RED: Robust Event-Guided Motion Deblurring with Modality-Specific Disentangled Representation

## Abstract

Abstract Event cameras provide sparse yet temporally high-resolution motion information, demonstrating great potential for motion deblurring.
However, the delicate events are highly susceptible to noise. Although noise can be reduced by raising the threshold of Dynamic Vision Sensors (DVS), this inevitably causes under-reporting of events.
Most existing event-guided deblurring methods overlook this practical trade-off, and the indiscriminate feature extraction and naive fusion result in unstable and mixed representations and ultimately unsatisfactory performance.
To tackle these challenges, we propose a Robust Event-guided Deblurring (RED) network with modality-specific disentangled representation.
First, we introduce a Robustness-Oriented Perturbation Strategy (RPS) that mimics various DVS thresholds, exposing RED to diverse under-reporting patterns and thereby fostering robustness under unknown conditions.
With an adaption to RPS, a Modality-specific Representation Mechanism (MRM) is designed to explicitly model semantic understanding, motion priors, and cross-modality correlations from two inherently distinct but complementary sources: blurry images and partially disrupted events.
Building on these reliable features, two interactive modules are presented to enhance motion-sensitive areas in blurry images and inject semantic context into under-reporting event representations.
Extensive experiments on synthetic and real-world datasets demonstrate RED consistently achieves state-of-the-art performance in terms of both accuracy and robustness.

## 1 Introduction

Motion blur is a pervasive degradation in dynamic visual scenes, typically caused by rapid object motion or unintended camera shake during exposure.
Image deblurring aims to reconstruct sharp textures and structures from a blurred observation, with approaches spanning handcrafted priors (Levin et al., 2009; Krishnan et al., 2011; Bahat et al., 2017) to modern CNN- and Transformer-based models (Nah et al., 2017b; Tao et al., 2018; Dong et al., 2023; Kong et al., 2023; Liang et al., 2024; Wang et al., 2024).
Yet under severe motion blur, critical structural and temporal details are heavily corrupted, and the performance of these methods degrades markedly.

Event cameras, inspired by biological vision systems, have emerged as a promising alternative to conventional sensors in high-speed scenes (Gallego et al., 2020).
They emit asynchronous streams of events that encode motion with high temporal precision, making them naturally well suited for motion deblurring.
Early methods (Pan et al., 2019; Zhang & Yu, 2022) relied on physical models to establish deterministic mappings between blurred images and events, whereas recent studies have adopted deep learning-based solutions that enable more expressive and powerful representations.
In particular, cross-modal fusion strategies such as event-image attention mechanisms (Sun et al., 2022), spatio-temporal attention modules (Yang et al., 2024), and frequency-aware interactions (Kim et al., 2024) have been introduced to bridge the gap between events and blurry images.
Furthermore, feature interaction strategies such as unidirectional event-guided fusion (Sun et al., 2023), bidirectional modality alignment (Chen & Yu, 2024), and asymmetric bidirectional integration (Yang et al., 2025) further explore the synergistic potential of multi-modal features.

Figure: Figure 1: Robustness analysis. Performance of existing event-based deblurring methods to noisy events and under-reporting events individually under low and high DVS threshold are shown in (c) and (d). As the noise or under-reporting ratio increases, the performance degrade sharply and fall below the image-only method DSTN.
Refer to caption: x1.png

Despite these advances, event cameras are intrinsically susceptible to noise because an event is triggered whenever the log-intensity change at a pixel exceeds a contrast threshold.
This threshold-triggering mechanism introduces various noise sources, such as background activity, photon shot noise, and electronic circuit noise, which carries little semantic information and contaminates the event stream (Guo & Delbruck, 2022; Duan, 2024).
As shown in Figure [1](https://arxiv.org/html/2509.05554v2#S1.F1)c, the performance of recent event-guided deblurring models drops sharply as the noise ratio increases.
Training with noisy events can partially improve robustness under heavy noise, but this inevitably sacrifices fidelity due to the low quality of events and the difficulty of extracting reliable motion priors.
Alternatively, raising the threshold of Dynamic Vision Sensors (DVS) can suppress spurious noise events to some extent (Vishenvskiy et al., 2024).
However, this inevitably induces threshold-driven under-reporting as weak motions and low-contrast edges fall below the trigger condition are not reported.
As illustrated in Figure  [1](https://arxiv.org/html/2509.05554v2#S1.F1)d, existing methods tolerate mild under-reporting but degrade rapidly once the ratio exceeds 0.20, even performing worse than image-only deblurring (Pan et al., 2023).
This contradicts the common assumption that events could monotonically provide positive contributions to deblurring.
Under pronounced under-reporting, the marginal utility of events becomes non-positive and can even be detrimental.
We attribute this to the fact that current pipelines ignores the disrupted events in practice, and often process images and events indiscriminately with shared feature extractors or naive fusion, leading to unstable and mixed representations and ultimately unsatisfactory results.
This motivates the need for a modality-specific representation that isolates event-specific motion evidence while preserving image structures, followed by robustness-aware cross-modal interactions to adaptively leverage reliable features.

In this paper, we present a Robust Event-guided Deblurring network (RED) with modality-specific disentangled representation.
First, we introduce a Robustness-Oriented Perturbation Strategy (RPS), which mimics realistic under-reporting events by varying DVS thresholds, enabling the network to adapt to diverse event dropouts.
It simultaneously expose key issues of deblurring network in practice:
indiscriminate extraction or naive fusion introduces unreliable features, and the absence of weak events makes deblurring more difficult.
To address these issues and effectively adapt to RPS, a Modality-specific Representation Mechanism (MRM) is designed to disentangle semantic, motion, and cross-modality features.
By separating semantic information from images and motion details from events before fusion, MRM prevents corrupted event characteristics from overwhelming image semantics and allows our RED to retain useful motion priors.
Finally, two modules are designed to achieve coadjutant interactions:
a Motion Saliency Enhancer Module (MSEM) that transfers motion-sensitive priors to enhance spatial details easily lost in blur, and
an Event Semantic Engraver Module (ESEM) that delineate semantic characteristics from images into deeper event encoding, mitigating the semantic deficiency caused by sparse events.
In this way, RED achieves reliable deblurring through robust event adaptability, disentangled representation learning, and effective cross-modal interaction.
In summary, our main contributions are as follows:

- 1.
We propose RED for robust event-guided motion deblurring.
Extensive experiments demonstrate our RED outperforms existing methods in both deblurring quality and robustness on synthetic and real-world datasets.
- 2.
RPS exposes our RED to diverse under-reporting events in various DVS thresholds, enhancing the robustness and adaptability to real-world conditions.
- 3.
MRM is tailored to factorize feature space into semantic and temporal dimensions for disentangled modality-specific representation, while robust motion-sensitive priors and compensatory semantic context are interacted by MSEM and ESEM.

## 2 Related Work

### 2.1 Image Deblurring

Image deblurring seeks to recover a clear image from its motion-blurred counterpart, a degradation typically caused by camera shake or dynamic object motion.
Classical image deblurring methods typically follow a two-step pipeline: estimating the blur kernel and performing non-blind deconvolution (Fergus et al., 2006; Cho & Lee, 2009).
To constrain this ill-posed problem, various image priors, such as Total Variation (Chan & Wong, 1998), sparsity (Xu et al., 2013), patch recurrence (Michaeli & Irani, 2014), and color statistics(Pan et al., 2016) have been explored.
While effective in controlled settings, these approaches struggle with spatially-varying blur and rely heavily on accurate kernel modeling (Tran et al., 2021; Hyun Kim et al., 2013), often leading to unstable performance and high computational cost.
With the advent of deep learning, CNN-based architectures have demonstrated notable advances by directly learning the blur-to-sharp mapping from large-scale images.
Multi-scale feature fusion (Nah et al., 2017b; Tao et al., 2018; Dong et al., 2023), multi-stage refinement (Chen et al., 2021; Zamir et al., 2021b; Yang et al., 2022; Liu et al., 2024), and coarse-to-fine strategies (Cho et al., 2021; Zheng et al., 2022) have been extensively explored.
More recently, attention-based models leveraging both spatial- or frequency- domain representations (Tsai et al., 2022; Zamir et al., 2022; Kong et al., 2023; Mao et al., 2024) have achieved state-of-the-art performance by capturing long-range dependencies and preserving fine-grained structures.
Despite these advancements, existing models still struggle under severe or complex blur conditions.

### 2.2 Event-based Motion Deblurring

Event cameras offer low-latency sensing by asynchronously recording per-pixel brightness changes, making them well-suited for high-speed motion deblurring.
Early works such as EDI (Pan et al., 2019) attempted to explicitly model the physical relationship, while recent methods have leveraged deep learning to construct more robust and expressive representations.
A straightforward approach concatenated event with RGB images to assist image deblurring (Jiang et al., 2020), but such naive integration failed to capture fine-grained interactions across modalities.
D2Nets (Shang et al., 2021) designed a learnable weight matrix to embed event priors into arbitrary image deblurring networks.
Further advancing event representation, EFNet (Sun et al., 2022) introduced a symmetric cumulative event encoding combined with a cross-attention mechanism to enhance inter-modal feature interaction.
To better exploit modality complementarity and suppress redundancy, STCNet (Yang et al., 2024) designed a cross-modal co-attention module that dynamically fuses spatial features from both streams.
FEVD (Kim et al., 2024) presented a frequency-domain filtering approach that captures global cross-modal dependencies via spatial frequency interactions.
Most recently, AHDINet (Yang et al., 2025) argued that symmetric or unidirectional fusion strategies (Sun et al., 2023; Chen & Yu, 2024) may lead to either insufficient or redundant interactions. They instead proposed an asymmetric bidirectional integration framework to more effectively leverage complementary information.

Despite these advances, most existing methods implicitly assume that event streams are complete and reliable.
In practice, however, events are inherently noisy or under-reporting due to the thresholding mechanism of DVS, and this disrupts the quality of motion information.
With an ignorance of the challenging events in practice, shared feature extractors or generic fusion strategies in current pipelines fail to effectively disentangle semantic context from images and motion evidence from practical events, leading to fragile cross-modal representations and reduced robustness.

Figure: Figure 2: Overview of our RED. In detail, a Robustness-Oriented Perturbation Strategy (RPS) is implemented to event input, and MRM is designed to disentangle modality-specific features with individually semantic reasoning in image module, motion-wise representation in motion module, and cross-modality fusion in cross-modal module. Furthermore, Motion Saliency Enhancer Module (MSEM) is designed to excavate motion-sensitive priors to image branch and Event Semantic Engraver Module (ESEM) is presented to compensate events with global semantic understanding.
Refer to caption: x2.png

## 3 Methods

In this paper, we propose a Robust Event-guided Deblurring (RED) network
in Figure [2](https://arxiv.org/html/2509.05554v2#S2.F2).
In detail, RED first performs Robustness-Oriented Perturbation Strategy (RPS) on event input and then extract modality-specific chracteristics via Modality-specific Representation Mechanism (MRM) , cooperating with two interactive modules, Motion Saliency Enhancer Module (MSEM) and Event Semantic Engraver Module (ESEM) for complementary modality interaction.

### 3.1 Robustness-Oriented Perturbation Strategy (RPS)

Event cameras generate an event when the logarithmic intensity change at a pixel exceeds a
contrast threshold $\theta\in\mathbb{R}_{+}$.
Formally, let the log-intensity increment at pixel coordinates $(x,y)$ and time $t$ be

$$ $\Delta\ell(x,y,t)=\log I_{t}(x,y)-\log I_{t-\Delta t}(x,y),$ (1) $$

where $I_{t}(x,y)$ denotes the intensity at time $t$, and $\Delta t$ is the time interval.
We decompose $\Delta\ell$ into a signal and a noise component:

$$ $\Delta\ell=S+N,$ (2) $$

where $S$ corresponds to true motion signals and $N$ accounts for noise.
An event is triggered when $|\,\Delta\ell\,|\geq\theta,p=\mathrm{sign}(\Delta\ell)$, which highlights a fundamental trade-off between sensity and noise.
When $S$ is weak, the events are easily dominated by noise $N$, making the stream highly susceptible to corruption, shown in Figure [1](https://arxiv.org/html/2509.05554v2#S1.F1)c and details in Appendix [A.2](https://arxiv.org/html/2509.05554v2#A1.SS2).

In practice, event noise originates from multiple physical sources (Jiang et al., 2024), such as photon shot noise and thermal or electronic fluctuations, which can be modeled as Poisson and Gaussian process.
To capture in a unified way, we represent the aggregated noise as a mixed distribution

$$ $N=N_{p}+N_{g},\qquad N_{p}\sim\mathrm{Poisson}(\lambda),\qquad N_{g}\sim\mathcal{N}(0,\sigma_{n}^{2}),$ (3) $$

where $N_{p}$ accounts for photon arrivals with rate $\lambda$, and $N_{g}$ represents circuit-induced perturbations with variance $\sigma_{n}^{2}$.
An event is incorrectly triggered when $|N_{p}+N_{g}|\geq\theta$.
The corresponding false positive rate (FPR) with $\mathbb{P}(\cdot)$ as the probability of an event is

$$ $\mathrm{FPR}(\theta)=\mathbb{P}\big(|N_{p}+N_{g}|\geq\theta\big),$ (4) $$

which decreases as $\theta$ increases.
When true motion $S$ is present, the true positive rate (TPR) is

$$ $\mathrm{TPR}(\theta\mid S)=\mathbb{P}\big(|S+N_{p}+N_{g}|\geq\theta\;\mid\;S\big).$ (5) $$

Although larger thresholds reduce $\mathrm{FPR}$, they also decrease $\mathrm{TPR}$ by discarding weak but informative events.
We quantify this by the under-reporting ratio (UR):

$$ $\mathrm{UR}(\theta\mid S)=1-\mathrm{TPR}(\theta\mid S),$ (6) $$

whose expectation over the distribution of $S$ defines the overall under-reporting:

$$ $\mathrm{UR}(\theta)=\mathbb{E}_{S}\big[\,\mathrm{UR}(\theta\mid S)\,\big].$ (7) $$

In summary, $\theta$ acts as a global control knob:
$\theta\uparrow\;\Rightarrow\;\mathrm{FPR}\downarrow,\;\mathrm{UR}\uparrow.$
This motivates our RPS, which explicitly reformulates event acquisition as a probabilistic triggering process.
At pixel $(x,y)$ and time $t$, whether an event survives is determined by the survival probability:

$$ $\pi_{t}(x,y)=\mathbb{P}\big(|S+N_{p}+N_{g}|\geq\theta\big),$ (8) $$

which encodes the likelihood that the log-intensity increment surpasses the contrast threshold.
Thus, the problem naturally reduces to a binary decision, an event either survives or is suppressed.

To emulate this mechanism during training, we adopt the voxel grid representation $\mathbf{D}\in\mathbb{R}^{T\times H\times W}$ of events  (Sun et al., 2022), where $T$ denotes the number of temporal bins.
At each slice $\tau\in\{1,\dots,T\}$, we stochastically thin the events according to their survival probability:

$$ $\widetilde{\mathbf{D}}_{\tau}=\mathbf{D}_{\tau}\odot\bm{\rho}_{\tau},\qquad\bm{\rho}_{\tau}(x,y)\sim\mathrm{Bernoulli}\!\big(\pi_{\tau}(x,y)\big),$ (9) $$

where $\odot$ denotes element-wise multiplication.
During training, we further use $\mathrm{UR}$ to control perturbation strength.
In detail, for each iteration we sample

$$ $\alpha\sim\mathcal{U}(\alpha_{\min},\alpha_{\max}),\qquad\frac{1}{HW}\sum_{x,y}\pi_{\tau}(x,y)\approx 1-\alpha,\quad\forall\tau,$ (10) $$

and draw $\bm{\rho}_{\tau}$ independently across temporal bins to mimic variability in thresholds and circuitry.
This generates a continuum of training regimes from mild to severe under-reporting, faithfully reflecting the physical mechanism of threshold-driven event triggering.
As verified in Tables [1](https://arxiv.org/html/2509.05554v2#S4.T1), [7](https://arxiv.org/html/2509.05554v2#A1.T7) and Figure [7](https://arxiv.org/html/2509.05554v2#S4.F7), our architecture-agnostic and parameter-free RPS substantially improves the robustness of RED, ensuring high-quality deblurring under diverse scenes.

### 3.2 Modality-specific Representation Mechanism (MRM)

Blurry images primarily encode high-level semantic context, whereas over-reporting events provide complementary motion priors but lack complete semantic information.
When both modalities are indiscriminatively extracted or naively processed together, the lack of disentangling can cause semantic and motion features to mix, reducing the clarity of the extracted representations.
Thus, we rethink effective event-based image deblurring with modality-specific representations, where MRM individually perform semantic-wise, motion-wise, and cross-modality representation.

#### Semantic-wise and Motion-wise Attention.

Given the intermediate features
$\mathbf{F}^{ei}_{\mathrm{I}}$ and $\mathbf{F}^{ei}_{\mathrm{E}}\in\mathbb{R}^{B\times N\times H\times W}$ from the image and event branches, where $B$ denotes the batch size and $N$ refers to the original mixed space.
To emphasize semantic understanding, we begin by disentangling the feature space. Using a $1{\times}1$ convolution followed by a depthwise $3{\times}3$ convolution, we obtain the query, key, and value tensors for semantic modeling:

$$ $\mathbf{Q}_{\text{sem}},\mathbf{K}_{\text{sem}},\mathbf{V}_{\text{sem}}\in\mathbb{R}^{B\times L\times C_{L}\times(THW)},$ (11) $$

where $N=C\times T$ represents the split between the channel and temporal dimensions, and $C=C_{L}\times L$ denotes the multi-head splitting, with $L$ being the number of heads.
For motion-specific modeling, tokens are reshaped as
$\mathbf{Q}_{\text{mot}},\mathbf{K}_{\text{mot}},\mathbf{V}_{\text{mot}}\in\mathbb{R}^{B\times L\times T_{L}\times(CHW)}$,
where $T=L\times T_{L}$.
Here, the splitting focuses on temporal indices, ensuring the attention explicitly captures motion continuity across time.
The corresponding semantic correlation and motion dependency are formulated as

$$ $\mathbf{A}_{\text{sem}}=\mathrm{Softmax}(\mathbf{Q}_{\text{sem}}\mathbf{K}_{\text{sem}}^{\top}),\quad\mathbf{A}_{\text{mot}}=\mathrm{Softmax}(\mathbf{Q}_{\text{mot}}\mathbf{K}_{\text{mot}}^{\top}).$ (12) $$

Then, the updated features are then computed as

$$ $\mathbf{F}^{ei}_{\mathrm{I}}=\mathbf{V}_{\text{sem}}\odot\mathbf{A}_{\text{sem}},\quad\mathbf{F}^{ei}_{\mathrm{E}}=\mathbf{V}_{\text{mot}}\odot\mathbf{A}_{\text{mot}}.$ (13) $$

In this way, semantic characteristics are enhanced in the image branch, while temporal motion
dependencies are emphasized in the event branch.

#### Cross-Modality Attention.

With the former modality-specific representation, a cross-modality interaction are designed to broadcast semantic understanding and motion changes.
First, query and key are derived from $\mathbf{F}^{ei}_{\mathrm{I}}$,
while value is taken from $\mathbf{F}^{ei}_{\mathrm{E}}$.
Here, spatio-temporal tokens are chosen, thus features from images attributes to delineating abundant semantic understanding.

$$ $\mathbf{A}_{I\rightarrow E}=\mathrm{Softmax}\!\left(\mathbf{Q}_{I}\mathbf{K}_{I}^{\top}\right),\qquad\widetilde{\mathbf{F}}_{\mathrm{E}}=\mathbf{V}_{E}\odot\mathbf{A}_{I\rightarrow E}.$ (14) $$

Second, query and key are derived from $\mathbf{F}^{ei}_{\mathrm{E}}$,
while value comes from $\mathbf{F}^{ei}_{\mathrm{I}}$.
Here, spatio-channel tokens are chosen, thus events guides the images to restore detailed structural distributions.

$$ $\mathbf{A}_{E\rightarrow I}=\mathrm{Softmax}\!\left(\mathbf{Q}_{E}\mathbf{K}_{E}^{\top}\right),\qquad\widetilde{\mathbf{F}}_{\mathrm{I}}=\mathbf{V}_{I}\odot\mathbf{A}_{E\rightarrow I}.$ (15) $$

Finally, two-mode outputs are concatenated to obtain the fused output $\mathbf{F}_{\text{out}}$.

In conclusion, MRM switches among semantic-wise, motion-wise, and cross-modality attentions, seperately serving as the core mechanism of image, event, and cross-modal modules.
By this way, MRM embody our RED with modality-specific representations and effective cross-modality fusion.

### 3.3 MSEM and ESEM

As illustrated in Figure [2](https://arxiv.org/html/2509.05554v2#S2.F2) and detailed structure in Figure [9](https://arxiv.org/html/2509.05554v2#A1.F9), MSEM and ESEM are integrated into the feature extraction stage, different from the cross-modality attention described earlier that operates after modality-specific disentanglement.
They establish early branch-to-branch interactions between the image and event pathways, enabling bidirectional transfer of motion priors and semantic characteristics to guide each branch with complementary features during representation learning.

MSEM highlights motion-sensitive structures from events and injects them into the image branch.
Starting from over-reporting events, we extract high-frequency components via a
downsample-then-subtract operation and depthwise convolutions.
These components are used to enhance the event features, which are then fused with the corresponding image features.
A motion-aware attention map is generated to refine the event representation, and the resulting fine-grained
motion-enhanced features are propagated into the next-stage image representation.

ESEM engraves high-level semantic understanding from the image branch into the event branch.
Specifically, a latent spatial feature is first extracted from image features via an image module and refined by channel-wise attention.
The resulting semantic embedding is fused with event features and processed by a dual-branch design, with a temporal attention to enhances event continuity while spatial attention to emphasize structural details.
The two outputs are concatenated to produce semantically enriched event features, which are propagated into the next-stage event representation.

Figure: Figure 3: Visualization in GoPro dataset. Besides, error maps are drawn to illustrate a comprehensive comparison of both localization and structure context.
Refer to caption: x3.png

## 4 Experiments

### 4.1 Datasets

We evaluate our method on both synthetic and real-world event-based deblurring datasets.
Specifically, we adopt the widely used GoPro dataset (Nah et al., 2017a), which provides pairs of blurry and sharp images synthesized by averaging high-speed frames.
GoPro contains 3,214 image pairs at a resolution of $1280\times 720$, including 2,103 pairs for training and 1,111 for testing.
To further assess the robustness, we leverage 412 blurry images of size $1632\times 1224$ from the HighREV dataset (Sun et al., 2023), along with their corresponding event streams.
Besides, six sequences from the REVD dataset (Kim et al., 2024) are adopted, which includes 1,359 blurry images captured in typical urban scenes with diverse motion patterns, such as ego-motion, object motion, and their combinations.

### 4.2 Implementation Details

In the training process, $256\times 256$ patches are cropped, and 400k iterations are conducted during the training process.
Besides, Equation [10](https://arxiv.org/html/2509.05554v2#S3.E10) is set as $\alpha\sim\mathcal{U}(\alpha_{\min}=0,\alpha_{\max}=0.2)$.
We employ the Adam optimizer with an initial learning rate of $2\times 10^{-4}$, $(\beta_{1},\beta_{2})=(0.9,0.999)$ and $\epsilon=10^{-8}$.
To maintain the training stability, a gradual warmup strategy in (Zamir et al., 2021a) is applied in our training process with a minimum learning rate of $1\times 10^{-6}$.
The Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index Metric (SSIM) are adopted as the evaluation metrics.

**Table 1: Comparisons on GoPro dataset. Metrics are PSNR ($\uparrow$) and SSIM ($\uparrow$). UR stands for the various under-reporting ratio on events. DSTN denotes image deblurring without event assistance.**
| UR | DSTN | EFNet | STCNet | TRMD | AHDINet | MAT | Ours |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 0 | 35.05 / 0.973 | 35.47 / 0.972 | 36.46 / 0.975 | 36.58 / 0.979 | 37.09 / 0.978 | 36.67 / 0.978 | 37.63 / 0.9802 |
| 0.05 | 35.05 / 0.973 | 35.25 / 0.971 | 35.93 / 0.973 | 35.77 / 0.976 | 35.78 / 0.973 | 35.77 / 0.974 | 37.55 / 0.9800 |
| 0.1 | 35.05 / 0.973 | 34.92 / 0.970 | 35.39 / 0.972 | 35.05 / 0.973 | 34.77 / 0.969 | 35.53 / 0.973 | 37.45 / 0.9797 |
| 0.15 | 35.05 / 0.973 | 34.52 / 0.968 | 34.84 / 0.970 | 34.42 / 0.970 | 33.90 / 0.964 | 35.20 / 0.972 | 37.34 / 0.9794 |
| 0.2 | 35.05 / 0.973 | 34.08 / 0.966 | 34.29 / 0.967 | 33.84 / 0.967 | 33.15 / 0.960 | 34.81 / 0.970 | 37.21 / 0.9790 |
| 0.3 | 35.05 / 0.973 | 33.16 / 0.960 | 33.15 / 0.962 | 32.88 / 0.961 | 31.81 / 0.951 | 33.79 / 0.965 | 36.89 / 0.9781 |

### 4.3 Comparable Results

Five state-of-the-art event-based image deblurring methods are chosen as fair and straight comparisons, including EFNet (Sun et al., 2022), STCNet (Yang et al., 2024), TRMD(Chen & Yu, 2024), AHDINet (Yang et al., 2025), and MAT (Xu et al., 2025). Besides, DSTN (Pan et al., 2023) as an image deblurring method, is adopted as a reference to verify the effectiveness of introducing events.

Figure: Figure 4: Performance comparisons. Our RED maintains a stable capablity to promote deblurring performance even under severe under-reporting events.
Refer to caption: x4.png

#### Comparable Results in GoPro:

Table [1](https://arxiv.org/html/2509.05554v2#S4.T1) reports quantitative comparisons on the GoPro dataset.
Across different under-reporting ratios (UR), RED consistently achieves the highest PSNR and SSIM.
As shown in Figure [4](https://arxiv.org/html/2509.05554v2#S4.F4), existing event-based methods exhibit a sharp performance drop as UR increases, indicating their inability to extract effective motion priors from over-reporting events.
This finding echoes our motivation: simply injecting event inputs without considering modality-specific characteristics renders models highly vulnerable to event corruption.
In contrast, RED maintains stable performance even at UR = 0.5, outperforming the image-only baseline.
This robustness is largely attributed to the diverse under-reporting patterns introduced by our RPS module, as well as the MRM, which disentangles modality-specific features and enables efficient cross-modality interactions.
Furthermore, visual results in Figure [3](https://arxiv.org/html/2509.05554v2#S3.F3) demonstrate that our method produces sharper details and cleaner textures.
More detailed numerical results and performance analysis are presented in Appendix [A.4](https://arxiv.org/html/2509.05554v2#A1.SS4), while additional qualitative results are provided in Appendix [A.6](https://arxiv.org/html/2509.05554v2#A1.SS6).

Figure: Figure 5: Visualization in HighREV dataset. Besides, error maps are drawn to illustrate a comprehensive comparison of both localization and structure context.
Refer to caption: x5.png

Figure: Figure 6: Visualization in REVD dataset. Besides, error maps are drawn to illustrate a comprehensive comparison of both localization and structure context.
Refer to caption: x6.png

#### Comparable Results in HighREV and REVD:

To further verify the generalization of event-based methods in practice, we conduct deblurring experiments on HighREV and REVD datasets with the former trained models on GoPro.
As shown in Tables [2(a)](https://arxiv.org/html/2509.05554v2#S4.T2.st1) and [2(b)](https://arxiv.org/html/2509.05554v2#S4.T2.st2), our method consistently achieves the highest PSNR and SSIM, demonstrating strong robustness across diverse scenes and motion patterns.
Besides, visual comparisons on HighREV dataset in Figure [5](https://arxiv.org/html/2509.05554v2#S4.F5) and challenging REVD dataset in Figure [6](https://arxiv.org/html/2509.05554v2#S4.F6) show Ours achieves clearer results.
Interestingly, we observe that PSNR of deblurred image in some methods is lower than that of blurry input.
This phenomenon indicates their failure to extract useful motion priors from real-world events for deblurring, further validating the motivation behind our approach.
More qualitative results are presented in Appendix [A.6](https://arxiv.org/html/2509.05554v2#A1.SS6).

**Table 2: Quantitative comparisons. Metrics are PSNR ($\uparrow$) and SSIM ($\uparrow$). Best results on HighREV (*Left*) and REVD (*Right*) datasets are in bold.**
| Methods | EFNet | STCNet | TRMD | MAT | AHDINet | Ours |
| --- | --- | --- | --- | --- | --- | --- |
| PSNR($\uparrow$) | 20.56 | 28.77 | 28.89 | 27.54 | 28.82 | 30.04 |
| SSIM($\uparrow$) | 0.808 | 0.869 | 0.892 | 0.922 | 0.857 | 0.929 |

### 4.4 Ablation Study

#### Investigation of RPS.

To evaluate the effectiveness of our RPS, we first validate RED and RED without RPS under different UR settings in Figure [7(a)](https://arxiv.org/html/2509.05554v2#S4.F7.sf1).
Then, we integrate RPS into existing event-based methods to further assess its efficiency, including Transformer-based MAT (Xu et al., 2025) in Figure [7(b)](https://arxiv.org/html/2509.05554v2#S4.F7.sf2) and CNN-based AHDINet (Yang et al., 2025) in Figure [7(c)](https://arxiv.org/html/2509.05554v2#S4.F7.sf3).
Finally, an overall comparison in Figure [7(d)](https://arxiv.org/html/2509.05554v2#S4.F7.sf4) is conducted on Ours, MAT with our RPS, and AHDINet with our RPS.

From Figure [7](https://arxiv.org/html/2509.05554v2#S4.F7), the following conclusions are dramn.
First, removing RPS from our framework leads to significant robustness drops.
Second, incorporating RPS into existing event-guided methods consistently improves their robustness, demonstrating the plug-and-play generality of RPS.
Finally, beyond the contribution of RPS, our method consistently achieves the best performance and robustness across all conditions, highlighting the importance of our modality-specific representation.
The detailed quantitative and qualitative results are presented in Appendix [A.5](https://arxiv.org/html/2509.05554v2#A1.SS5).

Figure: (a)
Refer to caption: x7.png

#### Investigation of MRM.

To assess the contribution of different components in MRM, we conduct a comprehensive ablation study by selectively replacing the semantic reasoning, motion-wise structural representation, and cross-modality attention mechanisms with a modality-agnostic self-attention module (Yuan et al., 2021), while keeping the feature dimensionality unchanged.
As shown in Table [3(a)](https://arxiv.org/html/2509.05554v2#S4.T3.st1), replacing all specialized attentions with generic self-attention causes a dramatic performance drop of 11.86 dB in PSNR, indicating that indiscriminately processing blurry images and events severely impairs effective feature extraction and interaction.
Replacing only the modality-specific attention in the image or event branch results in 2.64dB and 2.59dB drops, while a smaller but noticeable degradation of 0.49dB when replacing the cross-modality attention.
These findings collectively demonstrate that our MRM, not only captures the distinct semantic context and motion-sensitive areas, but also facilitates more effective and complementary feature fusion.

**Table 3: Ablation study. Metrics are PSNR ($\uparrow$) and SSIM ($\uparrow$). Best results are in bold.**
| Semantic | Motion | Cross | PSNR($\uparrow$) | SSIM($\uparrow$) |
| --- | --- | --- | --- | --- |
| ✗ | ✗ | ✗ | 25.77 | 0.864 |
| ✗ | ✓ | ✓ | 34.99 | 0.964 |
| ✓ | ✗ | ✓ | 35.04 | 0.966 |
| ✓ | ✓ | ✗ | 37.14 | 0.978 |
| ✓ | ✓ | ✓ | 37.63 | 0.980 |

#### Investigation of MSEM and ESEM.

We further investigate the contributions of two feature interaction modules in our RED.
As reported in Table [3(b)](https://arxiv.org/html/2509.05554v2#S4.T3.st2), each module individually improves PSNR and SSIM over the baseline.
The joint cooperation of MSEM and ESEM leads to the best performance, with a 0.85dB gain in PSNR over the baseline.
Besides, we observe that the performance gain from feature interaction strategies is less pronounced than that from modality-specific attention in Table [3(a)](https://arxiv.org/html/2509.05554v2#S4.T3.st1).
This also supports our hypothesis when deigning RED:
modality-specific feature encoding benefits to modality-agnostic representation and enables more effective cross-modality collaboration, ultimately leading to superior deblurring performance.

## 5 Conclusion

In this paper, we present RED, a robust and practical event-based deblurring framework that explicitly accounts for the under-reporting of event data caused by the trade-off between sensitivity and noise.
We introduce a RPS to embody RED with powerful and robust adaptability under different unknown scenario conditions.
To extract fine-grained motion priors from under-reporting events and prevent corrupted effect, a disentangled MRM is designed to explicitly model semantic, motion, and cross-modality correlations, allowing more effective extraction of modality-specific features and cross-modality fusion.
Building on these reliable features, two interactive modules are performed to delineate blurry image with robust motion-sensitive priors and complements incomplete event with holistic semantic context.
With the robust event representation and modality-specific disentangled representation, our RED delivers state-of-the-art deblurring performance and strong generalization.

## Ethics Statement

This work relies on publicly available datasets under their respective licenses. No new data involving human subjects were collected. All visualizations respect privacy. We confirm that our method and experiments do not raise additional ethical concerns

## Reproducibility Statement

We use publicly accessible datasets, GoPro (Nah et al., 2017a), HighREV (Kim et al., 2024), and REVD (Sun et al., 2023). After the blind review period, we will release our training/inference scripts, configuration files, and model checkpoints, together with step-by-step instructions and evaluation protocols to fully reproduce all tables and figures.

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## Appendix A Appendix

In the appendix, we first provide the LLM usage statement.
Then, we analyze the impact of low and high DVS thresholds on the performance of event-based deblurring methods, and then illustrate the process we focus on extracting effective motion priors from more clean events under high DVS threshold.
Subsequently, we illustrate the detailed structure of our modality-interaction module: MSEM and ESEM.
Next, we provide a more detailed analysis between the model performance and efficiency.
Finally, more visualizations on simulated and real-world datasets are presented.

Appendix is organized as follows:

### A.1 Use of LLMs

The LLMs are used only for language polishing and editing of the manuscript text.

### A.2 Robustness analysis of existing methods under low and high DVS throshold.

Here, we investigate the performance of existing event-based deblurring methods with events in low DVS threshold and high DVS threshold in various practical scenes.
Event cameras generate asynchronous events when the change of log-intensity at a pixel exceeds a contrast threshold $\theta$. As discussed in the main paper, this mechanism inherently involves a trade-off:

1) Low threshold ($\theta\downarrow$): The sensor becomes highly sensitive but produces many spurious events dominated by noise.

2) High threshold ($\theta\uparrow$): Noise can be suppressed but weak and informative events fall below the threshold, resulting in under-reporting.

Thus, the quality of the event stream depends critically on $\theta$, which controls the balance between noise contamination and information loss.
Comprehensive robustness verification experiments have been conducted on existing deblurring methods, which are illustrated in Figure [8(a)](https://arxiv.org/html/2509.05554v2#A1.F8.sf1) and Figure [8(b)](https://arxiv.org/html/2509.05554v2#A1.F8.sf2).

#### Robustness of Existing Methods under Noise (Low Threshold).

To study robustness against noise, we simulate different noise ratios under low threshold settings.
Table [4](https://arxiv.org/html/2509.05554v2#A1.T4) reports PSNR/SSIM values for state-of-the-art event-based deblurring methods and the image-only baseline (DSTN).
It can be observed that:

1) When the noise ratio is small (0.05), most event-based methods already degrade notably compared to the clean case.

2) As the noise ratio increases ($\geq 0.15$), existing methods collapse severely, falling even below the image-only baseline.

Figure: (a) Noise robustness curve.
Refer to caption: x11.png

**Table 4: Performance under low DVS theshold. Robustness of existing event-based deblurring methods under different noise perturbance ratios. DSTN denotes image-only deblurring baseline.**
| Noise Ratio | DSTN (no event) | EFNet | STCNet | TRMD | AHDINet | MAT |
| --- | --- | --- | --- | --- | --- | --- |
| 0 | 35.05 / 0.973 | 35.47 / 0.972 | 36.46 / 0.975 | 36.58 / 0.979 | 37.09 / 0.978 | 36.67 / 0.978 |
| 0.05 | 35.05 / 0.973 | 24.26 / 0.851 | 35.24 / 0.958 | 36.48 / 0.978 | 35.45 / 0.966 | 28.81 / 0.892 |
| 0.1 | 35.05 / 0.973 | 22.01 / 0.838 | 34.31 / 0.959 | 35.62 / 0.966 | 33.52 / 0.944 | 24.09 / 0.720 |
| 0.15 | 35.05 / 0.973 | 20.45 / 0.818 | 33.53 / 0.953 | 33.95 / 0.943 | 32.41 / 0.926 | 20.16 / 0.526 |
| 0.2 | 35.05 / 0.973 | 19.92 / 0.806 | 32.74 / 0.945 | 32.46 / 0.920 | 31.55 / 0.911 | 16.39 / 0.344 |
| 0.3 | 35.05 / 0.973 | 19.92 / 0.785 | 31.35 / 0.925 | 30.02 / 0.871 | 30.26 / 0.881 | 15.07 / 0.281 |

**Table 5: Performance under high DVS theshold. Robustness of existing event-based deblurring methods under different under-reporting ratios. DSTN denotes image-only deblurring baseline.**
| UR | DSTN (no event) | EFNet | STCNet | TRMD | AHDINet | MAT |
| --- | --- | --- | --- | --- | --- | --- |
| 0 | 35.05 / 0.973 | 35.47 / 0.972 | 36.46 / 0.975 | 36.58 / 0.979 | 37.09 / 0.978 | 36.67 / 0.978 |
| 0.05 | 35.05 / 0.973 | 35.25 / 0.971 | 35.93 / 0.973 | 35.77 / 0.976 | 35.78 / 0.973 | 35.77 / 0.974 |
| 0.1 | 35.05 / 0.973 | 34.92 / 0.970 | 35.39 / 0.972 | 35.05 / 0.973 | 34.77 / 0.969 | 35.53 / 0.973 |
| 0.15 | 35.05 / 0.973 | 34.52 / 0.968 | 34.84 / 0.970 | 34.42 / 0.970 | 33.90 / 0.964 | 35.20 / 0.972 |
| 0.2 | 35.05 / 0.973 | 34.08 / 0.966 | 34.29 / 0.967 | 33.84 / 0.967 | 33.15 / 0.960 | 34.81 / 0.970 |
| 0.25 | 35.05 / 0.973 | 33.62 / 0.963 | 33.72 / 0.965 | 33.34 / 0.964 | 32.46 / 0.956 | 34.33 / 0.968 |
| 0.3 | 35.05 / 0.973 | 33.16 / 0.960 | 33.15 / 0.962 | 32.88 / 0.961 | 31.81 / 0.951 | 33.79 / 0.965 |
| 0.35 | 35.05 / 0.973 | 32.67 / 0.957 | 32.59 / 0.959 | 32.46 / 0.958 | 31.21 / 0.946 | 33.18 / 0.962 |
| 0.4 | 35.05 / 0.973 | 32.16 / 0.954 | 32.03 / 0.956 | 32.08 / 0.954 | 30.66 / 0.941 | 32.53 / 0.959 |
| 0.45 | 35.05 / 0.973 | 31.62 / 0.949 | 31.49 / 0.953 | 31.73 / 0.951 | 30.13 / 0.936 | 31.90 / 0.955 |
| 0.5 | 35.05 / 0.973 | 31.03 / 0.944 | 30.96 / 0.949 | 31.41 / 0.948 | 29.64 / 0.930 | 31.24 / 0.951 |

#### Robustness of Existing Methods under Under-Reporting (High Threshold).

Next, we evaluate robustness to incomplete events caused by high thresholds. Table [5](https://arxiv.org/html/2509.05554v2#A1.T5) reports PSNR/SSIM under different under-reporting ratios (UR).
It can be observed that:

1) Performance degrades progressively as UR increases. While mild under-reporting (UR $\leq 0.1$) is tolerable, all event-based methods show sharp declines once UR $\geq 0.2$.

2) Notably, the performance eventually drops below the image-only baseline (DSTN), which contradicts the common assumption that events always provide positive contributions.

#### Key Insight and Motivation of RPS.

The above analysis of quantitative results in Tables [4](https://arxiv.org/html/2509.05554v2#A1.T4), [5](https://arxiv.org/html/2509.05554v2#A1.T5) and corresponding qualitative results in Figure [8](https://arxiv.org/html/2509.05554v2#A1.F8), it can be deduced that accurate motion information is more critical for deblurring than merely noisy events.
This motivates our RPS: instead of relying on idealized events, RPS explicitly exposes the model to threshold-induced disruptions during training, enabling RED to adapt to diverse real-world scenes.

### A.3 Detailed Structure of our MSEM and ESEM.

In our whole RED framework in Figure [2](https://arxiv.org/html/2509.05554v2#S2.F2), Motion Saliency Enhancer Module (MSEM) and Event Semantic Engraver Module (ESEM) are designed to achieve coadjutant interactions.
As illustrated in Figure [9](https://arxiv.org/html/2509.05554v2#A1.F9), MSEM aims to highlight motion-sensitive structures from events and inject them into the image branch.
Given the perturbed event input $\widetilde{\mathbf{D}}$, we first derive high-frequency components
$\mathbf{S}\in\mathbb{R}^{B\times N\times H\times W}$ via a downsample-then-subtract
operation followed by depthwise convolutions.

Figure: Figure 9: Framework of our proposed Motion Saliency Enhancer Module (MSEM) and Event Semantic Engraver Module (ESEM).
Refer to caption: x13.png

The motion-enhanced event feature is then initialized as:

$$ $\widehat{\mathbf{F}}^{(1)}_{\mathrm{E}}=\mathbf{F}^{(i)}_{\mathrm{E}}\odot\mathbf{S}+\mathbf{F}^{(i)}_{\mathrm{E}},$ $$

where $\odot$ denotes element-wise multiplication.
To transfer motion priors into the image branch,
$\widehat{\mathbf{F}}^{(1)}_{\mathrm{E}}$ is concatenated with $\mathbf{F}^{(i)}_{\mathrm{I}}$
and processed by a $1{\times}1$ convolution followed by two depthwise convolutions,
yielding a coarse fused representation $\mathbf{F}_{\text{mix}}$.
From $\mathbf{F}_{\text{mix}}$, we compute a motion-aware attention map
$\bm{\gamma}\in\mathbb{R}^{B\times N\times H\times W}$.
Using this map, the event feature is refined as:

$$ $\widehat{\mathbf{F}}^{(2)}_{\mathrm{E}}=\bm{\gamma}\odot\widehat{\mathbf{F}}^{(1)}_{\mathrm{E}}.$ $$

Finally, $\mathbf{F}_{\text{mix}}$ is concatenated with $\widehat{\mathbf{F}}^{(2)}_{\mathrm{E}}$
to produce a fine-grained motion-enhanced feature $\mathbf{F}_{\mathrm{E2I}}$,
which is then propagated to update the next-stage image representation $\mathbf{F}^{(i+1)}_{\mathrm{I}}$.

Complementary to MSEM, ESEM engraves high-level semantic representations from the image branch into the motion branch.
Given $\mathbf{F}^{(i)}_{\mathrm{I}}$,
we first extract a latent spatial feature $\widehat{\mathbf{F}}^{(i)}_{\mathrm{I}}$
through an image encoder.
A channel-wise attention then generates correlation weights $\bm{\beta}$, and
the semantic embedding is formulated as: $\mathbf{F}^{(i)}_{\mathrm{sem}}=\bm{\beta}\odot\widehat{\mathbf{F}}^{(i)}_{\mathrm{I}}.$
To enhance event features with this semantic prior,
$\mathbf{F}^{(i)}_{\mathrm{sem}}$ is fused with $\mathbf{F}^{(i)}_{\mathrm{E}}$
to form $\widehat{\mathbf{F}}_{\text{mix}}$.
We split $\widehat{\mathbf{F}}_{\text{mix}}$ along the channel dimension into
$\widehat{\mathbf{F}}^{\mathrm{E}}_{\text{mix}}$ and
$\widehat{\mathbf{F}}^{\mathrm{sem}}_{\text{mix}}$,
which are individually processed by a dual-branch attention module:
temporal attention on $\widehat{\mathbf{F}}^{\mathrm{E}}_{\text{mix}}$ produces
an event-aware output $\overline{\mathbf{F}}^{(i)}_{\mathrm{E}}$,
while spatial attention on $\widehat{\mathbf{F}}^{\mathrm{sem}}_{\text{mix}}$
produces a spatially modulated feature $\overline{\mathbf{F}}^{(i)}_{\mathrm{sem}}$.
The two outputs are concatenated to yield the final semantically enriched
event feature $\mathbf{F}_{\mathrm{I2E}}$,
which is forwarded to the next-stage event representation
$\mathbf{F}^{(i+1)}_{\mathrm{E}}$.

### A.4 Parameters and FLOPs.

As illustrated in Table [6](https://arxiv.org/html/2509.05554v2#A1.T6), we present a comparison between model complexity and performance, with a fixed spatial resolution of $360\times 640$.
In particular, EFNet, STCNet, and AHDINet adopt CNN-based architectures, enhanced by several cross-attention mechanisms to fuse event and blurry image features. These models generally maintain moderate parameter counts and computational costs, but varying performance under increasing UR.
For example, while AHDINet achieves strong results under clean events, its performance drops significantly as UR increases, indicating limited temporal robustness.

On the other hand, TRMD, MAT, and our proposed method employ Transformer-based architectures as the backbone, resulting in higher parameter counts compared to the CNN-based approaches.
This is expected, as Transformer modules are typically more expressive but computationally intensive.
Notably, our method achieves the best overall performance across all conditions, attaining 37.63 dB PSNR at UR = 0 and maintaining 35.63 dB even at UR = 0.5, while keeping the parameter count lower than MAT and comparable to TRMD.

These results highlight the trade-off between efficiency and robustness.
While CNN-based methods are lightweight, their temporal generalization tends to be limited.
Despite being computationally heavier, Transformer-based designs provide superior robustness to event disturbances.
Our proposed method achieves a favorable balance between these two aspects, validating the effectiveness of its architecture in handling diverse motion scenes.

**Table 6: Comparison between model complexity and performance on GoPro dataset. Metrics are PSNR ($\uparrow$) and SSIM ($\uparrow$). UR stands for the various under-reporting ratio on events. DSTN denotes image deblurring without event assistance.**
| UR | DSTN | EFNet | STCNet | TRMD | AHDINet | MAT | Ours |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Params(M) | 7.45 | 7.73 | 8.54 | 19.26 | 10.60 | 20.73 | 19.20 |
| FLOPs(G) | 168.29 | 379.43 | 669.65 | 113.02 | 288.04 | 749.91 | 637.45 |
| 0 | 35.05 / 0.973 | 35.47 / 0.972 | 36.46 / 0.975 | 36.58 / 0.979 | 37.09 / 0.978 | 36.67 / 0.978 | 37.63 / 0.9802 |
| 0.05 | 35.05 / 0.973 | 35.25 / 0.971 | 35.93 / 0.973 | 35.77 / 0.976 | 35.78 / 0.973 | 35.77 / 0.974 | 37.55 / 0.9800 |
| 0.1 | 35.05 / 0.973 | 34.92 / 0.970 | 35.39 / 0.972 | 35.05 / 0.973 | 34.77 / 0.969 | 35.53 / 0.973 | 37.45 / 0.9797 |
| 0.15 | 35.05 / 0.973 | 34.52 / 0.968 | 34.84 / 0.970 | 34.42 / 0.970 | 33.90 / 0.964 | 35.20 / 0.972 | 37.34 / 0.9794 |
| 0.2 | 35.05 / 0.973 | 34.08 / 0.966 | 34.29 / 0.967 | 33.84 / 0.967 | 33.15 / 0.960 | 34.81 / 0.970 | 37.21 / 0.9790 |
| 0.25 | 35.05 / 0.973 | 33.62 / 0.963 | 33.72 / 0.965 | 33.34 / 0.964 | 32.46 / 0.956 | 34.33 / 0.968 | 37.06 / 0.9786 |
| 0.3 | 35.05 / 0.973 | 33.16 / 0.960 | 33.15 / 0.962 | 32.88 / 0.961 | 31.81 / 0.951 | 33.79 / 0.965 | 36.89 / 0.9781 |
| 0.35 | 35.05 / 0.973 | 32.67 / 0.957 | 32.59 / 0.959 | 32.46 / 0.958 | 31.21 / 0.946 | 33.18 / 0.962 | 36.67 / 0.9774 |
| 0.4 | 35.05 / 0.973 | 32.16 / 0.954 | 32.03 / 0.956 | 32.08 / 0.954 | 30.66 / 0.941 | 32.53 / 0.959 | 36.40 / 0.9766 |
| 0.45 | 35.05 / 0.973 | 31.62 / 0.949 | 31.49 / 0.953 | 31.73 / 0.951 | 30.13 / 0.936 | 31.90 / 0.955 | 36.07 / 0.9755 |
| 0.5 | 35.05 / 0.973 | 31.03 / 0.944 | 30.96 / 0.949 | 31.41 / 0.948 | 29.64 / 0.930 | 31.24 / 0.951 | 35.63 / 0.9741 |

Figure: (a) Ours v.s. Ours w/o RPS.
Refer to caption: x14.png

**Table 7: Ablation study on RPS. Metrics are PSNR ($\uparrow$) and SSIM ($\uparrow$).**
| UR | 0.05 | 0.1 | 0.15 | 0.2 | 0.25 | 0.3 |
| --- | --- | --- | --- | --- | --- | --- |
| DSTN (no event) | 35.05 / 0.973 | 35.05 / 0.973 | 35.05 / 0.973 | 35.05 / 0.973 | 35.05 / 0.973 | 35.05 / 0.973 |
| MAT | 35.77 / 0.974 | 35.53 / 0.973 | 35.20 / 0.972 | 34.81 / 0.970 | 34.33 / 0.9681 | 33.79 / 0.9654 |
| MAT + RPS | 35.99 / 0.9754 | 35.92 / 0.9751 | 35.84 / 0.9748 | 35.75 / 0.9744 | 35.63 / 0.9739 | 35.48 / 0.9734 |
| AHDINet | 35.78 / 0.973 | 34.77 / 0.9687 | 33.90 / 0.964 | 33.15 / 0.960 | 32.46 / 0.956 | 31.81 / 0.951 |
| AHDINet + RPS | 36.63 / 0.9753 | 36.53 / 0.9750 | 36.42 / 0.9746 | 36.28 / 0.9740 | 36.11 / 0.9734 | 35.90 / 0.9726 |
| Ours wo RPS | 36.77 / 0.9773 | 36.28 / 0.9759 | 35.81 / 0.9745 | 35.35 / 0.9729 | 34.90 / 0.9713 | 34.47 / 0.9697 |
| Ours | 37.55 / 0.9800 | 37.45 / 0.9797 | 37.34 / 0.9794 | 37.21 / 0.9790 | 37.06 / 0.9786 | 36.89 / 0.9781 |

### A.5 Quantitative and qualitative results to verify the efficiency of our RPS.

To evaluate the effectiveness of our RPS, we first validate RED and RED without RPS under different UR settings.
In detail, quantitative results are listed in Table [7](https://arxiv.org/html/2509.05554v2#A1.T7) and corresponding qualitative results are shown in Figure [10](https://arxiv.org/html/2509.05554v2#A1.F10).
Then, we implement our RPS on existing event-based methods to further verify the efficiency, including Transformer-based MAT (Xu et al., 2025) in Figure [10(b)](https://arxiv.org/html/2509.05554v2#A1.F10.sf2) and CNN-based AHDINet (Yang et al., 2025) in Figure [10(c)](https://arxiv.org/html/2509.05554v2#A1.F10.sf3).
Besides, MAT and AHDINet are individually the third-best and second-best deblurring methods when UR = 0.
Finally, an overall comparison in Figure [10(d)](https://arxiv.org/html/2509.05554v2#A1.F10.sf4) is conducted on Ours, MAT with our RPS, and AHDINet with our RPS.

From Figure [10(d)](https://arxiv.org/html/2509.05554v2#A1.F10.sf4), we draw the following conclusions.
First, removing RPS from our framework leads to significant robustness drops.
Second, integrating our RPS into existing event-guided methods consistently enhances their robustness, demonstrating the plug-and-play generality of RPS.
Finally, beyond the contribution of RPS, our method consistently achieves the best performance and robustness across all conditions, highlighting the importance of our modality-specific representation.

### A.6 More qualitative results on GoPro, HighREV, and REVD datasets.

To provide an intuitive visualization of the deblurring performance, we present comprehensive qualitative comparisons on GoPro, HighREV, and REVD datasets.
Specifically, we compare the blurry inputs and the deblurred outputs by various state-of-the-art (SOTA) methods, including our proposed RED.
In addition, error maps are provided to clearly illustrate the accuracy of both spatial localization and texture restoration.

For the GoPro dataset, qualitative results are shown in Figures [11](https://arxiv.org/html/2509.05554v2#A1.F11), [12](https://arxiv.org/html/2509.05554v2#A1.F12), and [13](https://arxiv.org/html/2509.05554v2#A1.F13).
These examples cover diverse scenes such as foreground objects, background textures, and textual regions, allowing a comprehensive examination of the deblurring performance.
The results demonstrate that our method consistently produces sharper edges, cleaner backgrounds, and lower error levels compared to competing methods, highlighting its superior capability in precise detail reconstruction.

For the unseen real-world scenarios in the HighREV and REVD datasets, qualitative comparisons are presented in Figures [14](https://arxiv.org/html/2509.05554v2#A1.F14) and [15](https://arxiv.org/html/2509.05554v2#A1.F15).
These datasets contain challenging cases with degraded or noisy event data, where robustness becomes crucial.
Interestingly, we observe that for certain existing methods, the deblurred outputs can be even worse than the original blurry inputs, as reflected by lower visual quality and higher error levels.

As shown in Figure [14](https://arxiv.org/html/2509.05554v2#A1.F14), EFNet and MAT almost fail to perform meaningful deblurring, producing results that are close to the original blurry inputs.
In contrast, STCNet, AHDINet, and TRMD are able to restore coarse object contours, but their outputs still suffer from structural distortions, such as misaligned edges and inconsistent textures.
For the REVD dataset in Figure [15](https://arxiv.org/html/2509.05554v2#A1.F15), we observe different failure patterns: EFNet and MAT exhibit over-sharpening, generating noticeable artifacts and non-existent edges.
It is worth noted that STCNet produces results that are visually even worse than the blurry inputs, likely because its design of three-frame rolling input introduces a higher degree of unseen and challenging events, thereby degrading the output quality.
TRMD performs similarly to the blurry images, with only subtle pixel-level improvements that are barely visible to the naked eye, primarily around the right edge of the wall in Figure [15](https://arxiv.org/html/2509.05554v2#A1.F15).
Among all methods, AHDINet and Ours achieve relatively better deblurring performance, with Ours demonstrating sharper edges and more faithful details, such as clearer hair strands and finer clothing textures.

The above observations suggest that the event information, without modality-specific representation, may fail to provide positive guidance and may instead introduce artifacts.
With our RPS and modality-specific MRM, our approach maintains superior deblurring performance, producing clearer edges and more faithful texture details across diverse and challenging conditions.

Figure: Figure 11: Visualization of an example of background texture in GoPro dataset. Besides, error maps are drawn to illustrate a comprehensive comparison of both localization and structure context.
Refer to caption: x18.png

Figure: Figure 12: Visualization of an example of textual region in GoPro dataset. Besides, error maps are drawn to illustrate a comprehensive comparison of both localization and structure context.
Refer to caption: x19.png

Figure: Figure 13: Visualization of an example of foreground object in GoPro dataset. Besides, error maps are drawn to illustrate a comprehensive comparison of both localization and structure context.
Refer to caption: x20.png

Figure: Figure 14: Visualization in HighREV dataset. Besides, error maps are drawn to illustrate a comprehensive comparison of both localization and structure context. We can infer that: EFNet and MAT almost fail to produces results that are close to the original blurry inputs. In contrast, STCNet, AHDINet, and TRMD are able to restore coarse object contours, but suffer from misaligned edges and inconsistent textures. Relatively, Ours achieve a clearer and sharp result.
Refer to caption: x21.png

Figure: Figure 15: Visualization in REVD dataset. Besides, error maps are drawn to illustrate a comprehensive comparison of both localization and structure context. We observe that: EFNet and MAT tend to over-sharpen. STCNet produces results that are visually even worse than the blurry inputs since the three-frame rolling strategy causes a higher degree of events. TRMD performs similarly to the blurry images, with only subtle pixel-level improvements, primarily around the right edge of the wall. Among all methods, AHDINet and Ours achieve relatively better deblurring performance, with Ours demonstrating clearer hair strands and finer clothing textures.
Refer to caption: x22.png

## Peer Reviews

confidence: 3
contribution: 3
initial_rating: 4
presentation: 3
questions: 1. What is the performance of RED in high-noise scenarios with low DVS thresholds? Can supplementary comparative experiments under different noise intensities be provided to illustrate its advantages in noise robustness compared to methods such as EFNet and AHDINet?

2. Can the feature fusion weights of MSEM and ESEM be dynamically adjusted according to the degree of event under-reporting and image blur intensity? How adaptable is MRM to different motion types (rigid/non-rigid), and is targeted optimization required?
soundness: 3
strengths: 1.This paper systematically addresses the "noise-under-reporting" trade-off of event cameras. The RPS strategy innovatively simulates the physical mechanism of real event acquisition, the MRM realizes the disentanglement of modal features, breaking through the limitation of mixed features in existing methods, and the design of bidirectional interaction modules is highly targeted.
2. The experimental design is comprehensive, covering multiple synthetic and real-world datasets, verifying the method's robustness under different under-reporting ratios. Ablation experiments detailedly validate the necessity of each component including RPS, MRM, and MSEM/ESEM. The plug-and-play generality of RPS is verified by integrating it into other models, with highly credible conclusions.
3. The paper has a coherent structure, progressing layer by layer from problem analysis, method design to experimental verification. Framework diagrams and performance curves intuitively show the method flow and advantages. Technical details (such as the probabilistic modeling of RPS and the attention mechanism of MRM) are elaborated in detail, and the literature review is comprehensive, facilitating the understanding of the research background.
summary: This paper addresses the unstable deblurring performance caused by the "noise-under-reporting" trade-off of event cameras, proposing a Robust Event-guided Deblurring (RED) network. The core innovations include: designing a Robustness-Oriented Perturbation Strategy (RPS) to simulate event under-reporting patterns under different DVS thresholds, enhancing the model's adaptability to real-world scenarios; proposing a Modality-specific Representation Mechanism (MRM) to disentangle image semantic and event motion features, avoiding mixed representations; introducing two bidirectional interaction modules (MSEM and ESEM) to achieve complementary fusion of motion priors and semantic information. Experiments show that RED achieves state-of-the-art performance on both synthetic and real-world datasets, and RPS can be used as a plug-in to improve the robustness of other models. This method effectively solves the core pain point of event-guided motion deblurring, providing a robust and practical solution for deblurring in high-dynamic scenarios.
weaknesses: 1. Suboptimal computational efficiency: The parameter count (19.2M) and computational complexity (637.45G FLOPs) of RED are relatively high. Compared with lightweight models (e.g., EFNet: 7.73M parameters, 379.43G FLOPs), there is a lack of efficiency-performance trade-off analysis.
Insufficient depth of modal interaction mechanism: The feature fusion methods of MSEM and ESEM are relatively simple (such as element-wise multiplication and concatenation), and do not consider the dynamic adaptation between the degree of event under-reporting and image blur intensity. The cross-modal attention of MRM does not distinguish the differential adaptation of different motion types (e.g., rigid body motion, non-rigid body motion).
2.Incomplete coverage of extreme scenarios: Experiments do not involve extreme scenarios such as complete event loss, coexistence of strong noise and high under-reporting, and ultra-high-definition images (e.g., 4K). The adaptability of the method to different event camera models (different DVS threshold characteristics) is not evaluated.
3. Limited depth of core innovation: "Perturbation training to improve robustness" and "modality-specific feature extraction" are already mature ideas in the fields of image restoration and cross-modal fusion. The innovation of RED is more about the adaptive combination of existing ideas in event-guided deblurring tasks, lacking breakthrough paradigm innovation.

---

confidence: 4
contribution: 3
initial_rating: 4
presentation: 2
questions: As listed in the "Weaknesses".
soundness: 3
strengths: 1. The topic of handling under-reporting events in various DVS thresholds is an interesting topic for event-based deblurring. Bad event streams may indeed influence the fusion of two modalities.
2. It seems that the method can generalize better than others under high under-reporting ratio.
3. The figures and tables in the paper are well-organized.
summary: The paper deals with the issue of under-reporting of events for event-based motion deblurring task, and proposes a network named RED. The paper introduces Robustness-Oriented Perturbation Strategy (RPS) to enhance the robustness and adaptability of RED to real-world conditions. A Modality-specific Representation Mechanism(MRM) is designed to explicitly model semantic understanding, motion priors, and cross-modality correlations from blurry images and events.  Two interactive modules MSEM/ESEM are presented to enhance motion-sensitive areas in blurry images and inject semantic context into under-reporting event representations.
weaknesses: 1. The structure of the manuscripts is not good. Section 3.3 is too short to introduce the proposed modules. It appears that these two modules may have been combined in a way that gives the impression of being designed primarily to fulfill the workload requirement, rather than for a clear technical motivation.
2. The novelty of the MRM, as well as MSEM and ESEM, is not satisfying. The attention operations in the MRM are common modules in the field of the Transformer. Besides, there is no need for the full names of these modules to be so complicated.
3. The results in Figure 6 are not satisfying. The RED brings no visual improvement compared to other methods on this figure.
4. In real scenarios, are there situations when the under-reporting issue such high as the paper's experiments conducted?

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: How much computational overhead (in FLOPs or runtime) does RPS introduce during training and inference?

Could the RPS concept be extended to handle other sensor degradations, such as background noise or temporal jitter?

How independent are the features extracted by MSEM and ESEM? Have you visualized their activation maps to confirm disentanglement?

Can the authors provide more intuition or theoretical justification on how dynamic threshold perturbation leads to feature stability?

Would combining RPS with other noise-injection or adversarial training methods yield further robustness gains?
soundness: 4
strengths: The RPS module introduces a practical, model-agnostic way to simulate and handle event dropouts, effectively bridging the gap between synthetic and real-world noise conditions.

RPS can be easily integrated into other frameworks (e.g., MAT, AHDINet) and consistently improves their performance without architectural redesign.

The disentanglement design via MRM, combined with MSEM and ESEM, leads to well-structured multimodal representation and enhanced reconstruction fidelity.

Ablation and cross-model experiments rigorously validate the contribution of each component, demonstrating robustness across under-reporting ratios.

The model achieves superior PSNR/SSIM, especially under high dropout rates (>0.2).
summary: This paper introduces a robustness-oriented perturbation strategy (RPS) for event-based vision networks, aiming to improve their resilience under event under-reporting or dropout conditions. RPS dynamically simulates changes in the DVS output threshold to enable the network to adapt to diverse dropout patterns.

To further address feature unreliability caused by indiscriminate fusion or the absence of weak events, the authors propose a modality-specific representation mechanism (MRM) that disentangles semantic, motion, and cross-modality features. Two specialized submodules enable “coadjutant interactions”:

1. Motion Saliency Enhancer Module (MSEM) – strengthens motion-related spatial details often lost in blur.

2. Event Semantic Engraver Module (ESEM) – transforms semantic cues from images into deep event embeddings, mitigating semantic degradation caused by sparse events.

The paper also provides a probabilistic analysis of noise aggregation, modeling photon arrival with a Poisson process and circuit noise as Gaussian. Through comprehensive ablation and cross-model studies on several benchmarks, the proposed approach shows clear improvements in PSNR and SSIM, confirming both robustness and generalization.
weaknesses: Computational Cost Not Reported – The extra cost introduced by RPS (e.g., FLOPs, inference delay) is not discussed, which is crucial for real-time applications.

Limited Perturbation Scope – The experiments focus solely on under-reporting; robustness against other real-world degradations (e.g., motion blur, temporal jitter, or sensor noise) is untested.

Weak Theoretical Analysis – The mechanism by which RPS and MRM improve robustness lacks a formal mathematical explanation or interpretability study.

Partial Feature Disentanglement – Although semantic and motion features are decoupled in design, MSEM and ESEM remain interdependent through attention coupling, suggesting only dominant (not complete) disentanglement.

---

confidence: 3
contribution: 2
initial_rating: 6
presentation: 3
questions: Please see weakness.
soundness: 2
strengths: 1, It proposes a robust event-guided motion deblurring framework, which is trained with various DVS thresholds.

2, It disentangles modality-specific representation for image and event data. These two modalities interacts and benefits each other with two modules: one for improving spatial details in images and the other one for enhancing semantic information in event encoding.
summary: Considering the trade-off between noisy fake event and under-reporting of events, this paper proposes a robust event-guided deblurring network with modality-specific disentangled representation. It improves the network robustness to different DVS thresholds (diverse under-reporting patterns) with a perturbation strategy. Besides, it presents the blurry image and partially disrupted event data with modality-specific representations. It also introduces two interactive modules to enhance motion-sensitive areas in blurry images.
weaknesses: Overall, this paper is well-written and its designs are validated with various experiments. However, I have some concerns in terms of the core contributions.


1, The perturbation strategy looks like a data augmentation trick.

2, The novelty of disentangleed image and event representations is arguable. Previous methods already follow the three steps of image feature extraction, event feature extraction and cross-module aggregation.

3, The cross modality attention is the common cross attention. In addition, it is strange to compute the query and key from one modality, and obtain the value from another modality. Generally, key and value should come from one modality.

4, The abstract could be improved with more details on the modality-specific representations.


Decision: Reject