EvFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
Authors: Lin Zhu
Xiantao Ma
Xiao Wang
Lizhi Wang
Hua Huang
Venue: ICML 2025 poster (2025)
Keywords: Event-based reconstruction
Primary area: applications->computer_vision

Abstract: Event cameras are innovative sensors that capture brightness changes as asynchronous events rather than traditional intensity frames. These cameras offer substantial advantages over conventional cameras, including high temporal resolution, high dynamic range, and the elimination of motion blur. However, defocus blur, a common image quality degradation resulting from out-of-focus lenses, complicates the challenge of event-based imaging. Due to the unique imaging mechanism of event cameras, existing focusing algorithms struggle to operate efficiently on sparse event data. In this work, we propose EvFocus, a novel architecture designed to reconstruct sharp images from defocus event streams for the first time. Our work includes the development of an event-based out-of-focus camera model and a simulator to generate realistic defocus event streams for robust training and testing. EvDefous integrates a temporal information encoder, a blur-aware two-branch decoder, and a reconstruction and re-defocus module to effectively learn and correct defocus blur. Extensive experiments on both simulated and real-world datasets demonstrate that EvFocus outperforms existing methods across varying lighting conditions and blur sizes, proving its robustness and practical applicability in event-based defocus imaging.

EvFocus: Learning to Reconstruct Sharp Images from Out-of-Focus
Event Streams
Lin Zhu Xiantao Ma
Xiao Wang?
Lizhi Wang*
Hua Huang
Abstract
ET-Net
SPÁDE EZVID
Event cameras innovative sensors that cap- ture brightness changes as asynchronous events rather than traditional intensity frames. These cameras offer substantial advantages over con- ventional cameras, including high temporal reso- lution, high dynamic range, and the elimination of motion blur. However, defocus blur, com- mon image quality degradation resulting from out-of-focus lenses, complicates the challenge of wvent-based imaging. Due to the unique imag- ing mechanism of event cameras, existing focus- ing algorithms struggle to operate efficiently on sparse event data. this work, we propose EvFo- cus, novel architecture designed to reconstruct sharp images from defocus event streams for the first time. Our work includes the development of event-based out-of-focus camera model and a simulator to generate realistic defocus event streams for robust training and testing. EvFocus integrates a temporal information encoder, blur- aware two-branch decoder, and reconstruction and re-defocus module to effectively learn and correct defocus blur. Extensive experiments on both simulated and real-world datasets demon- strate that EvFocus outperforms existing methods across varying lighting conditions and blur sizes, proving its robustness and practical applicability in event-based defocus imaging.
Focus ScarN
Focus RAITEsoaam
Foent
Deloc
samp Ing
Defocus event steam
Recorstrution results
Figure I. Reconstructing sharp images from defocus event streams. Our model reconstructs sharp images that closely approximate the in-focus results from defocus event streams, effectively mitigating the impact of defocus blur in event-based imaging.
advantages, such as high temporal resolution, high dynamic range, and immunity to motion blur, making them particu- larly useful in scenarios where conventional cameras strug- gle (Gallego et al., 2020b). The ability capture precise and rapid changes in brightness opens new possibilities for im- age reconstruction applications, particularly in challenging conditions like low light and high-speed motion (Delbruck & Lichtsteiner, 2007), where traditional cameras often fail to deliver clear images.
Recent research increasingly leverages event data enhance image and video deblurring, capitalizing the rich edge information provided by event streams. Methods like (Jiang et al., 2020), (Lin et al., 2020), and (Wang et al., 2020) have successfully integrated event data with RGB image and video deblurring, leading to notable improvements in performance. For instance, (Sun et al., 2022) employs multi-head attention mechanism to combine event and im- age data, while (Kim al., 2022) introduces exposure time-based event selection (ETES) module to handle im- ages with unknown exposure times. Additionally, (Lin et al., 2022) proposes an autofocus strategy utilizing event streams, though struggles with fixed focus scenarios. Meanwhile, (Teng et al., 2024) develops a method using event accumula- tion during continuous focus scanning to aid RGB cameras in predicting sharp, focused images.
1. Introduction
Event cameras are innovative sensors that capture bright- ness changes in the form of asynchronous events rather than traditional intensity frames. These cameras offer significant
"School of Computer Science& Technology, Beijing Institute of Technology, Beijing. China "School of Computer Science, Anhui University, Hefei, China "School of Artificial Intelligence, Bei- jing Normal University, Beijing, China. Correspondence to: Hua Huang <huahuang@ @ bnu.edu.cn>>
However, major limitation of existing methods is their inability to effectively address defocus blur, which is a com- mon and critical issue in many real-world scenarios (see Fig. 1). Unlike traditional cameras, which benefit from ma- ture autofocus technology, event cameras lack such mecha-
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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EyFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
nisms and often rely on manual focusing, making defocus frequent occurrence in event data. While motion blur is natu- rally mitigated by the asynchronous nature of event cameras, defocus blur fundamentally reduces spatial intensity gradi- ents, resulting in a significant decrease in the number and quality of generated events. This makes defocus blur par- ticularly challenging to handle within existing frameworks, as the temporal dynamics of event streams are directly affected by the loss of gradient information. Unlike traditional deblurring techniques for RGB images, where spatial restoration dominates, the sparsity and continuous nature of event streams necessitate temporal -omain ap- proach to effectively handle defocus blur.
Circe of Contusion (CoC)
Scene
(a) Out-of of-Focus Thin Lens Model
Treditional Camera Model
2027A NEETO
Defocus Images
Event Sampling Model (b) Defocus Images and Events
Defous Events
In this paper, we present EvFocus, a novel framework specif- ically designed reconstruct sharp images from defocus event streams. To support this, first develop theoretical model of defocus blur in event cameras and a simulator capa- ble of generating large-scale, realistic defocus event datasets. These datasets include event streams, blurred images, sharp images, and optical flow, covering a wide range of defocus conditions. Unlike traditional defocus blur datasets, which primarily focus on spatial restoration, our dataset and ap- proach emphasize the temporal characteristics of defocus event streams, bridging the gap between theoretical explo- ration and practical application. Our framework includes a temporal information encoder with ConvLSTM layers to capture and preserve temporal dependencies, and a blur- aware two-branch decoder that separates blur-specific and aligmment features to ensure precise reconstruction. Addi- tionally, design a reconstruction and re-defocus module, where the aligned feature passes through Re-defocus mod- ule and Reconstruction module. The Re-defocus Image generated from this process undergoes self-supervised learn- ing with the blur feature, allowing the model to further learn defocus blur distribution. Experimental results demon- strate that our architecture effectively eliminates defocus blur across different conditions. In summary, the contribu- tions of this work are:
Figure 2. The sampling mechanism of defocus events. When the lens is out-of-focus, light is focused outside the sensor through the thin lens model and forms CoC on the sensor. When the light change at single pixel on the sensor reaches the threshold, the defocus event is generated.
lighting conditions and blur sizes, proving its robustness.
2. Related Work
Traditional Image-based Defocus Deblurring. Defo- cus deblurring in RGB images follows two main ap- proaches: defocus map estimation with deconvolution and deep learning based methods. Early works (Yi & Eramian, 2016; D' Andrès et al., 2016) estimate defocus maps before applying deconvolution, while (Xin et al., 2021) introduces an unsupervised approach with calibrated blur kernels. Deep learning has significantly improved defocus deblurrnng, with methods like (Abuolaim & Brown, 2020; Lee al., 2021; Son et al., 2021; Cho et al., 2021a; la; Ruan et al., 2022) en- hancing accuracy and efficiency.
For video deblurring, integrating temporal information helps recover sharp details from multiple frames (Chen et al., 2018: Pan et al., 2023; Zhu al., 2022a). However, these methods rely on intensity-based images and do not address event-based defocus blur. Unlike existing defocus blur datasets focused on spatial restoration, we explore the tem- poral characteristies of defocus event streams, shift- ing the focus from frame-based deblurring to event-based reconstruction.
1) We propose ExFocus, the first network designed to effec- tively reconstruct sharp images from defocus event streams, featuring a temporal information encoder, a blur-aware two- branch decoder, and a reconstruction and re-defocus module to ensure precise image reconstruction.
2) We explore and model defocus events in event cameras and develop simulator to generate realistic defocus event streams for robust training and evaluation. Additionally, we introduce defocus feature learning mechanism that en- hances the model's 's ability to learn defocus blur distribution through the blur-aware two-branch decoder.
Event-based Reconstruction. Neuromorphic cameras offer low latency and energy efficiency (Delbruck & Lichtsteiner, 2007; Zhu et al., 2019; 2020). Event-based reconstruction, which recovers intensity images or video from asynchronous events, is a core yet challenging application. Event- based re- construction has evolved from early physics-driven methods to deep learning. Classical approaches (Cook et al., 201 I; Kim al., 2008; Bardow al., 2016) rely on optimization
3) Extensive experiments on both simulated and real-world defocus event data demonstrate the superior performance of EvFocus, outperforming existing methods across various
2

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EyFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
Real-world events and images
Simulated events and images
ENDOOD Shiee
SIMDH
Ael Calca sas Coumbo -33
ToGal 5428
Tosu
1033 2736
Taol
1075
ToCL
5GC
Total
Tosd
Total Contt
67703
Coraars:
Counte
Coambo 1578
Defocus degree
Defocus degree
Figure 3. Event distribution of real-world (Left) and simulated (Right) data with different degrees of blur. We present the visual effects of RGB images under different degrees of blur as well as the corresponding events and event distribution statisties.
and filtering to estimate intensity images, while (Munda et al., 2018; Barua et al., 2016) shows motion-independent reconstruction feasibility. Deep learning further improves event-based reconstruction. E2VID (Rebecq al., 2019) introduces recurrent network using voxel grids, while FireNet (Scheerlinck al., 2020) achieves faster inference but struggles with high-speed motion. Improvements like E2VID+ and FStofeet et al., 2020) align syn- thetic and real data, and SPADE-based based(Cadena et al., 2021) and transformer-based (Weng et al., 2021) architectures re- fine quality. Recent advances, including SNN-based (Zhu al., 2022b) and dynamic event reconstruction models (Er- can et al., 2024), enhance efficiency and adaptability.
3. Background and Motivation
Event Sampling Mechanism. The sensor of the event cam- era operates independently at each pixel to detect changes in light intensity. An event is generated when the light inten- sity at pixel changes by more than a certain threshold from the time the last event is recorded (Gallego et al., 2020a). This can be expressed as:
AL log I(t) log I(t At),
(1)
where an event is generated if |AL| > C, C denotes the threshold. An event is defined by a quadruplet which con- tains the event's timestamp the pixel coordinates (z, y). and the event polarity p.
Event-based Deblurring. Events have been leveraged to enhance deblurring by preserving high-teemporl--esolutton edges (Jiang et al., 2020; Lin et al., 2020; Wang et al., 2020). Methods like (Sun et al., 2022) use multi-head attention to fuse event and image features, while (Kim al., 2022) pro- poses exposure time-based selection module. Autofocus strategies (Lin et al., 2022) remain limited to fixed-focus cases, and (Teng et al., 2024) introduces event accumulation for continuous focus scanning. (Lou et al., 2023) employ the event focal stack method to assist RGB image refocus- ing; however, this approach imposes stringent constraints on data acquisition.
Event-based Out-of-Focus Camera Model. Suppose that the k-th event in an event stream is expressed as
E (IRx Uke fx. PR.)
(2)
At th. changes in light intensity at the pixel reaches the threshold C, Eq. 1 can be rewritten as
AL yk. tx) L(zx, Wk. (x) L( yk. Atx), (3)
where JAL(IK. yks tx) 2 C. Conventional images typ- ically employ the thin lens model to model defocus blur (Mannan Langer, 2016). This model presumes that the thickness of the lens can be ignored, which is beneficial for simplifying optical ray tracing computations. Through this model, we approximate the circle of confusion (CoC) of the PSF of given point (Levin et al., 2007) based on the distance of the point from the lens and the camera parameters (i.e., focal length, aperture size, and distance). The model is shown in Fig. 2, where f is the focal length, the object distance, and F is the aperture value. The
Despite progress, event- besnt defocus deblurring remains challenging. Unlike motion blur, which events naturally mitigate, defocus blur weakens spatial gradients, reducing event generation and complicating sharp image reconstruc- tion. Factors like blur kernel size, event firing threshold, and refractory period further hinder effective modeling. Ad- dressing this requires a temporal-domain approach to cap- ture defocus event dynamics, moving beyond conventional spatial deblurring techniques.
3

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EyFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
distance between the lens and the sensor s' and the aperture
this case, under normal focus, the brightness change the central pixel (IrR, yk) satisfies AM yk. (x) C.
diameter q are defined as: s' = .-
fs
9 9=
f- --
The CoC radius of the scene point P1 at distance d from the camera is given by:
After defocus, the brightness change is averaged across the CoC, as described in Eq. 6. Since only the central pixel contributes a significant brightness change, the defocus effect reduces the overall brightness change at (Ixs yv), and we have AL' (z yk. fx) C. As a result, no events are triggered due to the "Defocus Averaging", even though the original brightness change exceeded the threshold. Thus, we get AL (Z', (2)= S ( Uk. (2).
q
d
(4)
8
Thus, defocused RGB imaging can be modeled as:
[2, v) (I h) (w.a))
Equality is achieved when all pixels within the blur kernel exhibit brightness changes with the same polarity, resulting in no cancellation effects. More generally, when pixels within the blur kernel exhibit brightness changes of mixed polarities, the contributions of positive and negative changes are weighted by the blur kernel G. For instance, if 24 emits positive event and another point 2, within the kernel emits a negative event with ا < and the weighted contribution AL', > 0 remains within the overall summation.
Here, I h represents the convolution operation, where is the input image and h is the convolution kernel.
Considering the diffraction of light within the CoC of defo- cus blur, the light intensity can be approximated as Gaus- sian distribution (Gokstorp, 1994; Elder & Zucker, 1998). This results in blurred edges and degraded image quality in RGB camera captures. To model event-based defocus blur, we approximate it using Gaussian blur. The defocused temporal light intensity difference AL' is defined as:
|ALzel I. then A < O
AL' AL' yk. tx)= (z yk, tx) L' L' yk. tx Atx),
From the above, we know that defocus blur causes events to become sparse (Fig. 3). The smaller the area of the object generating events, the more significant the effect of defocus blur on event generation. When events with both positive and negative polarities are generated within the blur kernel area, the polarity with smaller brightness changes is more affected by defocus blur, resulting in either no event being generated or an event generated with the opposite polarity. For further analysis, please refer the supplementary material.
(5)
where L' L'(Tk, yk, (x) and L' L2x yk lu At) represents (L+
Gta) (e) (1)))) and (I. Gt.- -br) (m -r)) respectively. Here, Gt represents the Gaussian kernel time tg, which approximates the size of the CoC. The blur kernel at (Ira, yk)
and timestamp &g can be modeled by a Gaussian distribution
and is expressed as G(z, v) = 2тσक exp
I
x x²
-202
Distribution of Out-of-Focur Event Streams. As the ob- ject distance and focal length are continuous variables, and the time resolution of the event camera is extremely high, we can assume that G remains unchanged at this moment, i... Gr. (u, = Gta-Ae(u, v). Therefore,
Out-of-Focus Event Stream Simulation. We develop a simulator to generate realistic defocus event streams for robust training and evaluation. In supplementary material, Alg. presents the simulation of our out-of-focus event stream. We apply Gaussian blur to the simulated event data using the Multi-Objects: becq et al., 2018) where multiple moving objects are cap- tured with a camera restricted to 2D motion. To simulate varying levels of defocus in real-world scenarios, we employ different PSF configurations for each scene. Experimental results demonstrate that the model trained on synthetic data achieves effective reconstruction for various defocus levels observed in real-world scenarios. We use the configuration of (Stoffregen al., 2020) ensure that our data has sim- ilar distribution as real-world data. We approximate defocus blur with multi-levels gaussian filter which is applied when high temporal resolution frames are rendered.
AT,,,, 1x)
Σ Σ AT( U(,, (6)
renderer option of ESIM (Re-
[(x+u where Al(u,)) u. - tu
At) and represents the size of the CoC at point (I Me When a pixel lies at a local strong edge or experiences sigmificant local brightness change, events are typically trig- gered. However, the defocus PSF causes the brightness peaks to shrink and the valleys to rise, resulting in local smoothing (defocus averaging). Due to the presence of this blurring phenomenon, strong brightness changes degrade into weaker brightness changes, which statistically mani- fests as reduction in event triggering and a degradation of the event distribution into a blurred distribution.
4. The Proposed Model
For instance, consider a 3 x 3 CoC where only the central pixel experiences a brightmess change that exceeds the event threshold, while all other pixels do not trigger events. In
Based on previous analysis, unlike traditional image defo- cus, event defocus also alters the number of events, mak-
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EyFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
Re-defocus Module
Sharp GT
Re-defocus
Conv e Sigmols
Image
Defocused Continuous Event Stream
CF
Conv + ReLU Concatinaton
Skip Connection
Sharp GT
Down-Samping
Faur
OutS O
Sampling
Reconstructed
Reconstruction
Defocus Image
Module
Defocus GT
Projection
5Y6comn
Layer Norm
Mus hoxd Saff Attontion
Layer Norm
Canus
Canls STS
ReBBockk
B Blur-aware Two-branch Decoder
K
Can
S-ócom
SucComn
SicCaru
Transformer Block in Re-defocus and Reconstruction Modules
Temporal Feature Encoder
Projection
Figure 4. The architecture of EvFocus, includes a Temporal Information Encoder, a Blur-aware Two-branch Decoder for separating blur and alignment features, and Reconstruction and Re- Re-Defocus Module that refines defocus blur correction through self-supervised learning, resulting in precise image reconstruction from defocus event streams.
ing event defocus deblurring and reconstruction a highly ill- ill-posed problem. In this section, we propose EvFocus, specifically designed to address the unique challenges posed by event cameras for deblurring and reconstructing defocus event streams (see Fig. 4). The model first extracts the tem- poral feature from the defocus event stream, then introduces two-branch decoder and reconstruction and re-defocus module, enabling robust defocus blur correction and image reconstruction. The model is structured as follows:
they can selectively forget irrelevant features and remember important temporal information over multiple time steps, enhancing the network's 's ability to learn and extract useful spatio-temporal features from the sparse event data.
Each encoder layer consists of 2D nown-sampling convo- lution and ConvI-TTM (Shi al., 2015). The 2D down- sampling convolution has kernel size of and a stride of 2. The ConvLSTM has a kernel size of 3, and the number of input and hidden layers is the same as the down- down-sampling convolution. Each encoder maintains state s that is up- dated at each iteration to memorize temporal information. ConvLSTM can enhance the stability of features by remem- bering and forgetting previous states. Three consecutive ConvLSTM blocks be represented by the equation :
The event camera's asyn- chronous nature produces sparse event streams rather than conventional frame-base based sequences, making it difficult to extract meaningful temporal information. Our temporal in- formation encoder is motivated by the need to capture and re- tain temporal dependencies in defocus event streams without losing critical temporal resolution. Similar to E2VID (Re- becq et al., 2019), we utilize ConvLSTM units to effectively preserve temporal information over time by maintaining hidden states.
Temporal Information Encoder.
FCL fT
(FC st
CL
where PCL denote the feature of ConvL i E {1, 2,3) denotes the current i-th ConvLSTM block, and sf denotes the state of the i-th ConVLSTM block at time L.
1) Event Representation. Events typically processed and converted into alternative representations to fit the net- work. In our model, we utilize voxel grid (Zihao Zhu et al., 2018) as the spatio-temporal representation of event stream.
Blur-aware Two-branch Decoder. The blur-aware two- branch decoder addresses the challenge of separating blur- specific features from the alignment features necessary for accurate image reconstruction. The need for two parallel decoding branches arises from the observation that blur and alignment features often interfere with each other during re- construction. By processing them separately, we can ensure
2) Feature Extraction. To stabilize the feature extraction process in an asynchronous event data context, we incorpo- rate ConvLSTM layers. These layers are chosen because
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EyFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
Smal (bed (.3.00%
Mtadua gortl Detoc
Large
Events
Image
Ours
ET-net
E2VID
ET-net
Figure 5. Qualitative comparison with baseline methods on out-of-focus simulated data. Our reconstruction result has the best quality, closest to the sharp ground truth, and restores more details.
that the blur decoder focuses solely understanding and correcting defocus blur, while the alignment decoder works on aligning features correctly for the final sharp image re- construction. The cantoiitemporlll encoded features are processed in parallel through two decoders, namely the blur feature decoder Ditu and the aligned feature decoder Datign= generating the blur feature Falur and the aligned feature Falign- Each decoder consists of three decoder blocks, where each decoder block is composed of bilinear upsampling and convolutional block with a kernel size of 5, followed by ReIU and BN.
ability to focus critical features over long distances, addressing the quadratic growth issue of key-query
interactions by applying self-attention across channels and
computing cross chasnel covariance for generating the attention map. Given queries (Q), keys (K), and values (V), we reshape and such that their dot product generates transposed attention map A RCXC instead of the traditional HW Overall, MHCA can be summarized as:
X' = Wp Attention V) +X,
(8)
Attention Q, K, V) = softmax K-
(9)
The features generated by the blur feature decoder Dater undergo a prediction layer and are then used to learn blur reconstruction with the blur ground truth (blur gt), which subsequently supervises the redegradation process. The aligned feature decoder Datign performs preliminary align- ment, followed by deblur reconstruction and the redegrada- tion process. After each decoder block, the Cross-Modal Fusion (CF) applies ReLU activation to the two features assist in extracting alignment features, and then concate- nates them along the channel dimension. Through the CF module, information from both parts is combined, enhanc- ing the features of the alignment decoder module, thus better focusing on extracting alignment information.
where X' and X are input and output feature maps, Wp is the 1 x point-wise convolution, and is a learnable scaling parameter to control the magnitude of (K Q) before applying softmax.
Self-supervised Mechanism We design a self-supervised loss mechanism. First, the aligned feature Fatign passes through a Re-defocus module and a Reconstruction module. The Re-defocus module outputs a Re-defocus Image, which undergoes self-supervised learning with the blur feature Folur- allowing the model to further learn the distribution of defocus blur in the Blur-aware Two-branch Decoder. Finally, the aligned feature Falign passes through the Reconstruction module, and under temporal consistency supervision, the model produces the final clear image reconstruction.
Reconstruction and Re-defocus Module. We design a self-supervised loss mechanism where the aligned feature Faligo first passes through Re-defocus module and Re- construction module. The Re-defocus module generates Re-defocus Image, which is used in self-supervised learning with the blur feature Felur. allowing the model to better learn defocus blur distribution in the Blur-aware Two-branch De- coder. The aligned feature Faign is then processed by the Reconstruction module to produce the final clear image reconstruction under temporal consistency supervision.
Loss Function. As shown Fig. 4, we use perceptual loss (Zhang et al., 2018) and temporal consistency loss (Lai et al., 2018; Rebecq et al., 2019) to ensure the quality of reconstruction, and we learn the deblurring process through Ln loss. The final loss is the sum of the losses:
LK = where LTCP represents the Temporal Consistency loss, and LRR, and L.R respectively represent the perceptual loss of the align feature Falign after prediction supervised by the sharp ground truth (sharp gt) and the blur feature Fūlur after prediction supervised by the defocus ground truth (blur gt). 21,2,3,4 is separately set 4, 1, 7, respectively.
X2 X3L + +4L1, (10)
Our Reconstruction and Re-defocus Module utilizes Transformer-based Multi-Head Channel Attention (MHCA)s (Vaswani, 2017; Zhang et al., 2024). to enhance defocus blur handling. Transformers are selected for their
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EyFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
Input/event
Image
Figure 6. Qualitative comparison on out-of-focus real-world data. Our proposed model achieves better reconstruction results with the clear edge restored.
Table I. Quantitative comparison on simulated event data with different defocus degrees.
Metric
sharp
k=3, ==1.0
k=5, o=1.5
k=7, a=2.0
a=2.5
k=11, d=3.0
Mean
0.065
0.085
0.044
0.065
0.065
4
0.047
0.047
0.050
0.052
ET.Ne
E2VID
0.256
0.234
0,216
0,202
0.190
0.184
0.214
E2VID+
0.342
0.309
0,282
0.265
0.248
0.237
0,281
SSIM
B
0.497
0.486
0.450
0.417
0.385
0,360
0.433
ET-Net
0.352
0.351
0.305
0.283
0.261
0.247
0.297
ET-Net*
0.425
0.428
0.413
0.392
0.367
0.349
0.396
Ours
0,550
0,525
0.486
0,449
0,413
0,385
0.468
E2VID
0.344
0.361
0.379
0. 394
0.406
0,422
0.384
E2VID
0.234
0.258
0.279
0. 0.205
3.309
0.316
0,282
LPIPS
R2VID*
0.173
0.173
0.188
0.200
0.213
0.226
0.196
Eet
0.213
0.234
0.254
0.273
0.303
0.261
ET-
0.151
0.155
0.168
0.
0.192
0,204
0.175
Ours
0.144
0.146
0.161
0.169
0.182
0.195
0.166
5. Experiment
scenes. 2) Implement Details. Our model implemented using the PyTorch framework. We adopt constant strat- egy of learning rate during training, which is set at lc-4. Our model is trained for 300 epochs with batch size of 1 on 3 NVIDIA GeForce RTX 3090 GPUs. 3) Evaluation metrics. For quantitative evaluation on synthetic data, we consider three widely-used evaluation metrics: (i) structure similarity (SSIM) (Wang et al., 2004). mean squared er- ror (MSE), and (iii) perceptual similarity (LPIPS) (Zhang et al., 2018). For quantitative evaluation real data,we use two No-Reference Image Quality Assessment metrics: Brisque (Mittal et al., 2012a) and Niqe (Mittal et al., 2012b). BRISQUE (Blind/Referenceless Image Spatial Quality Eval- uator) evaluates natural scene statistics the spatial domain without requiring a reference image, while NIQE (Natural Image Quality Evaluator) computes quality based on devia- tions from learned statistical regularities of natural images.
Experimental Setup. this section, we compare our method with mainstream reconstruction techniques both qualitatively and quantitatively using simulated and real data. We test model on simulated data with varying blur levels demonstrate its adaptability to different de- grees of defocus blur, and on real data with varying mo- tion speeds and different focal planes to show its effective- ness in handling motion blur and robustness across varying depth levels. And we visualize feature in our model demonstrate the effectiveness the model architecture. Additionally, we perform ablation studies to evaluate the contribution of each module in our proposed method. 1) Dataset. As stated in Sec. 2, we generate sequences of defocus events, sharp images, and optical flows, in which 41 sequences are used the training set and sequences in the test set. To verify the effectiveness our model on real data, we use the DAVIS 346 cameras to capture real-world
Evaluation on Simulated Event Data. 1) Qualitative Re-

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EyFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
Table 3. Ablation study.
Model
SSIM1
MSEL
LPIPS
All Modules
0.604
0.021
0.139
w/o CF
0.603
0.021
0.141
w/o sharp supervision (Fatign)
0.577
0.024
0.141
w/o defocus supervision
0.140
w/o Reconstruction Module
0.601
0.022
0.139
Events
Ours
E2VID
E2VID+
E2VID
ET-Net
ET-Net*
tively represent the Events view and the original APS frame captured by the camera. Each of the remaining columns represents the reconstruction result of one method. Fig. 6 shows SPADE-E2VID has the worst effect and the greatest degree of blur. E2VID can reconstruct basic objects, but the degree of blur is still quite large. E2VID+ and ET-Net have better reconstruction, but still remain a lot of artifacts and blurs. The retrained E2VID+ can eliminate blur to a certain extent, but high-frequency information such as edges still cannot be presented well. The retrained ET-Net can reconstruct high-frequence information to a certain extent. Compared with the above results, our method has the best reconstruction quality, and the restoration of details such as edges is the closest to ground truth. 2) Quantitative Results. Since the visual results of SPADE-E2VID are poor, and the image deblurring of MIMO-UNG or MIMO-UNet+ connected after E2VID does not improve the quality of the reconstructed image, we only conduct quantitative compar- isons of the remaining methods. Among them, our method achieves the best performance. We present quantitative re- sults on real data in Table 2. We omit SPADE-E2VIH and E2 EDDID followed by MIMO-UNet or MIMO-UNet+ due to poor quantitative scores. Our method achieves the best results on both Brisque and Niqe metrics.
Figure 7. Qualitative comparison with baseline methods on real data with different speeds. Our proposed model has better results with sharp details and fewer artifacts.
Table 2. Quantitative comparisons on the real-works data.
Model
Brisque
Nigel
Ours
27.136
6.345
E2VID
30.977
12.505
E2VID+
38.957
7,986
E2VID+*
34.399
6.877
FTT-Net
31.994
7.771
ET-Net
31,855
6,868
sults. Fig. 5 presents the quantitative results obtained by evaluating our method and current mainstream methods on the simulated data sequence generated by us. E2VID+* and ET-Net" denote the models are finettuned on our dataset. Among them, the first column represents the reconstruction results of clear events and sharp ground truth. The other columns represent different degrees of blur. The larger the kernel size and the larger the a, the bigger the blur. Each row shows the reconstruction result of each method for dif- ferent blurred events. The last row gives the ground truth as a reference. As shown in Fig. 6, E2VID (Rebeeq et al., 2019) and E2VID followed by MIMO-UNet or MIMO- UNet+ (Cho et al., 2021b) have the lowest quality and the most prominent artifacts and blurs, and basically unrec- ognizable. SPADE-E2VIL (Cadena et al., 2021) has rela- tively fewer artifacts, but the degree of blur is still quite large. E2VID+ (Stoffregen et al., 2020) and ET-Net (Weng et al., 2021) have relatively less blur than the former. E2VID+ and ET-Net retrained on the defocus synthetic data further reduce blur and distortion. However, the contrast of E2VID+ and the retrained E2VID+ still has serious distortion. And results are also better than all the above results and are the closest to the image quality of the sharp ground truth. 2) Quantitative Results. We calculate the average value of each metric on all frames in different blur degree groups as in Table The EvFocus method achieves state- of-the-art performance all metrics. These results validate the effectiveness of the proposed EvFocus method, and the reconstructed images generated by this method are more perceptually satisfactory and high-fidelity.
Ablation Study. To highlight the effectiveness of each module in our model, we conduct ablation experiments as shown in Table 3. As illustrated in the table:
1) Effect of sharp supervision to F Falign- Removing the perceptual loss supervised by the sharp ground truth re- sults in the worst performance in MSE and LPIPS. This demonstrates that the align decoder plays a crucial role in the reconstruction and re-degradation process by providing prior knowledge align Fatign with the followed reconstruc- tion. 2) Effect of Reconstruction Module. The model without the reconstruction module also shows worse per- formance, indicating that the reconstruction module, as the last module before producing the clear predicted image, im- proves the quality of the reconstruction results. 3) Effect of CF. Removing the CF structure leads to decline in the model's performance, proving the effectiveness of our strategy to enhance aligned features through Cross-Modal Fusion. 4) Effeet of T,, re-defocus supervision. The model without the L, loss also shows noticeable decrease, further validating the importance of our re-degradation module.
Evaluation on Real-world Event Data. 1) Qualitative Re- sults. We present the qualitative results in Fig. 6, in which each row represents the reconstruction result of the data captured in one scene. The first and second columns respec-
Feature Visualization. We visualize the intermediate fea-
8

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EyFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
Figure 9. Qualitative experiment on different depth levels. We capture three scenes with distinct depth variations for evaluation. Our method demonstrates effective deblurring performance across different depth levels.
09
(Fig. 7). E2VID has only slight motion blur at low speed, but does not effectively remove the motion blur at high speed. The reconstruction result is generally blurry. E2VID+ and the retrained E2VID+ have better reconstruction quality at low speed. but cannot effectively remove high-speed motion blur. ET-Neet and the retrained ET-Net have better detail re- covery at low speed. The retrained ET-Net has less motion blur at high speeds than ET-Net but still cannot be removed well. In contrast, our model shows better reconstruction quality at both high and low speed, and retains the best de- tails, proving that our method is also superior to the above methods for the reconstruction of different event densities.
Soone
Events
BGB
Festure visueliattio
(c)
Figure 8. Feature visualization. We visualize the features input to the decoder. Taking (a) as an example, the red box indicates region with slight defocus, while the blue box shows a region with severe defocus. Despite similar event densities caused by motion, the feature map reveals stronger responses in the heavily blurred area. This demonstrates that our network exhibits varying feature intensities for blur at different depths, validating the model's phys- ical consistency.
Parameters. We present the parameter size of each model and inference speed in Table 4. Our model is in the same order of magnitude as mainstream models such as E2VID and is lower than ETNet.
tures of our model using real-world data containing scenes with objects varying degrees of defocus in Fig. 8. The results demonstrate that our model can effectively perceive features across different defocus levels, validating the effec- tiveness of the model design.
Table 4, Comparison on Parameters and Runtime.
Methods
Params(M)
Inference Time(ms)
E2VID
10.71
Evaluation on Different Out-of-focus Blur Size. In Fig. 5, we conduct experiments on the degree of blur that our model can handle. Through experiments, our model can perform relatively complete and clear reconstructions for blurred events with kernel size less than equal to 7. For blurred events with kernel size greater than 7, the reconstruction quality of our model can also be the best.
ETNet
22.18
25.71
SPADE
11.46
12.66
Focus (ours)
12.11
24.09
6. Conclusion
We propose EvFocus, a novel framework for reconstructing sharp images from defocus event streams. incorporates a temporal encoder and blur-aware decoder to effectively correct defocus blur. Additionally, we develop defocus event simulator to generate realistic training data, enabling robust model performance. Unlike traditional methods fo- cused on spatial restoration, EvFocus leverages the temporal characteristics of event streams for improved defocus han- dling. Experiments on simulated and real-world datasets show that EvFocus outperforms existing methods, demon- strating its effectiveness in event-based defocus imaging.
Evaluation on Different Depth Level. We conduct ex- periments on scenes with varying depths to validate our model's 's ability to recover texture information at different focal lengths within the same scene in Fig. 9. The experi- mental results demonstrate that our model achieves effective defocus recovery across different focal settings.
Evaluation on Different Motion Speed. To test the pro- cessing effect of our model on different blurs, we collect real event data at different moving speeds for experiments

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EyFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
Impact Statement
Cook, M., Gugelmann, L.,, Jug, F., Krautz, C., and Steger, A. Interacting maps for fast visual interpretation. In The 2011 International Joint Conference Neural Networks, doi: 10.1109/ICCNN.201.1.00
This paper presents work whose goal to advance the field of Machine Learning. There are many potential societal consequences of our work, none which feel must be specifically highlighted here.
pp. 770-776, 2011.
Delbruck, T. and Lichtsteiner, Fast sensory motor control based on event-based hybrid ceuromorphic-proeeduue system. In 2007 IEEE international symposium cir- cuits and systems, pp. 845-848, IEEE, 2007.
Acknowledgments
This work partially supported by National Natural Science Foundation of China under Grant 6o.62302041 and China National Postdoctoral Program under contract
D' Andrès, L., Salvador, J., Kochale, A., and Süsstrunk, cum-aarmettric blur map regression for depth of field extension. IEEE Transactions Image Processing, 25 (4):1660-1673, 2016. doi: 10.1109/TP.220162522660
No.BX20230469.
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EyFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
A. Appendix: Theoretical Framework for Simulation
Our motivation to explore the temporal characteristics of defocus-induced event streams, which are crucial for understand- ing the dynamic interactions between motion, scene structure, and defocus effects. Unlike traditional defocus blur datasets, which primarily focus on the spatial restoration of sharp images from blurred ones, our work emphasizes the generation and analysis of event streams in the temporal domain. This shift in focus allows model and study how defocus blur impacts the asynchronous nature of event data, capturing fine-graines temporal dynamics that are often overlooked in conventional datasets. By incorporating motion dynamics and adopting multiple strategies, we aim to simulate real-world scenarios with greater fidelity and provide new insights into event-based vision under defocus conditions.
A.1. Event Triggering Model and Intensity Dynamics
Event cameras capture temporal intensity changes at high temporal resolution, generating asynchronous events based on logarithmic intensity differences. The triggering condition for an event at pixel (xr, y) and time can be formalized as:
if AMoana C++
L(x.dt)
E(x, U, v,
-1, 1, if AL(eg.)) SCC,
(I))
0,
otherwise,
where:
Lar,y, v, denotes the light intensity at pixel (Ir, v) at time 1,
AL y. t) = L(x, y. t) L(x, y. At) represents the temporal change in intensity over a time interval At,
C+ and C_ are predefined positive and negative contrast thresholds, respectively.
The magnitude of AL t) denotes temporal intensity gradient, which is influenced by two critical factors:
1. The relative motion between the camera and the observed scene, which induces pixel-level intensity shifts in the image plane.
2. The spatial intensity gradient, L y), representing local intensity variations.
These factors collectively determine the event triggering behavior and the density of the resulting event stream.
A.2. Impact of Blur on Intensity Gradients
Optical blur, such as defocus, attenuates spatial intensity variations by smoothing local intensity gradients, effectively reducing [z y). This smoothing effect can be mathematically represented using a Gaussian blur kernel Gg. defined as:
T (x. y, = G. T(,,,,
(12)
where G. a Gaussian kernel with standard deviation o that determines the blur magnitude. Consequently, the blurred spatial intensity gradient is expressed as:
V Lotur (2, (2, Go VL(x,,,t)
(13)
As the blur strength (о) increases:
1. The spatial intensity gradient V Luuee (2,, t) diminishes, leading to a reduced magnitude of temporal intensity changes, AL(x, t)
13

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EyFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
2. The reduction in intensity changes results in fewer events being generated, producing a sparser event stream.
Thus, the relationship between blur and event sparsity forms a fundamental aspect of modeling the effect of optical defocus on event generation.
A.3. Motion-Induced Intensity Changes
Relative motion between the camera and the scene is another primary source of temporal intensity variation. Let the motion-inde notion-induced speed in the image plane be Vp. The temporal change in intensity due to motion can be approximated as:
AL(x,,11 x Vp V Lntar (I, ys 1),
(14)
where:
V is the motion velocity projected onto the image plane,
(auur y. the blurred spatial gradient at pixel (2,, y)-
From this expression, it is evident that:
High motion velocity (V, large) amplifies temporal intensity changes, thereby mitigating the smoothing effects of blur.
Low motion velocity (V, small) reduces intensity changes, making the impact of blur more pronounced.
This interaction highlights the interplay between motion dynamics and optical blur in determining event density and spatial distribution.
A.4. Interdependence of Motion Speed and Blur Strength
an object moves with velocity Vp, we can simulate the object's movement by applying geometric transformations T(t) (such as translation, rotation, or scaling):
Transtormed (2, v. Tx)))((x.
(15)
Event triggering depends on the change in light intensity,
AL v. t) = Liramsffommed (I. y, Lurasformed y. t At).
(16)
we choose an appropriate motion velocity Vg, the intensity change rate generated by the geometric transformation matches that produced by different depth-related defocus blur, as shown by:
AL y,
A (x, y, d)
(17)
Txx,, v.t) Ta (x, y, 1, d)
Thus, for given motion speed V,, the events triggered by motion can be equivalent to those triggered by depth-related defocus blur.
Theorem 1 (Equivalence Theorem): There exists a mapping between the blur level (d) and the motion speed Vp such that for each depth d, the event density generated by motion at a specific velocity are equivalent to those generated by defocus blur that depth:
(d)
(18)
Vp
14

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EyFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
The preceding analysis suggests inverse relationship between motion speed (V₂) and defocus blur strength (σ) in their influence temporal intensity changes and event generation:
AL v. x Vp V L (x,
(19)
This relationship implies that:
1. High-speed motion (V, large) can counteract the effect of small blur kernel (a small).
2. Low-speed motion (Vp small) amplifies the impact of a large blur kernel (ar large).
This equivalence facilitates the simulation of varying levels of blur by appropriately adjusting the motion speed.
A.5. Simulation Pipeline
The analysis demonstrates that the generation of defocus-induced event streams is influenced by motion speed, scene intensity, and the degree defocus. Unlike traditional defocus-relatee work that focuses on image restoration the spatial domain, our research primarily investigates the temporal characteristics of defocus event streams. To model this, we adopt different backgrounds and foregrounds, complex motion trajectories and motion parameters, and multiple Gaussian blur kernels with varying standard deviations a, the simulation process structured follows:
Algorithm Synthetic Data Generation Pipeline Require:
A set of background images {B.}
A set of foreground images (Fij}} for each background i
Motion parameters (e.g. translation, rotation) for background & foreground
Number time steps
Ensure:
Synthetic dataset containing rendered scenes with events & optical flow
I: for each scene do
2: Select one background image B,
Select M foreground images {Fi, 1-M
4: Generate motion trajectories for background and each foreground:
trajs <- GeneratteTrrjeetoryymmooooo parameters)
trajy,, GenerateeTrgeectoryymoooto parameters)
for 1,..., do
6: Sample camera pose p < SamplePose()
7:
Sample camera distortion d. +- SampleDistortion()
8:
Render defocus brightness image:
I. + Render(B., (Fi,), trajglt), (trajs,, [t]} Pt, de)
9:
Compute brightness change Al; = I, It-1 (ift + 1)
10: Generate events & + EventGeneration A.)
11: Compute optical flow OpticalFlow(II)
12:
end for
Store (1)) (E.)7mr. [u.] J as the dataset for scene
14:
end for
B. Appendix: Network Architecture Details
Fig. 4 provides an overview of the ExFocus network, as detailed in Section 3. In this Section, we will explain other details of the model.
15

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EyFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
B.1. Temporal Information Encoder.
Our temporal information encoder module consists of head layer and three encoder modules. The head layer outputs number of channels N,- 32, and the number of channels is doubled after each encoder layer. The inputs to our temporal information encoding module 5-channel voxel-grids, and the outputs are feature maps with N₁ x 2 channels.
B.2. Self-supervised Mechanism
We design supervid loss mechanism. First, the aligned feature Falign passes through Re-defocus module and Reconstruction module. The Re-defocus module outputs a Re-defocus Image, which undergoes self self-supervised learning with the blur feature Fatur- allowing the model to further learn the distribution of defocus blur in the Blur-aware Two-branch Decoder. Finally, the aligned feature Falggn passes through the Reconstruction module, and under temporal consistency supervision, the model produces the final clear image reconstruction.
B.3. Reconstruction and Re-defocus Module
The Reconstruction and Re-defocus Module is designed to refine the network's 's ability to handle defocus blur. By utilizing Transformer- based Multi-Head Channel Attention (MHCA), we aim to improve the network's ' focus on the most relevant features for blur correction. Transformers are chosen because they offer the flexibility to attend to important features over long distances in the feature space, which is essential for effectively capturing the intricate patterns of defocus blur.
Our Reconstruction and Re-defocus Module consists of sequentially cascaded Transformer- bbssed Multi-Head Channel Attention (MHCA) modules (Vaswani, 2017; Zhang et al., 2024). Transformers have demonstrated unique advantages in image restoration tasks due to their flexible and adaptive kernels. To address the quadratic growth problem of key-query dot product interactions, we adopt the idea of applying self-attention across channels rather than spatial dimensions and compute the cross-channel covariance to generate the attention map.
B.4. Loss Fuction
Perceptual loss We use the calibrated perceptual loss LPIPS (Zhang et al., 2018) to supervise the reconstruction image i with sharp ground truth and two decoder outputs I1 and Iy after processing the prediction with out-of-focur and focused ground truth. The LPIPS loss can evaluate the perceptual similarity of frame quality in a more realistic manner. The perceptual reconstruction loss is specifically calculated as
LR = d(kx: Ik),
(20)
where d represents the LPIPS distance.
Temporal Consistency and Perceptual Joint Loss To supervise the final reconstructed image, we use a joint loss of temporal consistency loss and perceptual loss. The perceptual loss between the predicted image and the sharp ground truth used to ensure reconstruction quality. At the same time,due to the presence of temporal artifacts, we use a temporal consistency loss to correct the warping error between the reconstructed consecutive frames through the optical flow map (Lai al., 2018). This is specifically expressed as:
TC MM 1 W (f=
(21)
where d represents the LPIPS distance (Zhang et al., 2018), W (fx-1) 1) is the result of warping the reconstruction f to _ and M = exp(-a | M (1))) is a weighting term that helps mitigate the
i using the optical flow
effects of occlusion.
therefore,the Temporal Consistency and Perceptual joint Loss can be formulated
L TCP XLR, + LTC
(22)
where TCP represents the Temporal Consistency loss LR,g represents the perceptual loss, and À is set to 0.75.
L1 Loss To ensure that the re-defocus module can learn defocus blur, we use the L1 loss to supervise the output of the re-defocus module with the output of the blur decoder through the prediction layer, which can be described as:
16

---

EyFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
= 11 Pxl11 where ls represents the predicted output of the blur decoder Dotur and l'x represents the output of the re-defocus module.
Total Loss
The final loss is the sum of the losses mentioned above:
T LR + 2 A +aL11
(23)
where LR and L respectively represent the perceptual loss of the align feature Falign after prediction supervised by the sharp ground truth (sharp gt) and the blur feature Fylur after prediction supervised by the defocus ground truth (blur gt). 2,,2,3,4 is separately set to 4, 1, 7, 1, respectively.
B.5. Dataset Details
To evaluate the applicability of our method, conduct tests using seven real-world scenes as in Fig. 10. These scenes are captured by DVS 346, where events and frames are 346 x 260 resolution. The "Pic" test assesses the reconstruction performance for defocus events with similar focal lengths, while the "motionspeedl" and "motionspeed2" tests evaluate the model's suitability for handling motion blur. The "desk" "board" "library" and "toy" tests examine the reconstruction performance for defocus events in scenes with different blur kernels and significantly different focal lengths.
Table Statistics Test Scenes
Scene pic motionspeed1
Number of Frames
Number of Events
135
7912802
169
23476852
motionspeed2
41
I 166
desk
289
14980786
board
204
36806803
library
223
10912574
toy
377
6575390
C. Appendix: More Experimental Results
In this section, we primarily present the qualitative comparison of our model with several mainstream models across all real-world scenes and the parameter size for each model.
C.1. Qualitative Comparison
In Fig. 11 and Fig. 12, we compare the reconstruction results of our model with E2VID, E2VID++ ETNET and SPADE E2VID. From these experimental results, it can be observed that the reconstruction result of our model is the most effective in terms of blur removal. is capable clearly reconstructing edge details even when there is a large variance in focal length distribution. Additionally, the motion blur removal effect under varying motion speeds is superior to that of other methods.
In Fig. 13 and Fig. 14, further evaluate the performance of other mainstream reconstruction methods by using their results input to a deblurring model, NRKNet (Yuhui Quan & Ji, 2023), and then compare the output with our model's results. The experimental results demonstrate that the reconstructed frames followed by the additional defocus deblurring network, still fail to effectively remove the blur. In contrast, our model achieves high reconstruction quality.
17

---

EyFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
Board
Desk
Matiorspeed1
Metiorspeed
Pic
Toy
Library
Events
RGB
Events
RGB
Figure 10. Examples of our real-worle dataset.
18

---

EyFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
Pic
Board
Desk
Events
Ours
ETNet
E2VID+
E2VID
SPADE EZVID
Figure 11. Visual comparison results in the Desk, Board, and Pic scenes. We compared our model with E2VID, E2VID+, ETNET and SPADE E2VID.
19

---

EyFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
Lib
Mis1
Ms2
Toy
Events
Ours
ETNet
E2VID+
E2VID
SPADE E2VID
Figure 12. Visual comparison results in the Lib, Msl, Ms2 and Toy scenes. We compared our model with E2VID, E2VID+, ETNET and SPADE E2VID.
20

---

EyFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
Pic
Board
L
Desk
SPADE E2VID- NRKNet
Events
Ours
ETNet-NRKNet
E2VID+-NRKNet
E2VID-NRKNet
Figure 13. Visual comparison of E2VID, E2VID+, ETNET, SPADE E2VID followed by NRKNet and method in the Pic. Board and Desk scenes. For a better comparison defocus deblurring, we utilize the reconstruction frames from E2VID, E2VID+, ETNET, and SPADE E2VID input to NRKNet (Yubui Quan & Ji, 2023), and compare the results with method.
21

---

EyFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams
LiB
Ms1
Ms2
Toy
SPADE E2VID- NRKNet
Events
Ours
ERNet-NRKNet
E2VID+-NRKNet
E2VID-NRKNet
Figure Visual comparison E2VID, E2VID+, ETNET, SPADE F2VID followed by NRKNet our method the Lib, Msl, Ms2 and Toy scenes.
22


Reviews:
[{"review_id": "3J4agZCVrU", "reviewer": "Reviewer_hGCk", "summary": "This paper proposes EvFocus, a novel architecture for reconstructing sharp images from defocused event streams. The key innovation lies in its temporal encoder, blur-aware dual-branch decoder, and re-defocus module, combined with a synthetic defocus event dataset for training. Experiments on synthetic and real-world datasets demonstrate superior performance over existing methods under varying blur sizes and lighting conditions.\n\n## update after rebuttal \nThe author's response has resolved my issues. Considering the other reviewers' comments, I have decided to increase the score.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**We thank the reviewer for their thoughtful comments and constructive suggestions. We address each of the raised points below.**\n\n---\n### **Computational Efficiency**\n\nWe agree that runtime performance is important for real-world deployment. In response, we have conducted a **comprehensive runtime and parameter comparison** (input size: `[1, 5, 264, 352]` on RTX 3090 GPU):\n\n| **Model**     | **#Params (M)** | **Inference Time (ms)** |\n|---------------|------------------|--------------------------|\n| E2VID         | 10.71            | 4.41                     |\n| ET-NET        | 22.18            | 25.71                    |\n| SPADE-E2VID   | 11.46            | 12.66                    |\n| **EvFocus**   | **12.11**        | **24.09**                |\n\nOur model achieves a good trade-off between accuracy and complexity. The inference time is similar to ET-NET; our method offers significantly improved image quality. \n\n---\n\n### **Why isn't the real defocus ground truth (GT) used during refocusing?**\n\nThank you for this insightful question. Our model architecture includes a blur-aware dual-branch decoder, where the blur decoder passes through three CF modules and fuses with features from the align decoder. We employ a deblurring reconstruction module on the align path to enhance its output. To further leverage the design, we introduce a loss between the blur decoder output and the re-defocus module to encourage CF modules to extract meaningful blur representations.\n\nOne of our intentions is to encourage effective feature interaction between the two branches. Specifically, the blur decoder extracts blur-aware event representations, which are used to guide and support the learning process of the align decoder. This interaction allows the align decoder to better capture defocus-related features from the event domain.\nIn fact, there is a modality gap between events and RGB images: some low-texture regions or subtle motion details present in the GT image may not be well represented in event streams. In this context, directly supervising with defocus GT could potentially lead the optimization to overly favor one branch (e.g., the blur decoder), focusing more on image-domain appearance rather than learning the common defocus patterns reflected in the event stream. This may weaken the collaborative learning dynamics between the two branches.\n\nOur current design aims to balance the contributions of both branches and promote cooperative learning, and has proven effective in practice (validated through ablation studies in the paper). \nWe appreciate the reviewer’s question, and we will include a more detailed explanation of this design choice in the revised version.\n\n---\n\n### **What would be the performance if reconstruction is followed by defocus deblurring in the image domain?**\n\nWe performed experiments and reported results in our supplementary material (Fig. 11 and Fig. 12), where the NRKNet network was employed as SIDD network processing for defocus removal. \n\nThe results of other methods + SIDD were suboptimal. This is because:\n\n- Defocused event streams are inherently sparse and contain degraded spatial gradients.\n- Reconstructed intensity images from such events exhibit heavy artifacts and blurred textures, limiting the effectiveness of downstream image-based deblurring algorithms.\n- Events capture log-intensity changes, not absolute intensity, and are affected by defocus noise, which severely impacts the quality of intermediate reconstructions.\n\nThese findings suggest that directly addressing defocus in the event domain, as EvFocus does, is more robust and efficient.\n\n---\n\n### **What is the significance of using an event camera for this task?**\n\nThank you for your suggestion. Compared to Lou et al. [1], their work leverages a focal sweep with event cameras to reconstruct an all-in-focus image using timestamp selection and focal stack merging. This method assumes a dynamic focus adjustment and high-quality RGB input.\nIn contrast, our setting targets scenarios where only event streams are available, especially in high-speed or low-light scenes.\n\nOur approach reconstructs sharp images from single-pass, defocused event streams, making it suitable for settings without active focus control or RGB inputs like Lou et al. [1]. Hence, it complements Lou et al.’s method and serves different application domains.\n\n---\n\nWe thank the reviewer again for the helpful comments and the recognition of our work. We will incorporate the suggestions and clarifications in the revised version.", "claims_and_evidence": "yes", "methods_and_evaluation_criteria": "yes", "theoretical_claims": "yes", "experimental_designs_or_analyses": "yes", "relation_to_broader_scientific_literature": "n/a", "essential_references_not_discussed": "n/a", "other_strengths_and_weaknesses": "Other Strengths:\n\nFirst systematic solution for event-based defocus deblurring.\n\nMain Weaknesses:\n\nComputational efficiency and real-time performance metrics are not reported, crucial for event camera applications.", "other_comments_or_suggestions": "n/a", "questions_for_authors": "1. Why isn't the real defocus ground truth (gt) used during refocusing?  \n\n2. What would be the performance if reconstruction is performed first, followed by applying state-of-the-art defocus algorithms in the image domain?  \n\n3. What is the significance of using an event camera for this task? Some work [1] suggest that the high-speed characteristics of event cameras enable rapid autofocus to avoid defocus issues. In what scenarios would this algorithm be necessary?\n\n[1] Lou, Hanyue, et al. \"All-in-focus imaging from event focal stack.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.", "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": "XKabM3wIYs", "reviewer": "Reviewer_MV6M", "summary": "This paper introduce a new network architecture for restoring all-in-focus grayscale video from defocused event-camera measurements. The method assumes a thin lens model and gaussian defocus blur. It is tested on simulated and real data. The proposed method outperforms existing methods quantitatively and qualitatively, though not always by a large margin.\n\nOverall, this is a clearly written and technically sound paper that tackles an under-investigated real-world problem.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**We sincerely thank the reviewer for the encouraging feedback and constructive suggestions. We address the main points below.**\n\n---\n\n### **Additional baseline: event-to-video + image/video deblurring**\n\nWe appreciate this valuable suggestion. In the supplementary material (Fig. 11–12), we included an experiment that follows a similar two-stage baseline: we first reconstruct intensity images from defocused events, followed by applying NRKNet [CVPR'23]  as the SIDD network for defocus deblurring.\n\nHowever, the results were suboptimal, mainly due to the low quality of reconstructed images from sparse defocused events, which contain significant artifacts and structural degradation.\n\nThese observations support our decision to address defocus directly in the event domain, which our method handles more effectively in terms of both reconstruction fidelity and robustness. We will add more discussion in the revised version.\n\n---\n\n### **Clarification on Brisque and NIQE metrics**\n\nThank you for pointing this out. We will add the following clarifications in the final version:\n\n- BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator) evaluates natural scene statistics in the spatial domain without requiring a reference image.\n- NIQE (Natural Image Quality Evaluator) computes quality based on deviations from learned statistical regularities of natural images.\n\nThese no-reference metrics are particularly useful for assessing real-world reconstruction results where ground truth is unavailable.\n\n---\n\n### **Discussion of related work: Lou et al. (CVPR 2023)**\n\nThank you for highlighting this relevant work. Lou et al. [CVPR'23] propose an all-in-focus imaging method based on both RGB images and event focal stacks, which relies on capturing event streams under different focal settings and performing temporal fusion.\n\nIn contrast, our work focuses on single-shot defocused event streams, which:\n\n- Require no active focus control or RGB supervision;\n- Address the case where defocus has already occurred, aiming to restore sharp content purely from event data.\n\nWe will incorporate a discussion of this difference and clarify the complementary nature of the two approaches in the final revision.\n\n---\n\n### **Runtime comparison**\n\nThanks for your suggestion. We evaluated inference speed and model size under identical settings (input: `[1, 5, 264, 352]`, GPU: RTX 3090). The results are as follows:\n\n| **Model**     | **#Params (M)** | **Inference Time (ms)** |\n|---------------|------------------|--------------------------|\n| E2VID         | 10.71            | 4.41                     |\n| ET-NET        | 22.18            | 25.71                    |\n| SPADE-E2VID   | 11.46            | 12.66                    |\n| **EvFocus**   | **12.11**        | **24.09**                |\n\nOur method offers a balance between performance and complexity. While not the fastest, EvFocus yields consistently higher-quality reconstructions. \n\n---\n\n### **Extension to changing focal planes**\n\nThanks for your advice. This is an excellent and challenging direction. Our current method assumes static defocus, but we believe it can be extended to handle dynamic focal planes by:\n\n- Incorporating temporal modulation or adaptive kernel modeling;\n- Simulating changing focal depth in training data;\n- Leveraging the temporal encoder to track evolving blur distributions.\n\nWe consider this a promising line of future work and greatly appreciate the suggestion.\n\n---\n\nThank you again for your thoughtful review and support. We are happy to clarify further if any questions remain.", "claims_and_evidence": "The method is tested on both simulated and captured event data. The method is tested against two widely used event-to-video restoration networks, which are fine-tuned with simulated defocused data. \n\nAdding an additional baseline where defocused video was restored with a conventional event-to-video architecture and then made all-in-focus with an image-to-image or video-to-video network may improve the paper.", "methods_and_evaluation_criteria": "The methods and evaluation seem appropriate.\n\nA one to two sentence description of what the Brisque and Niqe metrics are computing would improve the paper.", "theoretical_claims": "The derivations seemed correct, though I did not check carefully.", "experimental_designs_or_analyses": "Experiments seemed appropriate.", "relation_to_broader_scientific_literature": "In general, the paper presents an accurate overview of the existing literature. To my knowledge, this exact problem has not been previously addressed.", "essential_references_not_discussed": "Though the problems aren't identical, the following paper is related and should be discussed:\nLou, Hanyue, et al. \"All-in-focus imaging from event focal stack.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.", "other_strengths_and_weaknesses": "None noted", "other_comments_or_suggestions": "None", "questions_for_authors": "How do the runtimes of the various methods compare? Can the method be extended to handle events generated by a changing focal plane?", "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": "k5dfs9YH2K", "reviewer": "Reviewer_y9QQ", "summary": "This paper propose the EvFocus, the first framework designed to reconstruct sharp images from out-of-focus event streams, addressing the challenge of defocus blur, where existing event deblurring methods fail due to reduced spatial gradients and sparse event generation. The proposed approach integrates a temporal information encoder (ConvLSTM) to capture event stream dependencies, a dual-branch decoder that separately learns blur distribution and feature alignment, and a self-supervised refocusing module for optimizing blur estimation. \n\nThe authors introduce a theoretical model linking defocus blur to event generation dynamics and develop a synthetic event simulator to generate realistic training data. Experiments on both synthetic and real-world datasets demonstrate EvFocus's superior performance over existing methods, achieving up to 20% higher SSIM and 50% lower MSE under severe blur conditions.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**We sincerely thank the reviewer for the detailed and thoughtful feedback, and we carefully address the raised concerns below.**\n\n---\n\n### **Realistic 3D scenes and evaluation**\n\nThank you for pointing this out. We have conducted additional experiments on realistic 3D scenes with varying depth levels. The qualitative results are shown in the **updated figure (https://anonymous.4open.science/api/repo/Anonymous-6BB9/file/fig1.pdf?v=9bbf2ec6)**.\n\nThe results demonstrate that our model effectively learns the relationship between event streams at different defocus levels and corresponding illumination intensities. It is capable of handling varying degrees of defocus blur across different depth planes in the scene.\n\nIn addition, we conducted quantitative evaluations on these 3D scenes using standard no-reference image quality metrics:\n\n\n| **Model**   | **Brisque ↓** | **Niqe ↓** | **Noise Estimate ↓** | **Contrast ↑** | **Sharpness ↑**   |\n|-------------|----------------|------------|------------------------|----------------|-------------------|\n| E2VID+*     | 20.73          | 4.26       | 0.003874               | 0.1539         | 0.0008297         |\n| E2VID+      | 25.50          | 6.08       | 0.004410               | 0.1593         | 0.0006359         |\n| ET-NET*     | 14.94          | 6.72       | 0.006690               | 0.1550         | 0.0022420         |\n| ET-NET      | 13.31          | 4.85       | 0.006499               | 0.1541         | 0.0020085         |\n| E2VID       | 17.07          | 7.06       | 0.006361               | 0.0879         | 0.0002424         |\n| **Ours**    | **13.21**      | **4.05**   | **0.003374**           | **0.2018**     | **0.0022520**     |\n\nThese results indicate that our model consistently achieves better sharpness and contrast while reducing noise and preserving perceptual quality. We will incorporate these new results, the introduction of metrics, and the analysis in the revised version.\n\n---\n\n### **​Depth-aware feature maps & metrics**\n\nWe appreciate the reviewer’s suggestions. We agree that PSF-aware validation could provide deeper insight into the model's physical consistency. However, similar to most image-based deblurring networks, our model does not include an explicit PSF estimation module, which makes such direct evaluation difficult.\n\nThus, to evaluate the model’s performance on multi-depth defocus removal, we performed feature visualizations on 3D scenes. In the **updated figure (https://anonymous.4open.science/api/repo/Anonymous-6BB9/file/fig2.pdf?v=72d4d619)**, the result demonstrates that our model is able to distinguish between objects near and far from the focal plane, indicating that it learns depth-aware defocus representations implicitly through training. In the revised version, we will also report video-level metrics such as frame-wise stability and temporal flicker scores to better reflect temporal behavior.\n\n---\n### **Visual quality and artifacts**\n\nThank you for pointing this out. We acknowledge that some visual artifacts remain, especially in challenging areas like edges or low-texture regions. However, this task is inherently difficult; the defocused event streams are sparse, noisy, and lack full spatial information, making accurate reconstruction very challenging.\nDespite this, our method achieves significantly better perceptual quality than existing baselines, as shown by multiple no-reference metrics in the tables. We will add more 3D scene results in the revised version.\n\n---\n### **Model and Dataset Design**\n\nThank you for the comments.  \nOur synthetic dataset is built on a physically-inspired defocus model that simulates event generation under varying PSF sizes and lighting conditions. By changing blur levels, motion speed, and intensity across sequences, we mimic different scene depths and real-world defocus effects.\nThis design allows the model to learn both temporal accumulation and spatial defocus patterns, and to adapt to the link between blur size and event sparsity, making it effective for depth-varying defocus.\n\nWe conducted experiments on multi-depth scenes and observed that E2VID pretrained on sharp data can handle slight defocus, suggesting that events exhibit robustness to slight defocus blur. Combined with our specially designed dataset, our model further learns depth-dependent defocus patterns and can distinguish and reconstruct objects at different depths, showing strong generalization to 3D scenarios.\n\n---\n\n### **Related work**\n\nThank you for this suggestion. We will discuss relevant references (e.g., Lou et al., 2023; Lin et al., 2022; Teng et al., 2024) on event-driven autofocus and focal stack fusion. Our method differs in that it does not require active lens control or RGB images, and is designed for passive, single-shot defocused event streams, which we believe complements autofocus-based methods.\n\n---\n\nThank you again for your valuable feedback. If there are any additional questions, we would be happy to address them.", "claims_and_evidence": "The paper proposes the **EvFocus** framework as an innovative solution for defocus deblurring in event cameras. However, some claims are not fully supported by sufficient evidence. Experimental results show that EvFocus significantly outperforms baseline models (E2VID, ET-Net) on synthetic data in quantitative metrics such as SSIM and MSE. Ablation studies further validate the necessity of the dual-branch decoder and re-defocus module, as removing them leads to a **15%-20% performance drop**. \n\nHowever, the **visual quality of the reconstructed images remains problematic**. As shown in **Figure 7**, the reconstructed images exhibit artifacts, blurred edges, and texture loss, particularly at the edges of the checkerboard pattern, where fine details are not well restored. Although no-reference image quality metrics (Brisque/Niqe) indicate that EvFocus performs better than competing methods, the **lack of sharpness and artifacts in real-world results may limit its practical applicability**. \n\nFurthermore, the paper does not include **experiments or validation on videos**, which weakens the completeness of the experimental evaluation.", "methods_and_evaluation_criteria": "​1. Simplistic Defocus Modeling\n\nThe synthetic dataset uses ​fixed Gaussian kernels to approximate defocus blur, ignoring the ​nonlinear depth-PSF relationship (Equation 4). Real-world PSFs are often asymmetric and aberration-affected, creating a ​domain gap between simulation and reality. This undermines the model’s ability to generalize to ​multi-depth scenes (e.g., foreground-background defocus).\n\n2. ​Unrealistic Dataset Design\n\nBlur kernel sizes are arbitrarily selected rather than tied to ​physical depth parameters (e.g., focal length, aperture). This decouples blur from its root cause (distance), failing to capture ​dynamic blur variations in moving scenes (e.g., objects approaching the camera).\n\n​3. Incomplete Evaluation Metrics\n\nMetrics like SSIM/MSE measure generic image quality but ​ignore depth-aware reconstruction accuracy. No validation of ​blur kernel estimation (e.g., PSF size vs. theoretical predictions) is provided, leaving the model’s ​physical consistency unproven.\n\n4. ​Lack of 3D Scenario Testing\n\nExperiments focus on static/global defocus, neglecting ​dynamic depth changes (e.g., objects moving along the optical axis) and ​multi-layer defocus (e.g., mixed foreground/background blur). This casts doubt on real-world applicability.\n\n- Real-world validation is insufficient: The paper only visualizes results on a ​single checkerboard pattern (Fig. 7), which lacks complexity. No examples are shown for ​3D scenes with layered depth (e.g., cluttered indoor environments, outdoor scenes with foreground/background separation), raising doubts about practical utility.", "theoretical_claims": "The paper’s theoretical justification for its ​dual-branch decoder and re-defocus module relies solely on ​quantitative metrics, failing to provide ​mechanistic evidence that these components learn physically meaningful defocus properties. For instance, there is no ​visualization of blur kernel estimation (e.g., PSF size variations across depth in 3D scenes) to confirm that the blur-aware branch captures depth-dependent defocus patterns. Without such analysis, it remains unclear whether the network truly models the ​distance-blur relationship (as implied by Equation 4) or merely exploits superficial correlations in synthetic data. A rigorous validation would require ​depth-aware feature maps (e.g., showing how PSF scales align with object distances) and ​controlled 3D experiments. Until then, the theoretical claims lack ​causal grounding.", "experimental_designs_or_analyses": "The experiments fail to establish a physically grounded link between defocus blur and 3D scene geometry. Synthetic data relies on fixed blur kernels (e.g., 3×3 to 11×11), arbitrarily assigned rather than derived from depth-dependent PSF modeling (Equation 4). This decouples blur from object distance, creating a non-physical training regime.\n\nReal-world evaluations exacerbate this issue—the checkerboard test lacks multi-depth layers or dynamic depth variations, leaving the model’s 3D-aware defocus handling unverified. Without depth maps or moving objects along the optical axis, the experimental validity remains in question.", "relation_to_broader_scientific_literature": "None", "essential_references_not_discussed": "The paper provides a clear background and related work discussion, but it overlooks prior event-based auto-focus methods, which are relevant to its scope. Several studies have explored event-driven autofocus mechanisms, yet they are not explicitly discussed here. If the authors were to acknowledge these works and clarify that their approach specifically targets scenarios where autofocus fails, it would strengthen the motivation and positioning of the paper within the broader literature.", "other_strengths_and_weaknesses": "I highly appreciate the paper's focus on event-based defocus deblurring, which is a novel and valuable research direction for the event-based vision community. \n\nHowever, the primary weakness lies in the lack of realistic 3D scenes and evaluation, which limits its practical applicability. If the authors can address this gap, I would be very supportive of the paper being presented at ICML (maintaining its current weak accept score).", "other_comments_or_suggestions": "NONE", "questions_for_authors": "NONE", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper received three positive initial reviews. There was general consensus about the importance of the problem considered (video reconstruction from event streams under motion blur), the technical soundness of the proposed solution, and the clarity of presentation. There were some concerns raised about experimental evaluations (e.g., baseline comparisons, choice of metrics, and overall visual quality of the reconstructed results). 

The authors provided a comprehensive response, after which two of the reviewers maintained positive (accept) decisions. 

One of the reviewers, however, lowered their score citing low computational efficiency of the proposed method (and some other technical concerns). These concerns, while valid, are not considered prohibitive. 

Therefore, on balance, an accept decision was reached.
Decision: Accept (poster)