FideDiff.

Published as a conference paper at ICLR 2026
FIDEDIFF: EFFICIENT DIFFUSION MODEL FOR HIGH- FIDELITY IMAGE MOTION DEBLURRING
Xiaoyang Liu'; Zhengyan Zhou', Zihang Xu', Jiezhang Cao², Zheng Chen', Yulun Zhang Shanghai Jiao Tong University, HHarvard University
ABSTRACT
Recent udvancements in image motion deblurring, driven by CNNs and transform- ers, have made significant progress. Large-scale pre-trained diffusion models, which are rich in real-world modeling, have shown great promise for high-quality image restoration tasks such deblurring, demonstrating stronger generative pabilities than CNN and transformer-basee methods. However, challenges such as unbearable inference time and compromised fidelity still limit the full potential of the diffusion models. To address this, we introduce FideDiff, novel single-step diffusion model designed for high-fidelity deblurring. We reformulate motion de- blurring a diffusion-like process where each timestep represents progressively blurred image, and we train consistency model that aligns all timesteps the same clean image. By reconstructing training data with matched blur trajectories, the model learns temporal consistency, enabling accurate one-step deblurring. We further enhance model performance by integrating Kernel ControlNet for blur ker- nel estimation and introducing adaptive timestep prediction. Our model achieves full-reference metries, surpassing previous diffusion-
superior performance based methods and matching the performance of other state-of-the-art models. FideDiff offers new direction for applying pre-trained diffusion models to high- fidelity image restoration tasks, establishing a robust baseline for further advanc- ing diffusion models in real-world industrial applications. Our dataset and code will be available at lttps: / //github. com/ xy F33////eeFFFFF
Blurry
Restormer Zame etall JIZz
HI-Diff Cleneral pes
UFFNer Fang al Mess
AdaRevD pie al 1006
RealBlur-J (sceme204-12)
DiffAIR Lanrral poar
Diff-Plusin Lanerll Mis
OSEDif (auetal JINT
FideDiff (ours)
HQ
Figure 1: Previous models often suffer from the
generalization.
INTRODUCTION
Image motion deblurring has long been considered an ill-posed image restoration problem due to the complex formation causes, such camera shake and high-speed object motion during exposure time. In recent years, with the development of CNNs and transformers, significant progress has been made the field of motion deblurring, including many end-to-end deblurring methods (Mao al. 2024 [Ceng et al. 2025) and motion blur kernel estimation-base approaches (Kim et al.) 2024). However, these specialized models often lack a true understanding of the world, which limits their ability to handle unknown situations and generalize to real-world scenarios, as shown in Fig. [
In recent years, large-scale pre-trained diffusion models (DMs) have been gradually employed for image restoration tasks, showing remarkable generalization and high-quality restoration ability, bringing new hope the deblurring field (Lin et al. 2024 Liu et al. 2024b). However, similar to the development of diffusion in many low-level vision tasks, several challenges hinder the full utilization of large-scale DMs, including unbearable inference time, usually with tens hun- dreds of sampling steps (Lin et al.] (024), and b) struggling with fidelity (Liu et al. 2024b) or even overlooking emphasize the perceptual quality (Cheng et al. 2025).
"quual contribution
Corresponning author: Yulun Zhang, yulun 100 gmail.com

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Published as a conference paper at ICLR 2026
Additionally, there appears to be trade-off (Fig. between the inference steps (time) and fidelity for DMs. Take well- developed super-resolution approaches struggle to maintain fidelity and sacrifice to pursue the overall quality. This shift undermines the core objective of im- age restoration, which is to restore an image to its original form, and deviates from practical industry-oriented tasks.
Sra
265
Sec5R-550
example, many few- one-step
26.0
ResSaith-15
25.5
Multtisstp
25.0
FluSRa1 OSEDitS-1
24.5
Single-utp
MUSIO
TSD-SR-11
To address these challenges and better harness the potential of pre-trained DMs for efficient and high-fidelity image motion de- blurring, we introduce our model, the high- high-Fidelity single-step deblurring Diffusion model, FideDiff, which reduces the sam-
56
50
64
68
Figure 2:
Sidelity-perceptin tradeoff w.r.t. steps in SR task.
pling steps to a single iteration while prioritizing restoration fidelity. Unlike previous single-step diffusion approaches (Wu al. 2024, Dong et al. 2025 Li al. (025a) that assign a fixed timestep to all low-quality images, we reformulate motion deblurring
diffusion-like process where each timestep corresponds to a specific blur severity. We analyze the challenges of traditional modeling and define the forward process through blur trajectories, then directly optimize consistency ob- jective that forces all timesteps to predict the same clean image. To enable this, we reconstruct the GoPro (Nah al. 2017) dataset to provide matched blur trajectories, allowing the model to learn cross-time consistency and naturally support accurate one-step deblurring during inference.
We carefully build a foundation model with pretrained diffusion priors for high-fidelity deblurring. Considering that effective utilization of the blur kernel and incorporating control information into diffusion-based deblurring methods are important but less explored, we propose Kernel ControlNet, which introduces blur kemel estimation and cleverly integrates control information in the form of filters into the trained foundation model, further improving deblurring performance. We also design a regression module for timestep prediction, enabling the model to adaptively select the appropriate timestep based on blur degree during inference, allowing it to handle various scenarios more flexibly.
Our contributions are as follows: a) We reformulate the diffusion process in deblurring and propose a time- consisteetn training paradigm to support one-step sampling. b) We develop a robust single- step high-fidelity foundation model, and c) introduce Kernel ControlNet to enhance performance, along with a t-prediction module for dynamic blur level prediction. Our model outperforms all pre- vious praitrd diffusion based models full-reference metrics, and it matches even surpasses models on perceptual similarity (LPIPS/DISTS), Our work
the well-developed transformer-baste provides a new perspective for applying pre-trained DMs to image restoration tasks and establishes a robust baseline, which can further refine and expand the application of DMs in low-level vision, facilitating their better application in real-world industrial scenarios.
2
RELATED WORKS
2.1 IMAGE MOTION DEBLURRING METHODS
Image motion deblurring has been widely studied, from traditional handcrafted models deep learn- ing approaches, with early methods framing it as an inverse problem, using priors like total varia- tion (Chan & Wong 1998). hyper-Laplacian (Krishnan & Fergus 2009), or gradient statistics (Shan iet al., 2008 Xu et al., 2013) to regularize deconvolution. Recently, CNN and transformer based methods have dominated, including many end-to-end methods 7Zamir al. 2022 Chen al. 2022
Kong et al. 2023). , facilitating downstream visual understanding such as Hu et al. (2023)
y Liu et al.
(2025b). Park et al. (2020) progressively removes blur with a multi- temporl recurrent neural net- work. Mao et al. (2024) uses adaptive patch exiting reversible decoder, while Liu et al. (2024a)
introduces a motion-adaptive separable collaborative filter to handle complex motion blur.
2.2 KERNEL ESTIMATION
Kernel estimation plays an important role in both blind and non- bon-blind deblurring. Previous methods
assume either uniform blur (Sanghvi al. 2024: Zhang al.; (0024), which limits real-worle ap-
plicability, or predict pixel-wise kernels (Sun et al. 2015 Carbajal et al. 2021 Gong et al. 2017)
for subsequent non-blind deblurring (Dong et al. 2020 Tai al. 201 I Tang et al. 2023) Fang
et al. (2023) represents motion blur kernels in a latent space with normalizing flows and uses uncer- tainty learning to improve end-to-end model performance. Kim al. (2024) presents an efficient deblurring model that decomposes the task into blur pixel classification and
conversion, achieving high performance with low computation cost.
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Published as a conference paper at ICLR 2026
2.3 DIFFUSION MODELS
Diffusion Models (DMs) (Rombach et al. 2022 Esser et al., 2024 Labs 2024 have recently made significant progress in the generation field, and become powerful tools many low-level vision tasks such as real-world super-resolution (Wu et al., 2024; Li et al. 2025a and image restoration tasks, such as deblurring (Cheng et al. 2025 Liu et al. 2025a) and many other tasks (Wang al.)
2025 Guo et al.) 2025 Gong et al. 2025). In the deblurring field, Xia et al. (2023) and [Cenn et al. )023) use additional diffusion architecture to construct conditional priors to strengthen the network. Meanwhile, [hhng et al. (2022) and [en et al. (2023) apply diffusion in the image space to denoise blurry images. The deblurring methods based on pretrained DMs, UID-Diff (Cheng et al.) 2023) and Diff-Plugin (Liu et al. 2024b), leverage unsupervised learning with unpaired data and the blur information plugin approach, respectively, to tame pretrained DMs to learn the deblurring.
However, diffusion-based methods often have high inference cost and require strong priors. And the trade-off between fidelity and perceptual realism of pretrained DMs is non-trivial. Some diffusion outputs may look perceptually plausible but deviate from ground truth.
TASK FORMULATION AND DATA PREPARATION
3.1 MOTIVATION
Despite the advances in accelerating diffusion processes, the application of one-step diffusion to image restoration has not been sufficiently studied. Distillation-bul based paradigms dominate existing efforts, for example. SinSR (Wang al. 2024) and FluxSR (Li al. (2025a) compress multi-step teachers into single-step students, while OSEDiff (Wu ct al. 2024) and TSD-SR (Dong et al.) 2025)
adopt score or distribution distillation to regress toward sharp images. While effective at reducing inference cost, such approaches suffer from two fundamental limitations.
Loss of Diffusion's 's Inductive Bias: Collapsing the iterative denoising into single mapping with fixed timestep reduces diffusion to a direct regression model, resembling Unet with strong initialization but detached from diffusion principles. Such an application is crude without proper modeling and, intuitively, is unsuitable for handling different levels of degradation, such deblurring. Ignoring this inductive bias risks sacrificing diffusion'' 's robustness and fidelity. 2. Target Inconsistency in Image Restoration: Super-resolution and motion deblurring are full- reference tasks aimed at recovering images close to the clean target. However, most one- step methods prioritize no-reference perceptual metrics (e.g., CLIPIQA (Wang et al. 2023 MUSIQ (Ke et al., 2021).. sacrificing full-reference fidelity (e.g., PSNR, LPIPS (Zhang et al. 2018)), shown in Fig. Pretrained generative priors (e.g., Stable Diffusion (Rombach al.
1.
2022
Esser et al. 2024)) encourage models to generate perceptually pleasing but less faithful
reconstructions, inflating perceptual scores at the cost of restoration goals.
3.2 PRELIMINARY
According to the standard diffusion process (Ho et al. 2020 Rombach et al. 2022). we define
T ((11T120)) = II (2)0120-1), (( = N(z /1 944-1,111
(1)
1=1
where is the latent variable at time step 1, which is progressively corrupted by Gaussian noise starting from 2g. The mean and variance are determined by the noise schedule 3r.
In the reverse process, the mean value of the Gaussian distribution q(2;-112,, =o) is calculated as:
[e(2e, 20) = var ā(-1) 24 + / 1
- ae 20.
(2)
where o, 11 3, and ā I1 ar The denoising network cg. for predicting P0(2,-1 (1))) needs to minimize the difference with af Zt- 1 20). z0), Estimated é from eq is used to generate the řo via
24
ājē va
20
é= E= c,t)
(3)
Here, represents the control information, like text, image, other conditions. With Eq. 3 each step, Eg can directly restore the clean .n. However, for stable and high-quality generation, denoising from -t-1 is preferred over directly exiting the backward process, seen from Eq
3

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Published as a conference paper at ICLR 2026
3.3 FORWARD AND BACKWARD REFORMULATION FOR DEBLURRING
Motion blur in images is caused by the relative motion between the camera and the scene during the exposure time. When capturing image, each point in the scene is projected onto the image plane over time. This can be mathematically represented as an integral over the exposure time, and alternatively. it can also be approximated by pixel-wise convolution, where the blur kernel K represents the motion path of the camera or object, and the blurred image Auur is obtained by convolving the sharp image shrrp with the blur kernel K and adding noise n.
A 1 to { Sensor(t) dt =2 Sharrp * K + 72
(4)
In Fig. 3 we reformulate the forward and backward processes for the image motion blurring and deblur- ring. We define the clean image as 2o and the ini- tial blur kernel as identity convolution ko, where 20 2a * ko. From a pure clean image to the blurry image, we regard the forward blur kernel generation process as a chain, following:
Ap
FrJ
Jetty 1)
Jeft.
Figure 3: Forward and backward processes.
T (k1::r (k) II ((kk)k--uu).
(5)
Commonly used kernel generation methods involve generating random blur trajectories, which are convolved with sharp images to produce blurry counterparts. In these methods, the blur kernel typ- ically exhibits non-linear and non-uniform trajectories, with k, depending on previous states 1:010 based on different simulation techniques. For simplicity, Figure Biuuree]illustattee a globally uniform blur, while real-world scenarios apply the blur kernel pixel-wise basis.
At each state in the forward process, 2t calculated as zt = zo k2. For backward process, ((2(1122)) is intractable, we consider q(=-11 22, 2n) and define q(ke|22, 20) the conditional distribution of the variable k2. With identity convolution Ko, we try to calculate q(ke-1 ko) using
(k,,-lki,, ( (ikk-ilki g(ki)lko)
Once we have the distribution of q(kt-1 kk, ko). we can compute the q(21-1/21, 20) via 24-1 kz-1 2o, and then optimize the probability model ep to minimize the KL divergence between the 11z))) and (==-11 1 =0), following the approach outlined in previous work.
However, two major challenges arise. Firstly, (kss 20) is generally intractable. If the blur ker- nel were spatially uniform across the entire image, could be inferred directly from 2, and 2g- In practice, however, real motion blur kernels vary pixel-wise due to depth differences, camera motion, and independently moving objects, making a deterministic solution for ke impractical. Secondly, the forward kernel generation process is highly complex and non- non-Markovian, as it often depends on physical factors such as point velocity. impulse, and inertia (Kupyn et al., 2018:: BBracchi & Foi, 2012). Consequently, neither the marginal distribution { nor the conditional q(k:-1 ko) can be accurately modeled by simple parametric distributions (e.g., Gaussians), violating the as- sumptions commonly exploited in standard diffusion formulations.
Despite these challenges, the problem is not unsolvable. As Eq. and Eq. 3 reveal, the fundamen- tal objective of DMs is to reconstruct zo. We reformulate the training objective as a cross-time consistency regression, explicitly enforcing fo(2,, t) to yield consistent estimate of zy across all timesteps t. This temporal alignment naturally supports single-step stepe-step inference without requiring multi-step denoising iterations. According [chusterbbuer et al. (2025) and Tong et al. (2024), the need for multiple sampling steps in standard diffusion arises primarily from the random pairing between Gaussian noise samples and data points during training. If the blur trajectory of each image is known, and all points along the backward trajectory are jointly trained to map toward the same clean target, the model learns intrinsic temporal consistency, Formally, enforcing
zg = fo(=,, fo(ze,('), min E., [o( 1) zoll"
promotes trajectory consistency (Song et al. 2023) and facilitates accurate one-step sampling.
4

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Published as a conference paper at ICLR 2026
Arg. Frames
5
9
11
13
15
Total
GoPro
0
175
0
1,818
110
0
2,103
GoPro (Enlarged)
838
980
869
2,081
1,900
1,209
7,877
Table 1: Statistics of GoPro image training pairs.
In this work, we base our consistency training on the pretrained Stable Diffusion model. As pre- viously mentioned, DMs directly target Zu- Among different optimization objectives (e.g., Hh, e, or u-prediction (Salimans Ho 2022)), we opt for the Stable Diffusion 2.1 base model with t- prediction for simplicity. which is commonly used in related tasks. To better adapt the pretrained DMs to the deblurring task, we retain the original diffusion coefficients a, and 8t. and adjust the é Eg such that the following equation holds, where é is not necessarily Gaussian distribution:
= his = vārzo + v1 ā.ē.
(8)
3.4 TRAINING DATA PREPARATION
As discussed in 3.3 if we can group {=0, =1, =2, 22.....) for each sample it, then the training naturally satisfies the consistency model's objective, and thus facilitates the one-step sampling. If we do not know each sample's 's definite backward trajectory and just randomly add the blur kernel to the clean image during training, the actual direct mapping between a blurry one and its clean version will degrade, which requires multi-step inference for sample quality.
Our target is to build a dataset with each blurry sample grouped with its definite backward trajectory, which is not difficult for both the Merreeemel generation method and the consecutive- frame averaging method. In this work, we utilize the widely used GoPro (Nah et al.. 2017) dataset. The dataset utilizes 240fps GOPRO camera and average of 7 13 successive frames to generate the blurry image for training and 11 frames for testing, while the middle frame is regarded as the sharp image. Firstly, we construct mapping from the number of the averaging frames (n) to the timestep of the diffusion (1), with projection function t = ((n) = (n 1) x 20, which satisfies the boundary condition g(1) O, i.e., the averaged single frame is regarded as t 0, and also corresponds to 2g as the initial point of the forward process. Apart from that, as shown in Tab. [ the original GoPro dataset has an unrich data distribution with most of the data averaging with
frames. In order
to meet the requirements for consistency training, we manually enlarge the GoPro dataset to ensure that each blurry image has at least 3 points on backward trajectories. The new statistics are shown at the bottom of Tab. I The detailed synthetic process is listed in the supplementary material.
METHODOLOGY
In this section, we propose FideDifff as shown in Fig. 4 Sec. I. and the Kernel ControlNet with kernel estimation and timestep prediction in Sec. 4.2 At last, we clarify the whole training pipeline and the loss definition in Sec. 4.3
We introduce our foundation model in
4.1 DEBLURRING FOUNDATION MODEL
Based on Stable Diffusion (Rombach et al. 2022 Esser et al., 2024). FLUX (Labs) 2024). etc, the image restoration field has witnessed significant progress from generative perspective. However, ensuring the rationality and effectiveness of few/one-step models (Wu et al. 2024 Li et al. 2025a), while maintaining fidelity, remains an urgent issue.
According to the analysis in Sec. 3.3 we perform consistency training to enforce that the model produces temporally consistent estimates of z0 for noisy latents sampled along the same trajectory, thereby enabling single-step inference. Given a training pair (L1q, T=Q, t = g(n)), we first utilize the Variational AutoEneoder (VAE) to encode LL with the downsampling factor d and gain the latent representation ZLQ, i.e., 2: in Eq. 8 Then, the latent diffusion model takes 2 and i as input Tearnable text embedding, omitting the tokenizer and text
to predict = co(2,, f, c), where encoder for better efficiency. As defined in Eq. 8 we obtain the predicted ån via ê0 = 2 VT-VI-ar
Vaz
and decode it as İHQ. We will clarify the timestep i used in the inference phase in Sec. 4.2
To further pursue the fidelity, we abandon the various distillation methods (Wang et al., 2024 Wu et al. 2024) that are intended for more natural content generation, and adopt a GAN discrimina- tor D (Sauer et al. 2024 2025) to ensure that the generated samples closely match the real data distribution. Given the restored Eμ and zNQ (encoded by VAE from InQ). the discriminator is sponsible for distinguishing between the real high- high-quality representation ZHQQ and the generated &n. providing feedback the generator to refine the restoration process and enhance the fidelity of the
5

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Published as a conference paper at ICLR 2026
Kernel ControlNet
E Ezas
Loss
Generation
forward
r forword
Forward Data Generation
Joss forward
A
Trainable
DMS
1x2
Frozen
X
Element-wiise Mullipication
Aug.
Element-wise Addilion
Agg
Decoder
Tno
Adaptive Average Pooling
Encoder
Down Sample
DMs
Block
Convolution
Foundation Model
discriinator
Zero-Inilaaiced Convolution
Figure Training pipeline of our FideDiff. Optimized used for inference.
reconstructed data. The GAN discriminator consists of a pretrained UNet encoder (initialized from DMs' encoder) and several convolution blocks generate true false.
4.2 KERNEL CONTROLNET
End-to-end learning often overlooks crucial motion information, and incorporating kernel priors into DMs remains underexplored. For instance, Liu et al.] (2024b) employs two vision encoders extract semantic information
plugin, whereas the vanilla ControlNet (Zhang al. 2023) accepts conditions like human pose, depth for generation, but has not explored kernel information. Recently, Lin al. (2024) adopts a two-stage reconstruction approach and proposes IRControlNet as post-pon pest-processing modifier, enhancing the quality of the repaired image. The foundation model alone is far from sufficient for fighfiidlitty deblurring tasks. In Fig. with estimated k,, the network (2 t, k2) is expected to be more powerful to predict the given the fact 2, k, *
To address this, we design Kernel ControlNet, aiming inject kernel information as an additional condition into the Unet of the DMs. Specifically, given blurry input T.Q., we process the blurry image with a convolutional Unet .M in the image space, resulting in a blur kernel representation k; MM(HQQ). ke E Rn xW where represents the predefined kernel size. Then, do not adopt the direct mapping used in the original ControlNet where the condition mapped to kin and added to Zin; Conv(2;) 24), because the kernel map, unlike depth pose, does not have direct spatial correspondence with the target image. Instead, we apply a filter-like module F:
Zing Conv(Zing); W = (Conv(Ca((eas Kins Zinz));
0 W & Zinz:
2out
Zini + Z(O),
where Z is convolution initialized with zeros, W is the attention weight, and & denotes the element- wise multiplication. Then, Rout serves the input for the subsequent ControlNet C. The structure of the ControlNet remains the same as the original DM's encoder, and the initial parameters of ControlNet are copied from those of the encoder in DMs.
t-prediction. Additionally, Kernel ControlNet plays another crucial role in estimating the timestep t during the inference phase. As discussed Sec. 33. consistency training enables one-step sam- pling during inference. However, it remains unknown at this stage. To address this, we design a small regression model T that follows the kernel estimation network M to get the É T(M(HQ)) Specifically, the more complex and prolonged the kernel trajectory, the higher the blur level, and consequently, the larger the timestep becomes.
4.3 TRAINING PIPELINE
Our training has three stages. The first stage is the foundation model training:
L = L JHQ) + Xı L JuQ) + >eLa(TnQ),
(9)
LG - -E,, (log D(ān))),
(10)
LD - E, (log D(=HQ)) Eiso (log(1 D(HHQ))).
(11)
The second stage the kernel estimation network M's pretraining. We define the reblur loss as
Crebiur (M((TQQ IHQ, ILQ),
(12)
which uses the pixel-wish wise kernel estimation map to convolve the clean image, thereby regulating the kernel estimation via backpropagation. For the third stage, the foundation model is frozen, and only

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Published as a conference paper at ICLR 2026
Metrics
Restormer
HIlDiff
UFPNet
AdaReyD
DiMBI
OSEDiT
Difl- Plug-nn
UID-Din"
FideDiff
Famceral p011
Bongrral MXN
Man
Lnntal Norh
Chrrp prrpaaal Hing
PSNR1
32.92
33.33
34,09
34.60
26.15
24.34
22.88
25.08
28.79
SSIMT
0.9611
0,9642
0.9686
0.9716
0.8377
0.8228
0.7798
0.7403
0.9148
GoPro
LPIPS.
0.0841
0.0799
0.0764
0.0712
0.2366
0.1738
0.2332
0.1310
0.0831
DISTS
0.0724
0.0710
0.0606
0.0672
0,1460
0.0834
0.1166
N/A
0.0525
PSNRT
31.22
31,46
31,74
32.35
25.94
23.20
21.94
N/A
27.28
SSIMT
0,9423
0,9446
0,9471
0.9525
0,8216
0.7611
0.7272
N/A
0.8775
HIDE
L 1
0.1082
0.1055
0,0931
0.0872
0,2091
0,2208
0,2732
N/A
0.1068
DISTS
0,0730
0.0725
0,0676
0,0666
0,1246
0.1001
0,1411
N/A
0.0644
PSNR
28.96
29.15
29.87
30.12
26.92
26.83
25.77
22.76
28.96
RealBlur
SSIMT
0.8786
0.8898
0,8840
0.8945
0,7450
0.8004
0.7711
0.7693
0.8695
LPIPS
0.1561
0.70
0.1441
0.1408
0.2587
0,2055
0.1379
0.1142
DISTS]
0,1112
0.1050
0,1101
0,1037
0.1599
0.1198
0.1355
N/A
0,0800
PSNR
36.19
36.28
36.25
36.53
32.60
33.54
32.64
22.47
36.01
RealBlur-R
SSIM
0.9572
0.9583
0,9528
0.9570
0.8493
0.9056
0.8496
0.7384
0,9424
LPIPS
0.0608
0.0602
0.0615
0.0621
0.3388
0.1057
0.1422
0.2348
0.0584
DISTS.
0,0833
0.0814
0.0854
0.0846
0.2576
0.1322
0.1673
N/A
0.0862
Table 2: Comparison results with full-reference metrics on the four datasets.
the Kernel ControlNet (,,C,M,T) is optimized:
L = (Tnqq THQ) + x LEA Tuq) + Aa +
(13)
EA-LPIPS is the LPIPS loss with edge detection model, proven useful in many works (Wang) ct al.
2025
Li et al. 2025b). Lisme a simple l2 regression loss, detailed in the supplementary.
EXPERIMENTS
5.1 EXPERIMENT SETTINGS
Dataset and Evaluation. In line with previous studies, we utilize the GoPro (Nah et al.) (017). HIDE (Shen et al. 2019), and RealBlur (Rim et al. (020) datasets. The GoPro dataset consists of 2, 1,10/11,11 sharp-blurry pairs for training and testing. The HIDE dataset contains 2,025 testing pairs. The RealBlur dataset has two subsets, and R. each with 980 testing pairs. FideDiff is trained on the GoPro training set and evaluated on the four test sets others. For evaluation, we employ the full-reference metrics: PSNR, SSIM, LPIPS (Zhang et al. 2018). and DISTS (Ding et al.) 2020). Implementation Details. FideDiff is based on Stable Diffusion (SD) 2.1 base version and fine- tuned with LoRA (Hu et al. 2022) in the first stage, and Kernel ControlNet is trained with full- parameter training. Because GoPro, HIDE, and RealBlur are low-resolution and affected by shoot- ing equipment and techniques, they unsuitable for direct SD use, especially when VAE compres- sion loses details. To fix this, we cut the VAE downsampling from d' = 8 to d = 4 by 2 upsampling conducted on four NVIDIA
inputs before FideDiff and resizing outputs back. Our experiments A800-80GB GPUs More implementation details and discussion are provided in the supplementary material. To ensure fair comparison, we train FideDiff on the original GoPro and other models on the enlarged GoPro, as detailed in Sec. 5.3 The GoPro/HIDE test set is originally synthesized by 11 consecutive frames, so tGT 200. RealBlur, being real-world captured, lacks a tGT. By default, we use f = 200 for GoPro/HIDE and the t-prediction strategy for RealBlur during inference.
5.2 EVALUATIONS
We compare our model, FideDiff, with many models in Tab. 2 including transformer-ban models: Restormer (Zamir et al., 2022). HI-Diff (Chen et al. 2023). UFPNet (Fang al. 2023), AdaRevD (Mao et al.) 2024), and dastrainedddiffusioo---aaeee
DIfIBIR (Lin et al. 2024),
OSEDiff (Wu et al.) 2022)) Diff-Plugin (Liu et al.) 20245). UID-Diff (Cheng et al. 2025
DifBBIR/SSEDif/DDitt Plugin/UID UID- are based on the SD v2.1 1...-bbse//2.111 wtw.1-bbeevl..//// tively. Diff-Plugin is originally trained on GoPro, while OSEDiff is retrained by us on GoPro. Diff- BIR is trained on 15M dataset to address almost any type of content loss, and combine it with AdaRevD to enhance its results. UID-Diff uses a larger-seale unpaired deblur dataset including GoPro, and as is closed-source, the metries in Tab. 2 are directly copied from their paper.
respec-
Quantitative Results. As shown in Tab. 2 our model, FideDiff, outperforms diffusion-based methods across four full-reference metrics by a large margin. Additionally, for perceptual simi- larity metrics (LPIPS/DISTS) of the metrics across all four datasets. Notably, when evaluated on the real-word wollwwrldd dataset RealBlur, model demonstrates robust generalization capabilities, with perceptual similarity outperforming
model surpasses four transformer- bassed methods on at least half
7

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Blurry
Restoreer
Dal
UFFNet
AdaReyD
GoPro (0862_111.000 1100000000))))
Diff-Plugin
OSEDiff
FideDiff (ours)
HQ
Blurry
Restormer
III Dal
UFPNet
AdaRevD
Real (scenel 176-18)
Diff-Plugin
OSEDiff
(ours)
HQ
7>
4+
17
4+1
17
4+1
32108
Bluryy
Restormer
HI-Diff
UFPNet
AdaRevD
17
4+1
171
4+1
1
4+1
17
4+1
RealBlur 194-1e194.19)
HQ Figure 5: Visual comparison with the transformer based and diffusion based models.
DiMBIR
Dill-Plugin
OSEDiT
FideDiff (ours)
Model
GoPro
RealBlur
Restormer
1.14
0.64
HI-Diff
0,58
Possible
UFFNeet
AdaRevD
0.75
1.09
0.42
0.66
Better quality
DIE
DISEDDE
DiffBIR-s50
25.40
12.84
UID-Diff=33'
N/A
URNN -H--DI
UID gir
DiIT Plugin <20
5.29
2.48
AdakeeD
OSEDiff-s
0.32
0.19
Ours (d=8, w/o KCN)
0.25
0.14
Impossible
Ours (de4, w/o KCN)
1.28
0.60
Less distortion
Ours (d=4)
1.52
0.72
Figure perception-distoriinn comparisons. most models, while the distortion metrics PSNR/SSIM show a significant reduction in the gap com- pared to transformerbbase methods. These results indicate that model not only better teaches the pretrained model to handle diverse types of blur but also generalizes significantly to real-world scenarios, making crucial step towards high-fidelity image motion deblurring. Furthermore, plot the perception-diitortion tradeoff (Blau & Michaeli 20188) curves for different models, as shown Fig. [6] This further demonstrates that our model is closer to the original image in terms of per- methods.
Table Inference speed (sec/image).
PSNN
ception, offering balanced alternative to transformer-
Speed. In Tab. 3 our foundation model with downsampling factor d = 8 achieves the fastest in- ference speed. As mentioned in Sec. 5.]] influenced by the capturing devices and the low resolution of the datasets, we set = 4, sacrificing time for reduced detail loss. The introduction of Kernel most terafforerr-bused. methods
ControlNet further lowers the speed, but it is still comparable and significantly faster than multi-step DMs, achieving up to a 17 speedup.
Visualizations. Visual comparisons are shown in Figs. and 5 It is evident that FideDiff signif- icantly outperforms diffusion- based methods, with restored details closer the ground truth com- pared to transformer-basee methods. More visualizations are shown in the supplementary material.
5.3 ABLATION STUDY
we present an abla- tion study of our foundation model on the original
Foundation Model. In Tab. 1
size
GAN LE PSNR1 SSIM LPIPS
DISTS.
384
I.PIPS
26.12
0.8609
0.1119
0.0648
GoPro dataset, showing that LPIPS loss improves
384
EA-LPIPS
X
26.07
0.8601
0.1138
0.0641
perceptual fidelity more effectively than DISTS.
EA-LPIPS
26.21
0.8631
0.1098
0.0732
384
EA-DISTS
25.58
0.8470
0.1288
0.0601
and that the GAN discriminator plays a crucial role- plet-parillllrll when optimizing for the DISTS metric. addition, we demonstrate that using a learnalle text embedding (LFE yields better perfor-
EAPP
0.8636
0.0633
384 EA-LPIPS
27.77
0.8970
0.1020
0.0633
EA-LPIPS
28.51
0.9101
0.0899
0.0555
Table 4: Foundation model ablation.
8

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Published as a conference paper at ICLR 2026
(CUPh))
FSUR (WITF)
-a- 1IPS (Gahe) - PIPS)Im))
28.5
PSNR
28.32
0.1
28,
59
28.69
25.65
55.5
28.45
LPEES
0:15
Model
PSNR1
SSIM1
Golfro
LPIPS)
DISTS]
PSNR1
SSIM)
HIDE
LPIPS)
DISTS)
base
28.68
0,9130
0.854
00533
27.30
08761
0.1094
0,089
0.1278
0:13
controluct
28.73
0,9139
00844
27.27
0.1077
27.5
+0.19
27.23
AUS
21.25
0,1095
0 1069
1065
099
0.11
motion align
28.75
0.9140
0.0511
0.0538
27.28
0.8777
0.1081
0.0658
57.13
keneel addition
28.70
0.9135
0.835
27.25
0.8770
0.1073
26,5
0.,895
0,0854
0,0422
10,083)
0.00
kernel
28.79
0.9148
0.0831
0.0525
27.28
0.8775
0.1068
0.0647
120
150
200
260
timaske
Figure 7: Performance w.r.t steps.
Table 5: Kernel ControlNet's effectiveness.
mance than adopting a fixed text embedding. We also validate that incorporating edge-enhanced perceptual loss leads to further performance gains. We demonstrate that using d = 4 significantly surpasses d 8. mainly due to the limitations of the dataset, e.g., some scenes contain over 40 people at only 1280x resolution, causing substantial detail loss during SD processing (detailed in the supplementary). Increasing the training patch size further improves performance, but we ultimately use 512 due to GPU memory constraints.
Kernel ControlNet. Table 5 demonstrates the effectiveness of our Kernel ControlNet, which out- performs both the foundation model and a vanilla ControlNet (using only T as input). We further show that our filter module is more effective than directly adding the kernel to 2nQ especially in terms of PSNR and SSIM. We also experiment with motion alignment module based on MISC- Filer (Liu et al. 2024a), but it is less effective than our Kernel ControlNet.
Dataset
Metrics
w/o CT
w/ CT
w/ CT & TP
Dataset
Metrics
OSEDiI--S
OSEDiF--
FideDiff-S FideDif-L
PSNR
28.74
28.79
28.62
PSNR1
24.34
24,29
28.56
28,79
SSIM
0.9142
0,9148
0.9123
SSIMT
0,9109
0.9148
LPIPS]
D.0771
0,0831
0,0828
GoPro
LPIPS
0.1738
0.1634
0.0873
0,0831
DISTS.
0,05418
0.0525
0,0522
DISTS]
0.0834
0,0772
0,0549
0.0525
PSNR
27.26
27.28
27.28
PSNR1
23,20
23.52
27.07
27.28
HIDE
[PIPS]
SSIMT
D.1192
D.774
0.1068
0.8775
0.1155
0,8779
HIDE
LPIPS
SSIMt
0.2208
0.7611
0.7779
0.2137
0.1141
0.7337
0.1068
0.8775
DISTS
0,0705
0,0647
0.0694
DISTSI
0.1001
0.1036
0,0690
0.0647
PSNR
28.95
PSNR
26,83
26.92
28.88
28.96
RealBlur-J
SSIM
D68687
0.8691
0,8695
RealBlur-J
SSIMT
D 8000
0,8044
0 0.8680
0.8695
LPIPS
0.1122
0,1151
0.1142
LPIPS
0.1793
0.1758
0.1145
0,1142
DISTS
0,0790
0,0804
DISTS
0.1198
0.0804
0.0800
PSNR
35.77
35.92
36.01
PSNRT
33.54
33.04
35.91
36.01
RealBlur-R R
LPIPS
SSIMT
D.9344
0.0662
0.0600
0.9414
11.4224
RealBlur- R
LPIPS
SSIMI
0.1057
0.9056
0,8937
0.1074
0.0606
0,9400
0,9424
0,0584
DISTS
0.0961
0.0881
0.0862
DISTS
0.1322
0.1408
0.0894
0.0862
Table 6: CT and TP ablation.
Table 7: Fair comparison.
Consistency training (CT) and time prediction (TP). In Tab. "w/
FID,
CT refers to our time-consistency training, where different blur lev- els correspond to varying t values ranging from 80 to 280 (calcu-
GoPro w/ CT
17.5
GoPro weo CT
lated with g(n) according to Tab. T.. "w/o CT means all training images are set to the same = 200. When inference set with i == tor = 200, FideDiff w/ CT outperforms FideDiff w/o CT on GoPro//HDDE/aaaBllrrR especially in perceptual similarity metrics like LPIPS and DISTS. This demonstrates the effectiveness of our consistency training in better decoupling different levels of blurriness and controlling blurry details. After adding t-prediction to the CT model, performance all improves on RealBlur, proving that t-prediction is effective for real-world blur with good generalization, further enhancing fidelity. On GoPro/HIDE, although t-prediction cannot perfectly predict = tGr, its performance is comparable to using no t-prediction.
15.0
HIDE CT
HIDE wro CT
12.5
10.0
100
200
Steps
Figure 8: FID w.r.t. steps.
Additionally, we analyze the variation in distortion (PSNR, SSIM) and perception (LPIPS, DISTS) of our foundation model w/ CT at different timesteps on GoPro/HIDE in Fig. Despite ter = 200, when f varies between approximately 120 and 280, the model maintains good performance, with either perception or distortion being better than at f 200. This demonstrates the robustness and desirable properties of our model, which are not presented w/o CT (in the supplementary material). Furthermore, we plot the FID curves of two models on the GoPro/HIDE datasets in Fig. 8 where w/CT demonstrates superior distribution similarity across all timesteps and maintains good gener- alization over wider range. The effectiveness of CT and TP inspires us to see how DMs can deal more effectively with varying degradation levels. The correct identification of degradation levels is crucial for achieving accurate model performance, treating all levels uniformly is not effective.

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Published as a conference paper at ICLR 2026
Fair Comparison. We train FideDiff the original GoPro (S) and OSEDiff on enlarged GoPro (L). As shown in Tab. 7 FideDiff-S still outperforms the diffusion baseline and can effectively leverage the abundant data to further boost performance. We also show the AdaRevD retraining results in the supplementary material, where it also struggles to fully learn the enlarged data.
CONCLUSION
In this paper, we analyze the challenges faced by current DMs in real-world deployment, focusing on time efficiency and fidelity. We reformulate the forward and backward processes of deblurring, design a time-consistent training paradigm, and develop FideDiff for high-fidelity single-step de- blurring. We also introduce Kernel ControlNet to inject blur kernel conditions into the foundation model for enhanced fidelity, and we predict the timestep based on kernel estimation to dynamically select it during inference. Our model demonstrates strong performance evaluations, advancing rapid, high fidelity restoration of DMs in real-word applications and driving the field's 's progress.
ACKNOWLEDGEMENTS
This work is supported by the National Natural Science Foundation of China (62501386, 625B21 16)) and CCF-Tencent Rhino-Bird Open Research Fund.
ETHIC STATEMENT
All authors confirm adherence the ICLR Code of Ethics throughout submission and discussion.
REPRODUCIBILITY STATEMENT
We provide detailed description of the training and testing procedure in the supplementary material and will release the code and dataset.
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Reviews:
[{"review_id": "cq2DRewV1j", "reviewer": "Reviewer_jEid", "summary": "The paper proposes applying a diffusion-model to the single-image deblurring task. The authors claim that by diffusion process, they achieve high-fidelity sharp image restoration on datasets such as GoPro and RealBlur‑R.", "questions": "Could you provide more visual materials such as videos?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "The idea of using a diffusion-model for deblurring is interesting in the deblurring community.\n\nThe paper presents a comprehensive experimental evaluation with multiple datasets, which shows the authors’ effort to validate the method across settings.\n\nThe writing is clearly structured, and the method is described with reasonably good clarity in terms of pipeline, training and inference details.", "weaknesses": "1. Numbers inconsistent with public SOTA.\n\nOn GoPro, the paper reports PSNR 28.79 dB, far below recent results, e.g., 34.09 dB LoFormer; 34.60 dB AdaRevD. On RealBlur-R, it reports 36.01 dB, while widely reported numbers are 38.6 dB. This raises concerns about evaluation setup and fairness.\n\n2.Perceptual evaluation is insufficient.\n\nFor a diffusion model, perceptual quality should be central. The paper underweights CLIP-IQA/LPIPS/DISTS and provides limited qualitative/video evidence. No user study or side-by-side videos under realistic motion is shown.\n\n3. Cost–benefit trade-off is unclear.\n\nThe method adds diffusion + kernel control modules, yet runtime, memory, and step count are not thoroughly compared to strong CNN/Transformer baselines under the same hardware. Efficiency claims are therefore hard to judge.\n\n4. Ablations not fully isolating gains.\n\nThe paper does not cleanly separate the contributions of the foundation diffusion, Kernel ControlNet, and t-prediction across datasets. Stronger ablations and cross-dataset tests are needed to establish robustness.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "dYWEHdyChw", "reviewer": "Reviewer_qJtp", "summary": "The paper proposes FideDiff, a single-step diffusion model that reformulates motion deblurring as a diffusion-like process where each timestep corresponds to a progressively blurred image rather than Gaussian noise levels.\nThe core technical contributions include a consistency training paradigm where the model learns to map all blur levels along a trajectory to the same clean image and Kernel ControlNet, which integrates blur kernel estimation into the diffusion model via a filter-like module\nThe model is built upon Stable Diffusion 2.1 and evaluated on GoPro, HIDE, and RealBlur datasets.", "questions": "- What is the actual benefit of using pre-trained diffusion models if both performance and speed are inferior to specialized transformer models? This brings an interesting discussion. The authors claim that generative models produce higher image quality due ot their ability to generate/hallucinate scene details. Yet in most examples in the supplementary, the result is nearly identical across all state-of-the-art models. In addition, the evaluation still focuses on standard image similarity metrics: PSNR / SSIM / LPIPS, favoring noisier solutions that hallucinate less (transformer-based models). To demonstrate the effectiveness of the method, authors should consider computing image quality metrics (FID and others) or conducting a user preference study.\n- Do you think the current approach might require orders of magnitude more data than transformer methods to achieve superior performance to transformer-based methods?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. **Problem Formulation Is Reasonable**: The paper clearly discusses the limitations of existing diffusion-based deblurring methods (significant inference time, fidelity-perception tradeoff) and provides concrete evidence (Figure 2) showing the tradeoff between steps and fidelity in super-resolution as motivation.\n\n2. **Consistency Training Framework**: The reformulation of deblurring as a diffusion-like process where timesteps correspond to blur severity rather than noise levels is reasonable. The insight that temporal consistency can enable single-step inference is valuable.\n\n3. **Comprehensive Experimental Evaluation**: The paper includes extensive comparisons across multiple datasets (GoPro, HIDE, RealBlur-J, RealBlur-R) and metrics (PSNR, SSIM, LPIPS, DISTS), with both transformer-based and diffusion-based baselines. Tables 4-7 and Figures 7-8 systematically evaluate the contribution of each component (foundation model design choices, Kernel ControlNet variants, consistency training, timestep prediction).", "weaknesses": "1. **Conceptual Novelty and Practical Advantages** Single-step diffusion with Kernel ControlNet and GAN discriminator is conceptually similar to transformer-based models with discriminators. The main distinction is initialization from pre-trained Stable Diffusion weights. However, this does not translate to practical advantages, the performance (both speed and metrics on academic benchmarks) is still a bit inferior or similar to transformer-based methods \n\n2. **Dataset Scale**\nThe training relies on the enlarged GoPro dataset containing only ~7,877 image pairs, which might be too small to efficiently fine-tune large-scale generative models, which partially can explain the performance gap.\n\nMinor:\n- The filter-like module F (Section 4.2) is presented as novel, but it's a relatively standard way to inject spatial information\n- The improvement over vanilla ControlNet is small: 0.06 PSNR (28.73 → 28.79) showing that theoretically correct kernel fusion doesnt have signifact impact on model performance.\n- Table 5 also shows \"motion align\" module performs similarly (28.75 PSNR), suggesting the specific design choice is not critical\n- Line 013: \"true-world modeling\" should be \"real-world modeling\"", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "01kxKF0mEj", "reviewer": "Reviewer_Hp3A", "summary": "This paper proposed a novel motion deblurring architecture based on a pre-trained stable diffusion model, successfully distil the multi-step diffusion model into a single-step model.", "questions": "1. What is the blue block in Figure 4?\n2. Why can't the model be trained end-to-end instead of using three stages.\n3. Any evaluation of time prediction? I am not sure how accurate it is.\n4. The data generation process in supp is unclear to me. What is the meaning of \"For the 13-frame average sample, using the middle sharp frame as a reference, we synthesize blurry images by averaging 15 and 11 frames\" and \"For the 11-frame average sample, we employ a probabilistic augmentation method. First, we synthesize all 13-frame combinations. Then, we generate blurry images by averaging 9 and 15 frames based on a probability distribution of [0.3, 0.7].\"", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. The performance shown in the paper is promising. Even though the model is trained on synthetic data, it shows better performance on all datasets compared with other diffusion-based methods.\n2. Solid mathematical analysis. Although the markov prediction of kt from kt-1 is infeasible to calculate, following the training assumption of diffusion model, every training step share the same sharp ground truth, which makes the single-step possible.\n3. The paper proposed an efficient single-step diffusion model that avoid the loss of diffusion inductive bias as other one-step methods did, which reduce the sensitivity of the method to spatial-variant and severe blurs. The key thing here is the proposed time-consistency training. \n4. High-fidelity results, which surpass both conventional and diffusion based methods.\n5. Properly modify the controlnet to better inject the blur kernel as an extra condition.", "weaknesses": "1. How to generate z_0 with input Z_LQ is unclear. Needs further clarification.\n2. The training data compatible of this architecture are limited, which reduces the generalization of the proposed method.\n3. How to concatenate latent feature z with the estimated blur kernel?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3mk4e86yGZ", "reviewer": "Reviewer_n57E", "summary": "This paper introduces a single-step, diffusion-based deblurring model that improves the pixel and perceptual fidelity of deblurring results. Motion deblurring can be formulated as a diffusion-like process, with each timestep representing an increasingly blurred image. A kernel control net is proposed for kernel estimation and adaptive timestep prediction. This approach outperforms existing diffusion-based methods and achieves superior performance on reference-based metrics.", "questions": "Please See the weaknesses part.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "The proposed method is novel, and the manuscript is well organised and written. FideDiff significantly improves the fidelity of deblurring results, outperforming other diffusion-based models. It may also encourage other researchers to explore diffusion-based, high-fidelity methods for other restoration tasks.", "weaknesses": "1.\tIn Section 3.4, the mapping function between the number of averaging frames and the timestep is t = g(n) = (n - 1) * 20. Why was this set?\n2.\tThe symbol * is used many times in the manuscript, but it has different meanings in each instance. For example, in Eq. (4), it represents a convolution operation. However, in the mapping function t = g(n), it may represent a multiplication operation. Please clarify this.\n3.\tThe proposed framework uses a learnable text embedding as a condition for prediction. However, this is lacking in the framework architecture shown in Fig. 4. Moreover, what are the text descriptions used in the proposed method? Is the CLIP model used in the proposed framework?\n4.\tFigure 4 lacks the symbol k_t, which represents a blur kernel. Furthermore, Z_(in1) = Conv(Z_t) should be Z_(in1) = Conv(Z_(LQ)(Z_t)). Where is k_(in) in Fig. 4? What do the orange and blue blocks represent? In the filter-like module F, these lines lack arrows. Does * represents the element-wise multiplication? Why are there two lines with arrows showing the output of the DMs?\n5.\tWhat is the EA-LPIPS function? How does it influence the performance of image deblurring? Please conduct experiments to evaluate this.\n6.\tWhat is the meaning of * in Eq.(12)?\n7.\tI think the proposed method could be extended to other low-level tasks, such as super-resolution and deraining. However, it is unclear how it can be extended to image compression, given that this task does not involve modelling physical degradation.\n8.\tIn the implementation details, I see that you perform 2x upsampling before inference and then resize the outputs back to their original spatial size. What interpolation operation is used here, and might it cause detail loss?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)