Asymmetric Dual-Lens Video Deblurring
Authors: Zeyu Xiao
Xinchao Wang
Venue: NeurIPS 2025 poster ()
Keywords: Image deblurring
Video deblurring
Primary area: applications

Abstract: Modern smartphones often feature asymmetric dual-lens systems, capturing wide-angle and ultra-wide views with complementary perspectives and details. Motion and shake can blur the wide lens, while the ultra-wide lens, despite lower resolution, retains sharper details. This natural complementarity offers valuable cues for video deblurring. However, existing methods focus mainly on single-camera inputs or symmetric stereo pairs, neglecting the cross-lens redundancy in mobile dual-camera systems. In this paper, we propose a practical video deblurring method, AsLeD-Net, which recurrently aligns and propagates temporal reference features from ultra-wide views fused with features extracted from wide-angle blurry frames. AsLeD-Net consists of two key modules: the adaptive local matching  (ALM) module, which refines blurry features using $K$-nearest neighbor reference features, and the difference compensation (DC) module, which ensures spatial consistency and reduces misalignment. Additionally, AsLeD-Net uses the reference-guided motion compensation (RMC) module for temporal alignment, further improving frame-to-frame consistency in the deblurring process. We validate the effectiveness of AsLeD-Net through extensive experiments, benchmarking it against potential solutions for asymmetric lens deblurring.

Asymmetric Dual-Lens Video Deblurring
Zeyu Xiao
Xinchao Wang"
National University of Singapore
zeyuxiao@nus.o www.:/uuuuuuuue.e...... sg,
xinchao@nus. www.ooduuussuuu..g....
Abstract
Modern smartphones often feature asymmetric dual-lens systems, capturing wide- angle and ultra-wide views with complementary perspectives and details. Motion and shake blur the wide lens, while the ultra-wide lens, despite lower resolution, retains sharper details. This natural complementarity offers valuable cues for video deblurring. However, existing methods focus mainly single-camera inputs or symmetric stereo pairs, neglecting the cross-lens redundancy in mobile dual- camera systems. In this paper, we propose a practical video deblurring method, AsLeD-Net, which recurrently aligns and propagates temporal reference features from ultra-wide views fused with features extracted from wide-angle blurry frames. AsLeD-Net consists of two key modules: the adaptive local matching (ALM) module, which refines blurry features using -neeersst neighbor reference features, and the difference compensation (DC) module, which ensures spatial consistency and reduces misalignment. Additionally, AsLeD-Net uses the reference-guides motion compensation (RMC) module for temporal alignment, further improving frame-to-frame consistency the deblurring process. We validate the effectiveness of AsLeD-Net through extensive experiments, benchmarking it against potential solutions for asymmetric lens deblurring.
Blurry frames
Introduction
Wide Lens
Modern smartphones increasingly feature asymmetric dual-lens systems, combining ande- and ultra-wide lenses to capture scenes with complementary perspectives and details (see Figure 1). However, during dynamic scenes or camera shake, focal length and aperture dif- ferences introduce blur discrepancies. For example, the iPhone 14 Plus, ultra-wide lenses typically have shorter fo- cal lengths (e.g., 13mm) and smaller apertures (e.g., f/2.4) compared to wide-angle lenses (e.g. 26mm, f/1.5). The shorter focal length of ultra-wide lenses results in deeper depth of field, reducing defocus blur but making them more prone motion blur due to the longer exposure time required for sufficient light intake. In contrast, wide-angle lenses with larger aperture capture more light, allowing for faster shutter speeds and reducing motion blur while maintaining shallower depth of field that enhances complementary characteristics provide valuable cues for video deblurring, as shown Figure 2.
Ultra-wide
lens
iPhone
Reference fromes
"How did we get such huge camera upgrade to fit?
"By thinking diagonally" Apple
Figure 1: A typical dual-lens camera sys- on iPhone in practice.
separation. These
Existing video deblurring methods primarily focus on single-camera inputs or symmetric stereo pairs. For instance, flow-guided bidirectional propagation methods [8, 21, 40] align features using deformable convolutions and attention mechanisms, while VRT and RVRT [37, 38] employ spatio- temporal self-attention aggregate information across video frames. However, these methods do not leverage the cross-lens redundancy inherent in mobile dual-camera systems. Although several
*Corresponding Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

(a) Ultra-wide lens views (Reference frames)
(b) Wide lens views (Blurry frames)
Trajectorics blurry f. (d) Trajectories of ref.
(c) Optical flow of ref. f.
(f) Optical flow of ref. f.
On
(g) Blurry frames
(h) Ground truth
DSTNet
(i) AsLeD-Net (Ours)
Figure 2: Examples of asymmetric dual-lens video deblurring. (a) Reference frames captured using the ultra-wide lens. (b) Blurry frames captured using the aide-angle lens. (c-d) Motion discrepancy visualization through trajectory comparisons (Please zoom in and best viewed on the screen). (e-f) Optical flow visualization of blurry and reference frames. (g) Input blurry sequences. (h) Ground truth. (i) Results from state-of-the-art method (DSTNet) [52]. () Our AsLeD-Net results. In this figure, "f." short for frames, and "ref." is short for reference, respectively. We propose the first deep learning method for asymmelric dual-lens video deblurring, where ultra-wide lens videos (shorter focal length) provide motion priors restore clear wide-lens blurry sequences. This is motivated by the observation that ultra-wide lenses inherently suffer less motion blur due to their focal properties: shorter focal lengths reduce perceived motion parallax, enabling more stable scene capture despite the lower resolution. AsLeD-Net delivers high deblurring accuracy with strong temporal coherence.
image restoration techniques have been developed for dual-camera setups, including dual-lens super-resolution [70, 94, 31, 84, 28, 69, 7, 76, 85, 95, 53] and dual-lens stereo matching [12, 64, 90], as well reference-based image super-resolution [82, 62, 44, 6, 93, 56, 23, 24, 20, 29, 92, 25), video deblurring in an asymmetric dual-lens setting remains unexplored. In this paper, we introduce Asymmetric dual-Lens video Deblurring (AsLeD), which exploits the complementary information between blurry and reference videos captured from asymmetric dual-camera systems. Unlike traditional video deblurring approaches, AsLeD explicitly considers the structural disparities between the two views to enhance the restoration process.
AsLeD effectively models the relationship between blurry and reference frames in asymmetric dual-lens settings. At each time step, the two frames share nearly identical content within their overlapping field of view (FoV) (top and middle rows of the leftmost column in Figure/ 2). As the video progresses, neighboring reference frames provide sharp details that help recover regions beyond the overlapped FoV (bottom row of the leftmost column in Figure 2). By leveraging these cross-frame correspondences, AsLeD enhances video restoration by utilizing multi-frame redundancy asymmetric multi-camera setups.
eD--Nt employs a dual-branch architecture built upon the BasicVSR framework to exploit asymmetric dual-lens characteristics. The base branch uses bidirectional recurrent design [8, 21] for temporal feature propagation, while the reference branch processes ultra-wide lens inputs with reduced motion blur. This asymmetric design incorporates three key modules in the reference branch: (1) The adaptive local matching (ALM) module: Establishes semantic- aware correspondences between blurry frames (base branch) and clear reference frames (ultra-wide branch) using K K-nearest neighbor feature aggregation, effectively transferring structural details. (2) The difference compensation (DC) module: Bridges feature discrepancies between the branches, preserving edge sharpness fast-moving regions and enhancing spatial-temporal consistency. (3) The reference-guides motion compensation (RMC) module: Aligns features across time steps using optical flow guided by ultra-wide reference frames, climinating cumulative errors in traditional flow-based warping. The base branch progressively

---

fuses ALM-refined features, DC-compensated residuals. and RMC-aligned references via attention- guided fusion blocks. This hierarchical integration enables the model leverage the base branch's reconstruction capabilities and the reference branch's blur-insensitive motion priors. As shown in Figure 2, AsLelD-Net outperforms DSTNet [52], enhancing texture details like the text on the wall and the coffee shop logo while preserving strong temporal coherence.
The contributions of this work are summarized follows: (1) We formalize asymmetric dual-lens video deblurring (AsLeD), leveraging cross-lens redundancy from a sharper ultra-wide reference to assist a blurry wide view, extending prior dual-lens image deblurring to the video setting. (2) We present AsLeD-Net with three task-specific modules: ALM for structure-aware K- K-nearest-neighho reference aggregation, DC for cross-view spatial consistency, and RMC for temporal alignment. (3) AsLeD-Nee -et achieves superior quantitative and qualitative results on AsLeD.
2 Related Works
Video deblurring. Unlike image deblurring [48, 67, 87, 79, 77, 35], video deblurring benefits from
spatio-temporal cues to enhance restoration quality [51, 33, 99, 98, 88, 72, 66, 80, 10, 21, 22, 49, 65, 81, 37, 58, 89]. Deep soariing-based methods [3, 2, 97, 96, 54, 55, 36, 78] have become the dominant approach. Recurrent video deblurring models exploit temporal dependencies for progressive feature propagation [98, 91, 72, 10, 39, 49, 52, 32]. For example, STRCNN [27], RDN [74], and IFRNN [49] adopt recurrent architectures for sequential feature refinement. STEAN [100] employs dynamic filters, while PVDNet [63] integrates blur-invarian flow estimator. Recent methods leverage bidirectional propagation for improved restoration [8. 101. 21, 40, 88]. BasicVSR++ [8] introduces aggressive bidirectional propagation, while RNN-MBP [101] incorporates multi-scale bidirectional updates. However, error accumulation remains challenging for long-range temporal modeling [21]. Spatio-temporal transformers enhance video deblurring by capturing long-range dependencies [37, 40, 39, 37, 38). Recently, Zhang et al. [89] propose a spatio-temporal sparse Transformer for efficient video deblurring.
Dual-Iens image restoration. Asymmetric dual-lens systems, commonly found in smartphones, consist of an uldra-wide lens and wide lens with different focal lengths. These systems capture the same scene with varying Vs.s. Typically, the ultra-wide lens (short focal length, large FoV) is the main lens, while the wide lens (longer focal length, narrower FoV) provides higher resolution within the overlapped FoV. This configuration enables various vision tasks by leveraging the complementary imaging capabilities of the two lenses. Previous works have explored dual-lens systems for image refocusing [1], correspondence estimation [12, 64, 90] and image super-resolution [70, 94, 31, 84, 28, 69, 7, 76, 85, 95, 53], where telephoto images serve high-resolution references enhance wide-angle images. Beyond image super-resolution, other tasks such as image deblurring [46, 61, 59], novel view synthesis [75, 83, 45], high-dynamic-range imaging [34, 60], and image colorization [15] have also been investigated, demonstrating the practical benefits of cross-lens redundancy. Among prior studies, the most similar to ours are Mohan et al. [46], who deblur static dual-lens image pairs by enforcing cross-view and depth consistency in unconstrained capture settings. Lai et al. [30] deblur face regions in still photos using synchronized sharp ultra-wide reference. We extend this line from images to video by integrating siterence-guider cross-view alignment with recurrent temporal propagation to preserve frame-to-fran to-mme coherence.
Reference- image restoration. Another related topic is reference-basee image restoration, which includes tasks such reference-based image super-resolution [82, 62, 44, 6, 93, 56, 23, 24, 20, 29, 92, 25], image deblurring [41, 42) and burst image restoration [14, 19, 4, 26, 16, 5, 17, 73, 18, 43, 29]. These tasks leverage auxiliary high-quality images enhance the restoration of a degraded input. In this work, we address reference-batte dual-lens smartphone cameras. Unlike existing approaches focusing on still images, our method utilizes cross-lens redundancy in videos to achieve high-quality deblurring, effectively integrating information from both lenses.
video deblurring in the context of asymmetric
3 Method
3.1
Overview
The proposed AsLeD-Net reconstructs a high-quality video Ý RT*Hxxxx from a blurry input IB x xxxx3xx33 captured with wide lens, leveraging relatively sharper reference video
3

---

Branch
Fusion net
h
ENC
Enc
ALM
Blurry Frome Feature Encoder
Ref
EnC
[-1
RGMC
Ref Branch
Reference Fromet Feature Encoder
Base Branch
Fusion net
1
Ench
Adoptive Local
Motching
Offference Compersation
Den
Frame Feature Decoder
nef
Enc
sefeerece-gguider
Mation Compensation
Element-wise Addition
Ref Branch
Base Branch
Fusion net
4
Cancat
t+1
Enc
ALM
Forward Propogation
DC
Dac
Ref
t+1
Enc.
RGMC
Bockward Propogation
Ref Branch
0
Figure 3: Overview of the proposed AsLeD-Net. AsLeD-Net processes blurry video frames from wide lens and reference frames from an ultra-wide lens using dual-branch architecture. The base branch reconstructs the blurry frames with bidirectional frame propagation [9], while the reference branch extracts asymmetric information from the ultra-wide frames. Features from both branches are encoded separately and then refined by ALM, DC, and RMC modules. The concatenated features merged through residual blocks and fed to the frame decoder to generate the final clear output.
IRes E RTxH*W*x taken simultaneously by an ultra-wide len in a dual-lens camera (e.g., iPhone). The objective is to restore Ý to closely match the ground truth I effectively exploiting the complementary information from the reference view. Here, T. H, and W denote the number of frames, height, and width, respectively. Following previous video deblurring and restoration methods [8, 9]. we adopt a bi-directional recurrent architecture for its simplicity and efficiency. The overall framework of our proposed AsLeD-Net is shown in Figure 3.
The base branch, identical to Basic VSR, reconstructs blurry videos I captured with wide lens, using bidirectional frame propagation based on optical flow to extract features Bose, To exploit the unique properties of the AsLel) task, we introduce second reference branch, which processes ultra-wide lens inputs with reduced motion blur. Since reference and blurry frames have different FoV and contain varying information, blurry frame size. We leverage the reference branch to extract asymmetric reference information despite potential colour discrepancies due lens differences.
center-crop and resize the reference frames match the
For the blurry frame 1. and its corresponding reference frame T.Ref time I, first feed them separate feature encoders with non-shared weights to obtain feature representations F.B and F.Ref each consisting of N₃ residual blocks. These features are then processed by the ALM, DC, and RMC modules, which transfer structural details from the reference frames, bridge feature discrepancies while preserving edge sharpness in fast-moving regions, and align features across time steps using optical flow guided by the ultra-wide reference frames. After concatenation, the outputs FALM FDC. and PRMC are merged with the base branch's 's output P.Base by feeding them to residual blocks, resulting in F,. Finally, F, is input into a frame decoder, composed of N2 residual blocks, to generate the final reconstructed result I,, with a residual connection from the input added.
Our approach does not assume perfect geometric calibration or strict cross-view alignment. Instead, AsLeD-Net learns content-aware feature alignment that tolerates cross-lens baselines and photometric shifts, enabling reference guidance under realistic dual-lens configurations. In practice, we retain cross-view geometric and photometric discrepancies and rely on content-aware, data-driven feature alignment, while ensuring frame-level temporal synchronization.

---

3.2 Adaptive Local Matching
Motion blur erases high-frequency details and disrupts spatial coherence, leading to texture ambiguity and structural distortions. Recovering these details is particularly challenging due to the ill- ill-posed nature of the problem. To address this issue, we exploit the complementary information available in reference frames, which are captured under similar conditions but with less no blur. These frames retain rich structural cues that can guide the restoration of the blurry frame, providing more robust mechanism for handling local details. The core idea is to adaptively leverage the reference frame's features based on local context, enabling more accurate restoration the blurred image.
Without loss of generality, we focus on the time step to illustrate the ALM module. Given the extracted blurry frame feature F.P E Cx HxW at timestamp t, the ALM module aims refine these blurry local features by incorporating the complementary reference feature FRRe/ E RC x x captured under similar conditions but with less blur. These deep features of H x W local representations, each of size C-dimensional. To match the blurry frame with the reference features, first compute similarity map S, to measure the cosine similarity between each blurry local feature F:B(() and each reference feature F RR)(()
be represented
set
F,B(1)
F RefG)
St (( cos (Fs(F.. (F Ft R.Rafl5))
||
||
-| 1I
where i E (l....,Ng. N and j E (I,.... N,..,, ). and N₂P and N.Ros represent the number of local representations in the blurry and reference frames, respectively, s(t,)) represents the similarity between the i-th blurry local representation and the j-th reference local representation.
For each blurry local representation, select its K-nearest neighbors from the reference features and fuse these local representations into a reference representation ttref
((()
K
(R) at
x F. (k)(())
itee
=
where Σx1 representations then adaptively refine the corresponding blurry features. Finally, the blurry and reference representations are concatenated and processed by an adaptive layer go to obtain the fused local representations
(k) 1, 1, 0, and Or
(k) is obtained using the softmax function. The reference
k= dz
FALM = ((
(3)
where Øt. represents the fused local representations, and denotes the adaptive fusion layer. The cosine similarities in Eq. (1) form query-reference affinity used to rank reference tokens; the top- are aggregated with poftmax-normallnn
weights. The adaptive layer ge in Eq. (3) is a lightweight 1x1 convolution applied to the concatenated [dr,ref, P.), enabling content-aware fusion with low overhead. This process facilitates information fusion by incorporating relevant reference features to enhance the blurry frame representation.
Residual Block
3.3 Difference Compensation
FB
et
Conv
ReLU
Cone
Rusichel Block
PCC
In AsLeD, while blurry and reference images share structural consistency, focus, exposure, and sensor processing differences create subtle feature mismatches, making direct concatena- tion suboptimal. The DC module provides simple yet effective solution by adaptively modeling residual information. It ensures that only benefi- cial details aggregated while preserving spatial consistency. Unlike complex alignment- approaches, the DC module efficiently refines blurry features with relevant reference information, preventing artifacts and enhancing deblurring with minimal computational overhead.
Fe
Ref
Figure Overview of the difference compensation (DC) module.
Given the blurry frame feature F.B and its corresponding reference frame feature F.Re1 time I, the goal of the DC module refine the blurry features by effectively leveraging reference information. As shown in Figure 4, predict the difference between the blurry feature F. and the reference
5

---

feature F.Ref, enhancing the residual information e, to facilitate effective feature aggregation and adaptive refinement. The difference compensation between R.eef and FRRe be summarized as 3.4 Reference-Guided Motion Compensation
Ct F. FRRe è f(e),, FPC F. FB+ &e.
where e, represents the residual feature that captures useful differences between the blurry and reference features, and f()) denotes the cascaded residual blocks. This progressive refinement adaptively integrates reference information while preserving spatial consistency, ensuring effective feature aggregation for improved deblurring.
In the base branch of AsLeD-Net, opti- cal flow computed between the current and previous blurry frames for warping. While this approach has its merits, of- ten introduces limitations that degrade de- blurring performance. Specifically, both the current and previous blurry frames may contain significant motion blur and noise, leading to inaccuracies in optical flow estimation. These imperfections can cause misalignment during warping, resulting in artifacts or an inability to capture fine motion details, ultimately reducing the quality of deblurring.
Ref
r+1
SpyNet +
Init t11
Resichal Blocks
1
Warping
F RMC
Figure 5: Overview of the reference-guided motion com- pensation (RMC) module.
To address these issues, we propose the RMC module, which estimates optical flow between the current blurry frame and a sharper reference frame (previous or next) rather than between consecutive blurry frames. leverages two complementary motions: temporal flow within the base view and cross view flow guided by the sharper reference; the latter accounts for differences in field of view, perspective, and resolution, more reliable under heavy blur, and reduces misalignment to improve motion tracking. As is shown in Figure 5, given the current blurry frame 1," and the previous reference frame 1, we first use SpyNet [57] estimate the initial optical flow Of between these two frames. However, this initial flow estimate may not be sufficiently accurate due to factors such as noise, motion blur, or misalignment, which can affect the precision of the optical flow calculation. To improve the flow estimate, we concatenate the current blurry frame I and the initial flow estimate O, then feed this concatenated pair residual blocks to refine the flow. This refined flow is expected to capture fine details and correct the misalignment introduced by the initial estimation
F SpyNet(I, 1B
Offefiedd f o!rit)-
(5)
The refined flow is not directly supervised; is learned implicitly via end-to-end reconstruction loss, in line with prior video restoration designs where flow acts auxiliary representation facilitate motion compensation rather than a target itself.
After refining the optical flow, we perform warping using the refined flow and the current blurry frame 1 The result is then combined with the residual information to obtain the motion-ccomeenstee feature FRMC which is more accurate in capturing the motion and structural details of the scene. This process ensures that the reference frame's 's motion effectively transferred to the blurry frame, improving alignment and reducing artifacts. The above process can be formalized as
FRMC warp(I," O ORefined + +(
By refining the optical flow and leveraging the residual information, the RMC module ensures more precise alignment between the current blurry frame and the reference frame, leading to improved deblurring performance.
4
Experiments
4.1 Experimental Settings
Datasets. We use the RealMCVSR dataset [31] for our experiments. Originally designed for multi- view video super-resolution, RealMCVSR consists of triplets captured with ultra-wide, wide-angle, and telephoto lenses. In this setup, the wide-angle and telephoto videos share the same spatial dimensions the ultra-wide video but have x2 and x higher resolutions, respectively. For the

---

AsLeD task, we adapt the dataset by using the ultra-wide video as the reference input and the wide- angle video as the blurry input. Since the wide-angle video has twice the resolution of the ultra-wide video, we center-crop and upsample the ultra-wide frames to match its resolution. We follow the original data split of the RealMCVSR dataset. To simulate motion blur, we generate blurred frames by the widely used technique of averaging multiple consecutive frames of the video captured by different lenses [65, 48, 47, 71, 50], We generate blurry frames for training by averaging every 7 consecutive frames, simulating varying motion blur intensities. We synthesize motion blur by averaging seven consecutive frames for both training and testing, a widely used approximation that does not fully model real-world blur. For real captures, we use iPhone 14 Plus whose two lenses introduce physical baseline and distinct imaging characteristics, yielding cross-view shifts and photometric differences. We intentionally avoid spatial color pre-alignment and ensure frame-level temporal synchronization with the DoubleTakke APP, Note that RealMCVSR is captured on iPhone 12 Pro Max, whereas our real tests use iPhone 14 Plus, introducing cross-device differences in optics and ISP to assess generalization. Evaluations on these real videos demonstrate robust generalization beyond the averaging assumption.
Implementation details. We use 7 frames as input during training, with a mini-batch size of 4 and an input frame resolution of 128 x 128. We apply data augmentation techniques to the training data, including horizontal flips and random rotations of 90º, 180", and 270". AsLeD-Nec is trained for 300K iterations using the Adam optimizer with Cosine Annealing learning rate scheduler. Network architecture parameters are set to N1 and N2 = 30. The number of channels is 64, and K in ALM
is set to 3. Supervision is enforced using the Charbonnier loss [71] via L. = VIL. - ZGT 1|2 + s2, where e is set to x 10-3 in our experiments. We omit the subscript for simplicity. The initial learning rate for AsLeD--ett is x 10-1 Training is conducted on an NVIDIA RTX 3090 GPU.
Inference settings. We evaluate the reconstructed results using PSNR, SSIM, and LPIPS on the RGB channels to assess fidelity and perceptual quality.
4.2 Quantitative and Qualitative Comparisons
We compare the proposed AsLeD-Net against diverse set of baseline methods to evaluate its effectiveness for the AsLeD task. These baselines encompass wide range of potential approaches, aiming to cover many varied and rich method- ologies as possible: (1) Single-image de- blurring and restoration methods: includ- ing MIMOUNet [13], MIMOUNet+ [13],
Table 1: Quantitative evaluation on the RealMCVSR
testset. We mark the best and the second best results in bold and underline, respectively. #Params means the number of network parameters (M). Time costs (ms)
are measured on blurred frames with resolution of 256 x 256 using an NVIDIA GTX 1080 Ti GPU.
RealMCVSR
Costs
Method
PSNR
SSIM
LPIPS]
#Params
Time
Blur frame
18.22
0.8009
0.4319
NAFNet [11]. and Restormer [86]. (2)
Video deblurring and restoration meth-
MIMOUNet
24.50
0.8937
0.4042
MIMOUNet++
24.70
0.8894
0.4074
16.1
47.4
ods: including IFIRNN [49], DBN [65].
NAFNet
24.80
0.9047
0.4109
67.9
52.9
EDVR [71], VSR 191, and Ba-
Restormer
24.90
0.9045
0.3875
116.9
sicVSR++ [8]. (3) Reference-based
IFIRNN
24.75
0.8984
0.3434
4.1
7.6
DBN
25.05
0.9050
0.3763
15.3
4.2
video restoration method: specifically Re-
EDVR
25.15
0.9088
0.3773
23.6
320.1
FVSR [31]. Notably, some of these meth- ods are not initially designed for deblur- ring. For approaches primarily intended for super-resolution tasks, we remove the upsampling operation and instead apply a convolutional layer to generate the final output directly. We meticulously re-train all baseline methods on our dataset to ensure a fair and comprehensive comparison using the publicly available code. How- ever, due to limited computational resources (with only 3090 GPU and 1080 Ti GPUs available), we cannot reproduce specific potential video deblurring methods. We plan to incorporate more advanced and computationally demanding methods when sufficient computational resources are available.
BasicVSR
25.10
0.9117
0.2885
6.2
55.8
BasicVSR++
25.60
0.9121
0.2207
9.5
57.1
DSTNet
25.70
0.9134
0.2604
RefVSR
25.58
0.9124
0.2156
4.8
268.3
Ours
26.34
0.9167
0.1614
8.8
248.7
Quantitative results. As shown in Table 1, our proposed AsLeD-Net consistently achieves the best performance across PSNR, SSIM, and LPIPS metrics. Our method outperforms the second-best DSTNet by 0.64 dB PSNR (26.34 vs. 25.70 dB), demonstrating superior noise reduction and detail preservation. Compared to widely-used methods like BasicVSR+ (25.60 dB) and EDVR
7

---

Blur frame
Restormer
NAFNet
EDVR
DBN
ESR
Reference frame
Blur frame
DSTNet
AsLeD -et (Ours)
Ground truth
Figure 6: Qualitative comparison of AsLeD performance the RealMCVSR dataset.
FR.
+
FR.
FR.
Blur frame
Restormer
NAFNet
FR.
FR.
FR.
Reference frame
EDVR
DBN
VSR
FR.
FR.
F
Blur frame
DSTNet
AsLeD-Net (Ours)
Ground truth
Figure 7: Qualitative comparison of AsLeD performance real-world blurry scenes captured using
an iPhone 14 Plus. More results can be found in the supplementary material.
Gronn
Figure 8: Temporal consistency comparison the AsLeD task, where temporal profiles extracted
along horizontal (in blue) and vertical (in green) directions of the reconstructed frames.
Ground truth frame
Blur frame O. F.
Restormer O.
NAFNet O. E.
EDVR O.
DBN O. F.
BasicVSR++ O.
DSTNet O. AsLeD-Net (Ours) Ground truth
Figure 9: Temporal consistency comparison on the AsLeD task. The optical flow (O. F.) are estimated using the pre-trained RAFT [68].
(25.15 dB), approach achieves gains of 0.74 dB and 1.19 dB. respectively. For SSIM, AsLeD-Net achieves 167, improving upon DSTNet (0.9134), BasicVSR++ (0.9121), and ReiVSR (0.9124), indicating enhanced structural consistency. Moreover, (0.1614), significantly surpassing the closest competitor, RefVSR (0.2156), with a 0.0542 reduction, demonstrating the perceptual quality of our restored frames.
method sets new benchmark in LPIPS
Computational cost results. As shown in Table 1, our AsLeD-Net achieves superior performance on the ReaIMCVSR dataset but comes with higher model complexity (8.8M parameters) and longer inference time (248.7 ms) compared lightweight models like IFIRNN (4. M,, 7.6 ms) and DBN (15.3M M. 4.2 ms). This limitation hinders its applicability in real-time resource-constraing

---

scenarios. This limitation indicates that our method might not be optimal for real-time or resource- constrained applications. future work, we plan to explore model compression techniques, such as knowledge distillation and lightweight architecture designs, to reduce computational costs while maintaining high restoration quality.
Qualitative results. We present visual comparison results on the RealMCVSR dataset in Figures and 7. AsLeD-Net excels in preserving fine texture details, consistently outprrorriigg methods such as Restormer, NAFNet, EDVR, DBN, DSTNet, and BasicVSR++, It effectively retains architectural details, such as grid-like window patterns and building facades, which are often blurred by other methods. As shown in Figure 7, AsLeD-Ne D-eet produces sharper, more legible Chinese characters on signs and enhances clarity in real-world iPhone 14 Plus captures, revealing finer details in street signs and logos, thus improving text readability and recognition for practical applications.
Temporal consistency. We present the temporal consistency analysis in Figure 8 and Figure highlighting the superior performance of our method in preserving motion coherence and fine details across consecutive frames. Compared baselines, our method maintains smoother transitions and finer details in the temporal profiles, ensuring better motion consistency and reducing artifacts such as flickering. The estimated optical flow using RAFT [68] closely matches the ground truth and exhibits sharper, more well-defined structures than baselines. This demonstrates our model's ability to capture temporal coherence.
Table 2: Ablation study of three core components in AsLeD-Net on RealMCVSR. We replace the removed components with the residual blocks, ensuring parameter consistency.
4.3 Ablation Study
We conduct experiments on RealMCVSR in terms of PSNR/SSIM. Due space limitations, please refer to the supplementary material.
Core Components
RealMCVSR
Method
ALM
DC
RMC
PSNR
SSIM
Table 2 ablates the three core components. Each
single module improves the baseline (25.40 PSNR / 0.9120 SSIM). RMC yields the largest single module gain (25.79 / 0.9143). Adding RMC to ALM or DC brings further gains, and using all three achieves the best result (26.34 / 0.9167). This shows that temporal alignment is pivotal, while ALM and DC provide complementary spa-
(a)
x
25.40
0.9120
(b)
25.72
0.9137
(c)
x
/
25.69
0.9138
(d)
x
X
25.79
0.9143
(e)
x
25.89
0.9145
(f)
/
25.84
0,9140
(g)
/
25.75
0.9139
(h)
26.34
0.9167
tial refinement. Within context aggregation, our
ALM with & = 3 attains the best PSNR/SSIM and outperforms DAT and MASA SR under the same protocol. For latency sensitive settings, an ALM+RMC variant offers favorable speed and quality tradeoff. Notably, ALM+DC without RMC (25.75/ / 0.9139) yields only limited uplift, indicating that temporal alignment is the dominant bottle- neck under realistic motion. Taken together with the ALM study (where h ==3 peaks), these results suggest that structure aware reference matching and temporal propagation mitigate complementary error modes, namely cross view misalignment and blur induced motion ambiguity. and that a reduced ALM+RMC configuration a practical choice when latency critical.
Conclusion
In this paper, we propose AsLeD-Net, a practical video deblurring method that leverages the com- plementary perspectives of asymmetric dual-lens systems. By aligning and propagating temporal reference features from ultra-wide views and fusing them with blurry wide-angle frames, AsLeDD-Ntt effectively addresses cross-lens redundancy. Key modules such as the ALM module and the DC module ensure refined feature alignment and spatial consistency, while the RMC module enhances temporal alignment. Through extensive experiments, we validate the effectiveness of AsLeD-Net, showcasing its superiority over existing methods for deblurring asymmetric lens systems.
Our method performs well across various blur levels and scenarios, but frame averaging for training has limitations in simulating real-world motion blur, especially in high-motion and high-contrast scenes, and is frame rate-dependent; see the supplementary material for details.
Acknowledgment. This project is supported by the National Research Foundation, Singapore, under its Medium Sized Center for Advanced Robotics Technology Innovation.

---

References
[1] Hadi Alzayer, Abdullah Abuolaim, Leung Chun Chan, Yang Yang. Ying Chen Lou, Jia-Bin Huang, and Abhishek Kar. De2: Dual-camera defocus control by learning to refocus. CVPR, 2023.
[2] Haowen Bai, Jiangshe Zhang, Zixiang Zhao, Yichen Wu, Lilun Deng, Yukun Cui, Tao Feng, and Shuang Xu. Task-driven image fusion with learnable fusion loss. In CVPR, 2025.
[3] Haowen Bai, Zixiang 7hao, Jiangshe Zhang, Yichen Wu, Lilun Deng, Yukun Cui, Baisong Jiang, and Shuang Xu. Refusion: Learning image fusion from reconstruction with learnable loss via meta-learning. International Journal of Computer Vision, 2024.
[4] Goutam Bhat, Martin Danelljan, Luc Van Gool, and Radu Timofte. Deep burst super-resolution. In CVPR, 2021.
[5] Goutam Bhat, Michaël Gharbi, Jiawen Chen, Lue Van Gool, and Zhihao Xia. Self-supervised burst super-resolution. In ICCV, 2023.
[6] Jiezhang Cao, Jingyun Liang, Kai Zhang, Yawei Li, Yulun Zhang, Wenguan Wang, and Luc Van Gool. reference-based image super-resolution with deformable attention transformer. In ECCV, 2022.
[7] Tiantian Cao, Xuan Dong, Chunli Peng, Zhengqing Li, Xinyu Guo, and Weixin Li. View transition based dual camera image fusion. arXiv preprint arXiv:2312. 11184 2023.
[8] Kelvin C. K. Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Basicvsr++:: Improving video super-resolution with enhanced propagation and alignment. CVPR, 2022.
[9] Kelvin CK Chan, Xintao Wang, Yu, Chao Dong, and Chen Change Loy. Basievsr: The search for essential components in video super-resolution and beyond. In CVPR, 2021.
[10] Zhu Chao, Dong Hang, Pan Jinshan, Liang Boyang, Huang Yuhao, Fu Lean, and Wang Fei. Deep recurrent neural network with multi-scale bi-directional propagation for video deblurring. In AAAI, 2022.
[11] Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun. Simple baselines for image restoration. In Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, and Tal Hassner, editors, ECCV, 2022.
[12] Xihao Chen, Zhiwei Xiong, Zhen Cheng, Jiayong Peng. Yueyi Zhang, and Zheng-Jun Zha. Degradation- agnostic correspondence from resolution-asymmelrii stereo. In CVPR, 2022.
[13] Sung-Jin Cho, Seo- Non Ji, Jun-Pyo Hong, Seung- Won Jung, and Sung-Jea Ko. Rethinking coarse-to-fine approach in single image deblurring. In ICCV, 2021.
[14] Mauricio Delbracio and Guillermo Sapiro. Burst deblurring: Removing camera shake through fourier burst accumulation. In CVPR, 2015.
[15] Xuan Dong, Weixin Li, Xiaoyan Hu, Xiaojie Wang, and Yunhong Wang. A colorization framework for monochrome-color dual-lens systems using a deep convolutional network. IEEE TVCG, 2663)1149-11455 2020.
[16] Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Burst image restoration and enhancement. In CVPR, 2022.
[17] Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Burstormer: Burst image restoration and enhancement transformer. In CVPR, 2023.
[18] Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, and Ming-Husan Yang. Burst image restoration and enhancement. IEEE TPAMI, 2024.
[19] Clément Godard, Kevin Matzen, and Matt Uyttendaele. Deep burst denoising. In ECCV, 2018.
[20] Yixuan Huang, Xiaoyun Zhang, Yu Fu, Siheng Chen, Zhang, Yan-Feng Wang, and Dazhi He. Task decoupled framework for reference-based super-resolution. In CVPR, 2022.
[21] Bo Ji and Angela Yao. Multi-scale memory-basee based video deblurring. In CVPR, 2022.
[22] Bangrui Jiang. Zhihuai Xie, Zhen Xia. Songnan Li, and Shan Liu. Erdn: Equivalent receptive field deformable network for video deblurring, In ECCV, 2022.
10

---

[23] Yuming Jiang. Kelvin CK Chan, Xintao Wang, Chen Change Loy, and Ziwei Liu. Robust reference-based super-resolution via c2-matching. In CVPR, 2021.
[24] Yuming Jiang, Kelvin CK Chan, Xintao Wang. Chen Change Loy, and Ziwei Liu. Reference- based image and video super-resolution via c2-matching. JEEE TPAMI, 45(7):8874 8887, 2022.
[25] Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, and Dacheng Tao. Turning frequency to resolution: Video super-resolution via event cameras. In CVPR, 2021.
[26] Ahmet Serdar Karadeniz, Erkut Erdem, and Aykut Erdem. Burst photography for learning to enhance extremely dark images. JEEE TIP, 30:9372-99885. 2021.
[27] Tae Hyun Kim, Kyoung Mu Lee, Bernhard Schölkopf. and Michael Hirsch. Online video deblurring via dynamic temporal blending network. In ICCV, 2017.
[28] Youngrae Kim, Jinsu Lim, Hoonhee Cho. Minji Lee, Dongman Lee, Kuk-Jin Yoon, and Ho-Jin Choi. Efficient seatreece-bseee video super-resolution (ervsr): Single reference image all you need. 2023.
[29] Sconggwan Ko, Yeong Jun Koh, and Donghyeon Cho. Reference- baaeenee--baed burst super-resolution. In ACM MM, 2024.
[30] Wei-Sheng Lai, Yichang Shih, Lun-Cheng Chu, Xiaotong Wu, Sung-Fang Tsai, Michael Krainin, Deqing Sun, and Chia- hai Liang. Face deblurring using dual camera fusion on mobile phones. ACM Transactions on Graphics (TOG), 41(4):1-16, 2022.
[31] Junyong Lee, Myconghee Lec, Sunghyun Cho, and Seungyong Lec. Reference- based vidco super- resolution using multi- camera vidco triplets. In CVPR, 2022.
[32] Dasong Li, Xiaoyu Shi, Yi Zhang, Ka Chun Cheung, Simon See, Xiaogang Wang, Hongwei Qin, and Hongsheng Li. A simple baseline for video restoration with grouped spatial-temporal shift. In CVPR, 2023.
[33] Dongxu Li, Chenchen Xu, Kaihao Zhang, Xin Yu, Yiran Zhong, Wenqi Ren, Hanna Suominen, and Hongdong Li. Arvo: Learning all-range volumetric correspondence for video deblurring. In CVPR, 2021.
[34] Weixin Li. Tiantian Cao. Chang Liu, Xue Tian, Ya Li, Xiaojie Wang, and Xuan Dong. Dual-lens hdr using guided 3d exposure enn and guided denoising transformer. ACM Transactions on Multimedia Computing, Communications and Applications, 1-20,, 2023.
[35] Zhuoyuan Li, Jiacheng Li, Yao Li, Li Li, Dong Liu, and Feng Wu. In-loop filtering via trained look-up tables. In VCIP, 2024.
[36] Zhuoyuan Li, Junqi Liao, Chuanbo Tang, Haotian Zhang, Yuqi Li, Yifan Bian, Xihua Sheng, Xinmin Feng. Yao Li. Changsheng Gao, et al. Uste-td: A test dataset and benchmark for image and video coding 2020s. IEEE Transactions on Multimedia, 2025.
[37] Jingyun Liang, Jiezhang Cao, Yuchen Fan, Kai Zhang, Rakesh Ranjan, Yawei Li, Radu Timofte, and Luc Van Gool. Vrt: A video restoration transformer. IEEE TIP, 2024.
[38] Jingyun Liang, Yuchen Fan, Xiaoyu Xiang, Rakesh Ranjan, Eddy 1lg, Simon Green, Jiezhang Cao, Kai Zhang, Radu Timofte, and Lue Van Gool. Recurrent video restoration transformer with guided deformable attention. 2022.
[39] Jingyun Liang. Yuchen Fan, Xiaoyu Xiang, Rakesh Ranjan, Eddy llg, Simon Green, Jiezhang Cao, Kai Zhang, Radu Timofte, and Luc Van Gool. Recurrent video restoration transformer with guided deformable attention. 2022.
[40] Jing Lin, Yuanhao Cai, Xiaowan Hu, Haoqian Wang, Youliang Yan, Xucyi Zou, Henghui Ding, Yulun Zhang, Radu Timofte, and Luc Van Gool. Flow-guided sparse transformer for video deblurring. 2022.
[41] Cunzhe Liu, Zhen Hua, and Jinjiang Li. Deep reference- hased dynamic scene deblurring. KSII Transac- tions on Internet & Information Systems, 18(3), 2024.
[42] Cunzhe Liu, Zhen Hua, and Jinjiang Li. Reference- dual-task framework for motion deblurring. The Visual Computer, 40(
2024.
[43] Huan Liu, Mingwen Shao, Yecong Wan, Yuexian Liu, and Kai Shang. Sebir: conantic-guider burst image restoration. Neural Networks, 181:106834, 2025.

---

[44] Liying Lu, Wenbo Li. Xin Tao, Jiangbo Lu. and Jiaya Jia. Masa-sr: Matching acceleration and spatial adaptation for reference-baan based image super-resolution. In CVPR, 2021.
[45] Xianrui Luo, Zijin Wu, Juewen Peng. Huiqiang Sun, Zhiguo Cao, and Guosheng Lin. Dual-camera all- in-foous neural radiance fields. IEEE TPAMI, 2025.
[46] MR Mahesh Mohan, GK Nithin, and AN Rajagopalan. Deep dynamic seene deblurring for unconstrained dual-Iens cameras. IEEE TIP, 50:4479-44991 2021.
[47] Seungjun Nah, Sungyong Baik, Scokil Hong, Gyeongsik Moon, Sanghyun Son, Radu Timofte, and Kyoung Mu Lee. Ntire 2019 challenge on video dehlurring and super-resolution: Dataset and study. In CVPRW, 2019.
[48] Seungjun Nah, Tae Hyun Kim, and Kyoung Mu Lee. Deep multi-scale convolutional neural network for dynamic scene deblurring. In CVPR, 2017.
[49] Seungjun Nah, Sanghyun Son, and Kyoung Mu Lee. Recurrent neural networks with Intra-frame iterations for video deblurring. CVPR, 2019.
[50] Seungjun Nah, Sanghyun Son, Suyoung Lee, Radu Timofte. Kyoung Mu Lee, Liangyu Chen, Jie Zhang, Xin Lu, Xiaojic Chu, Chengpeng Chen, et al. Ntire 2021 challenge on image deblurring. In CVPR, 2021.
[51] Jinshan Pan, Haoran Bai, and Jinhui Tang. Cascaded deep video deblurring using temporal sharpness prior, In CVPR, 2020.
[52] Jinshan Pan, Boming Xu, Jiangxin Dong, Jianjun Ge, and Jinhui Tang. Deep discriminative spatial and temporal network for efficient video deblurring. In CVPR, 2023.
[53] Chunli Peng, Xuan Dong, Tiantian Cao, Zhengqing Li, Kun Dong, and Weixin Li. Rewite: Realistic wide-angle and telephoto dual camera fusion dataset via beam splitter camera rig. In ACM MM, 2024.
[54] Long Peng. Yang Cao. Yuejin Sun, and Yang Wang. Lightweight adaptive feature de-drifting for compressed image classification. IEEE Transactions on Multimedia, 26:6424-6438 2024.
[55] Long Peng, Yang Wang. Xin Di, Xueyang Fu, Yang Cao. Zheng-Jun Zha, et al. Boosting image de-raining via sentral-urrounding synergistic convolution. In AAAI, 2025.
[56] Marco Pesavento, Marco Volino, and Adrian Hilton. Attention- based super-resolution. In ICCV, 2021.
learning for image
[57] Anurag Ranjan and Michael J Black. Optical flow estimation using a spatial pyramid network. In CVPR, 2017.
[58] Chen Rao, Guangyuan Li, Zehua Lan, Jiakai Sun, Junsheng Luan, Wei Xing, Lei Zhao, Huaizhong Lin, Jianfeng Dong, and Dalong Zhang. Rethinking video deblurring with wavelet-aware dynamic transformer and diffusion model. ECCV, 2024.
[59] Jaesung Rim, Junyong Lee, Heemin Yang, and Sunghyun Cho. Deep hybrid camera deblurring for smartphone cameras. In ACM SIGGRAPH 2024 Conference Papers, pages 1-11, 2024.
[60] Finn Russell and William JB Midgley. Asymmetric stereo high dynamic range imaging with smartphone cameras. Sensors, 24(18 51866, 2024.
[61] Shayan Shekarforoush, Amanpreet Walia, Marcus A Brubaker, Konstantinos G Derpanis, and Alex Levinshtein. Dual-camera joint deblurring-denoisnggg arXiv preprint 2309.22309 2023.
[62] Gyumin Shim, Jinsun Park, and In So Kweon. Robust reference-based super-resolution with similarity- aware deformable convolution. In CVPR, 2020.
[63] Hyeongseok Son, Junyong Lee, Jonghyeop Lee, Sunghyun Cho, and Seungyong Lee. Recurrent video deblurring with blur-invariant motion estimation and pixel volumes. IEEE TIP, 18:5):185: 2021.
[64] Taeyong Song, Sunok Kim, and Kwanghoon Soha. Unsupervised deep asymmetric stereo matching with apatially-adaptive self-similarity. In CVPR, 2023.
[65] Shuochen Su, Mauricio Delbracio, Jue Wang, Guillermo Sapiro, Wolfgang Heidrich, and Oliver Wang. Deep video deblurring for hand-held cameras. In CVPR, 2017.
12

---

[66] Maitreya Suin and A. N. Rajagopalan. Gated spatio-lemporal attention-guider video deblurring. In CVPR, 2021.
[67] Xin Tao, Hongyun Gao, Xiaoyong Shen, Jue Wang, and Jiaya Jia. Scale-recurrent network for deep image deblurring. In CVPR, 2018.
[68] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In ECCV, pages 112-419,20 2020.
[69] Ruohao Wang. Xiaohui Liu, Zhilu Zhang, Xiaohe Wu, Chun-Mei Feng. Lei Zhang, and Wangmeng Zuo. Benchmark dataset and effective inter-frame alignment for real-world video super-resolution CVPR. 2023.
[70] Tengfei Wang, Jiaxin Xie, Wenxiu Sun, Qiong Yan, and Qifeng Chen. Dual-camera super-resolution with aligned attention modules. In ICCV, 2021.
[71] Xintao Wang, Kelvin C. K. Chan, Ke Yu, Chao Dong, and Chen Change Loy. EDVR: vidco restoration with enhanced deformable convolutional networks. In CVPRW, 2019.
[72] Yusheng Wang, Yunfan Lu, Ye Gao, Lin Wang. Zhihang Zhong, Yingiang Zheng, and Atsushi Yamashita. Efficient video deblurring guided by motion magnitude. In ECCV, 2022.
[73] Pengxu Wei, Yujing Sun, Xingbei Guo, Chang Liu, Guanbin Li, Jie Chen, Xiangyang Ji, and Liang Lin. Towards real-world burst image super-resolution: Benchmark and method. In ICCV, 2023.
[74] Patrick Wieschollek, Michael Hirsch, Bernhard Schölkopf, and Hendrik P. A. Lensch. Learning blind motion dehlurring. In ICCV, 2017.
[75] Renlong Wu, Zhilu Zhang, Yu Yang, and Wangmeng Zuo. Dual-camera smooth zoom on mobile phones. In ECCV, 2024.
[76] Xiaotong Wu, Wei-Sheng Lai, Yichang Shih, Charles Herrmann. Michael Krainin, Deqing Sun, and Chia-Kai Liang. Efficient hybrid zoom using camera fusion mobile phones. ACM Transactions on Graphics (TOG), 42(6):1-12, 2023.
[77] Zeyu Xiao, Jiawang Bai, Zhihe Lu, and Zhiwei Xiong. A dive into sam prior in image restoration, arXiv preprint arXiv:2305 13620, 2023.
[78] Zoyu Xiao, Xueyang Fu, Jie Huang, Zhen Cheng, and Zhiwei Xiong. Space-time distillation for vidco suser-nesolution. In CVPR, 2021.
[79] Zeyu Xiao, Zhihe Lu, Michael Mi, Zhiwei Xiong, and Xinchao Wang. Unraveling motion uncertainty for local motion deblurring. In ACMMM, 2024.
[80] Zeyu Xiao and Zhiwei Xiong, Incorporating degradation estimation in light field spatial super-resolution. CVIU, 25:: 104295, 2025.
[81] Zeyu Xiao, Zhiwei Xiong, Xueyang Fu, Dong Liu, and Zheng-Jun 7ha. Space-time video super-resolution using temporal profiles. In ACMMM, 2020.
[82] Yanchun Xie, Jimin Xiao, Mingjie Sun, Chao Yao, and Kaizhu Huang. Feature representation matters: End-to-end leamning for afference-based image super-resolutio. In ECCV, 2020.
[83] Ruikang Xu, Mingde Yao, Yue Li, Yueyi Zhang, and Zhiwei Xiong. High-resolution and few-shot view synthesis from asymmetric dual-lens inputs. In ECCV, 2024.
[84] Ruikang Xu, Mingde Yao, and Zhiwei Xiong. Zero-shot dual-lens super-resolution. In CVPR, 2023.
[85] Huanjing Yue, Zifan Cui, Kun Li, and Jingyu Yang. Kedusr: Real-world dual-lens super-resolution via kemne--rre matching. In AAAI, 2024.
[86] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for resoluiont image restoration. In CVPR, 2022.
[87] Hongguang Zhang, Yuchao Dai, Hongdong Li, and Piotr Koniusz. Deep stacked hicrarchical multi-patch network for image deblurring. In CVPR, 2019.
[88] Huicong Zhang. Haozhe Xie, and Hongxun Yao. Spatio-tempone deformable attention network for video deblurring. In ECCV, 2022.
13

---

[89] Huicong Zhang, Haozhe Xie, and Hongxun Yao. Blur-aware spatio-temporal sparse transformer for video deblurring. In CVPR, 2024.
[90] Jinrui Zhang, Huan Yang. Ju Ren, Deyu Zhang, Bangwen He, Ting Cao, Yuanchun Li, Yaoxue Zhang. and Yunxin Liu. Mobidepth: Real-time depth estimation using on-device dual cameras. In Proceedings of the 28th Annual International Conference on Mobile Computing And Networking. 2022.
[91] Kaihao Zhang, Wenhan Luo, Yiran Zhong, Lin Ma, Wei Liu, and Hongdong Li. Adversarial spatio- temporal learning for video deblurring. IEEE TIP, 28(1):291 301, 2019.
[92] Lin Zhang, Xin Li, Dongliang He, Fu Li, Errui Ding, and Zhaoxiang Zhang. Lmr: large-scale multi-reference dataset reference-basee based super-resolution. ICCV, 2023.
[93] Lin Zhang. Xin Li, Dongliang He, Fu Li, Yili Wang, and Zhaoxiang Zhang. Rrst: Reciprocal reference- based image super-resolution with progressive feature alignment and selection. In ECCV, 2022.
[94] Zhilu Zhang, Ruohao Wang, Hongzhi Zhang. Yunjin Chen, and Wangmeng Zuo. Self-supervised learning for real-world super-resolution from dual zoomed observations. In ECCV, 2022.
[95] Zhilu Zhang, Ruohao Wang. Hongzhi Zhang. and Wangmeng Zuo. Self-sunervised learning for real-world super- resorttesolttion from dual and multiple zoomed observations. IEEE TPAMI, 2024.
[96] Zixiang Zhao, Haowen Bai, Bingxin Ke, Yukun Cui, Lilun Deng, Yulun Zhang, Kai Zhang, and Konrad Schindler. unified solution to video fusion: From multi-frame learning benchmarking. arXiv preprint arXiv:2505. /9858, 2025.
[97] Zixiang Zhao, Haowen Bai, Jiangshe Zhang. Yulun Zhang, Shuang Xu, Zudi Lin, Radu Timofte, and Luc Van Gool. Cddfuse: Correlation-driven dual-branch feature decomposition for multi-modality image fusion. In CVPR, 2023.
[98] Zhihang Zhong, Ye Gao, Yinqiang Zheng, Bo Zheng, and Imari Sato. Real-world video deblurring: A benchmark dataset and an efficient recurrent neural network. IJCV, 2022.
[99] Kun Zhou, Wenbo Li, Liying Lu, Xiaoguang Han, and Jiangbo Lu. Revisiting temporal alignment for video restoration. In CVPR, 2022.
[100] Shangehen Zhou, Jiawei Zhang, Jinshan Pan, Wangmeng Zuo, Haozhe Xic, and Jimmy S. J. Ren. Spatio-temporal filter adaptive network for video deblurring In ICCV, 2019.
[101] Chao Zhu, Hang Dong, Jinshan Pan, Boyang Liang, Yuhao Huang, Lean Fu, and Fei Wang. Deep recurrent neural network with multi-scale bi-directional propagation for video deblurring. In AAAI, 2022.
14

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: main claims in the abstract and introduction accurately reflect the contributions AD-eeD- The paper introduces the first framework for asymmetric dual-lens video deblurring (AsLeD), proposes key modules (ALM, DC, RMC), and validates superior performance through experiments (Section 4). Contributions explicitly outlined in Section 1 and Section 3.
Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: Section 5 ("Discussions") explicitly addresses limitations: (1) training data generated via frame averaging may not fully simulate real-world motion blur, especially in high-motion scenarios; (2) computational inefficiency (248.7 inference time, 8.8M parameters) limits real-time applicability. Future directions (e-g., model compression) are suggested.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [No]
Justification: The paper focuses on algorithmic design and empirical validation, with no theoretical dcrivations or mathematical proofs. Methodology is descrihed through architectural diagrams (Figures 3-5) and equations for feature fusion (Eqs. 1-7).
Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper the extent that it affects the main claims and/or conclusions of the paper (regardless whether the code and data are provided or not)?
Answer: [Yes]
Justification: Section details datasets (RcalMCVSR), training protocols (Adam, 300K iterations), and hyperparameters.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [No]
Justification: The paper references public datasets (RealMCVSR) but provides no links to code reposi- tories pre-trained models. Reproducibility relies on re-implementation from textual descriptions.
Experimental sefting/details
Question: Does the paper specify all the training and test details (e.g.. data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary understand the results?
Answer: [Yes]
Section 4.1 comprehensively describes experimental settings: datasets, blur simulation (7-frame averaging), input resolution (128 x 128), optimizer (Adam with Cosine Annealing). loss function (Charbonnier), hardware (NVIDIA RTX 3090), and evaluation metries (PSNR, SSIM. LPIPS).
Justification:
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate informa- tion about the statistical significance of the experiments?
Answer: [No]
Justification: Results in Tables 1-3 report PSNR/SSIM/ .PPPS values but lack error bars, confidence intervals, or statistical significance tests. Variability factors (e.g., data splits, initialization) are not discussed.
Experiments compute resources
Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
15

---

Answer: [Yes]
Justification: The paper training hardware (RTX 3090) but omits per-experiment memory usage, total compute costs, or training time. Inference time (248.7 ms) reported.
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https: https://neete http://eeerrpp.... http://urrppp.ccppplll////tt//aa httpppppbble/thhhccccc..........
Answer: [Yes]
Justification: The "Broader impact" section addresses ethical concers: privacy risks (enhanced surveillance), dual-use potential (misuse in synthetic media), and model bias. The authors align with NeurIPS ethics guidelines.
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
The paper discusses positive impacts (healthcare, autonomous systems) and negative risks (privacy violations, misuse). While mitigation strategies are not detailed, ethical trade-offs are acknowledged.
Justification:
11. Safeguards
Does the paper deseribe safeguards that have been put in place for responsible release of data models that have a high risk for misuse (e.g.. pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: The paper does not release high-ris assets (models, datasets). AsLeD-Ner D-Net is presented as a research framework without deployment, no safeguards are discussed.
12. Licenses for existing assets
Question: Are the creators or original owners of assets (e.g., code. data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [No]
Justification: Public datasets (RealMCVSR) and methods (e.g., BSR, EDVR) are cited, licenses are not explicitly mentioned (c.g., RealMCVSR [30] terms of use are omitted).
13. New assets
Question: Are alongside assets?
introduced in the paper well documented and is the documentation provided
Answer: [NA]
Justification:
The paper does not introduce new datasets, codehases, models.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?
Answer: [NA]
Justification: The work involves no human subjects or crowdsourcing.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Does the paper describe potential risks incurred by study participants. whether such risks were disclosed the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based on the requirements your country or institution) were obtained?
Question:
Answer: [NA]
Justification: No human subjects research is conducted.
16.
Declaration of LLM usage
Question: Does the paper describe usage of LLMs it important, original, or non standaad component
core methods this research? Note that the L.M used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Answer: [NA]
Iustification: LLMS are not used methodology, experiments, or analysis.
16


Reviews:
[{"review_id": "Mp0SiitX0f", "reviewer": "Reviewer_FKmP", "summary": "This paper introduces Asymmetric Dual-Lens Video Deblurring (AsLeD-Net), a novel deep learning method designed to deblur video from asymmetric dual-lens camera systems, commonly found in modern smartphones. It uniquely leverages the complementary information from a blurry wide-angle lens and a sharper ultra-wide reference lens.", "questions": "refer to the weakness.", "limitations": "refer to the weakness.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n1. AsLeD-Net consistently achieves state-of-the-art quantitative results on the RealMCVSR dataset across PSNR (26.34 dB), SSIM (0.9167), and LPIPS (0.1614), demonstrating enhanced detail preservation and perceptual quality. \n2. Qualitatively, it excels in recovering fine textures and maintaining strong temporal coherence, reducing flickering artifacts. \nWeaknesses:\n1. While the paper claims AsLeD-Net is the \"first video deblurring method\" to leverage asymmetric dual-lens systems and the \"first study on reference-based video deblurring for dual-lens smartphone captures\" , prior work has explored similar concepts for deblurring and related tasks using dual-lens cameras. For instance, Mohan et al. (2021) proposed a \"dynamic scene deblurring method for unconstrained dual-lens (DL) cameras\". Furthermore, Lai et al. (2022) developed a \"Face Deblurring using Dual Camera Fusion on Mobile Phones\" system that explicitly uses an ultrawide-angle camera as a \"reference camera\" to deblur a \"blurry main shot\" on mobile devices, leveraging the concept of a sharper auxiliary view from an asymmetric setup for deblurring, albeit for images and specifically faces. While AsLeD-Net's specific architectural modules and focus on general video deblurring are distinct, the fundamental idea of exploiting complementary information from asymmetric dual-lens systems for deblurring has precedents, which somewhat diminishes the strength of the \"first\" claim. \n\n- Mohan, M. R. M., Nithin, G. K., & Rajagopalan, A. N. (2021). Deep Dynamic Scene Deblurring for Unconstrained Dual-Lens Cameras. IEEE Transactions on Image Processing, 30, 4479-4491. \n- Lai, W.-S., Shih, Y., Chu, L.-C., Wu, X., Tsai, S.-F., Krainin, M., Sun, D., & Liang, C.-K. (2022). Face Deblurring using Dual Camera Fusion on Mobile Phones. ACM Transactions on Graphics (TOG), 41(4), 1-16.\n2. The reliance on simple frame averaging for generating training data may not fully simulate the complexities of real-world motion blur, especially in high-motion and high-contrast scenarios, potentially affecting generalization.\n3. There is a notable contradiction in the text regarding the performance of ALM compared to alternative methods (DAT and MASA-SR) in the ablation study, where the text claims improvement while the table shows the proposed ALM outperforming them.", "was_revised": true, "final_justification": "Thanks for the detailed reply, and my concern is well-solved. I would like to raise to weak accept for this paper.", "rebuttal": "We sincerely thank you for your thoughtful review and positive recognition of AsLeD-Net’s performance, especially in perceptual quality, temporal coherence, and texture recovery. Your encouraging feedback affirms its practical value. We address your concerns in detail below.\n\n`>>Q1: Task setting.`\t\n\nA1: We sincerely thank the reviewer for highlighting the connections to earlier works, including Mohan et al. (2021) and Lai et al. (2022). We have deep respect for these important contributions. Both studies demonstrated the value of asymmetric dual-lens systems for image-level deblurring and provided thoughtful insights into the use of complementary views for restoration. Their work has undoubtedly laid a solid foundation for the broader use of cross-lens information in image processing, and we are grateful to build upon their ideas. \n\nWhile our initial submission described our work as the first in this area, upon further reflection, we realize that the key concept of utilizing a sharper auxiliary view has already been established in previous literature. Therefore, we would like to revise and soften our original claim. Rather than emphasizing the notion of being the first, we now view our work as a meaningful and practical extension of this existing research direction into the context of video deblurring. From the perspective of task setting, our work differs in several ways. For clarity, we provide the following comparison:\n\n| Work | Task | Input | Temporal | Description |\n|------|------|-------|----------|-------------|\n| Mohan et al. | Image deblurring | Dual-lens image pair | ✘ | Deblurs static image pairs from unconstrained dual-lens cameras with view and depth consistency constraints. |\n| Lai et al. | Face image deblurring | Main + UW image pair | ✘ | Deblurs face regions in still photos using a synchronized sharp reference from the ultra-wide camera. |\n| Ours| Video deblurring (reference-guided) | Wide + UW video | ✔ | Deblurs general video sequences using asymmetric dual-lens inputs, integrating reference-guided alignment and temporal modeling. |\n\nUnlike prior image-based methods, our method addresses video-specific challenges such as temporal coherence, frame-wise propagation, and cross-view alignment. We propose a two-stream architecture with three core modules: Adaptive Local Matching, Difference Compensation, and Reference Guided Motion Compensation, which enable spatial and temporal reference enhancement. A recurrent propagation strategy is further adopted to ensure temporal consistency.\n\nOnce again, we sincerely thank the reviewer for the thoughtful comments and the opportunity to clarify and refine the presentation of our contributions. We will revise the manuscript to more accurately and respectfully position our work in the context of existing literature.\n\n\n`>>Q2: Simple frame averaging may fail to capture real-world motion blur complexity, limiting generalization.`\n\nA2: We respectfully disagree. As discussed in the Limitation section, we are aware of this approximation and have been transparent about its potential drawbacks. Nonetheless, we believe this design choice remains reasonable and does not significantly affect the effectiveness or diminish the overall contributions of our method. (1) Despite being trained on synthetically generated data, our model demonstrates strong generalization to real-world dual-lens video inputs. For example, as shown in Figure 7 of the main paper and Figure 7 of the supplementary material, AsLeD-Net produces perceptually consistent and visually compelling results on challenging real sequences. (2) We believe that expecting fully realistic training data with precisely aligned blurry-sharp video pairs at this early stage of dual-lens research may be overly demanding. Simulation-based training, while imperfect, has been a widely accepted starting point in both image and video deblurring literature, enabling practical progress in the absence of large-scale real datasets. That said, we agree that more realistic data would further benefit this line of work. We have identified tools such as the \"Blackmagic Camera\" app, which support flexible control over asymmetric camera parameters such as exposure time, aperture size, shutter speed, and light intake. We plan to use such tools in future work to collect real blurry-sharp video pairs under realistic dual-lens settings. This will help us further improve the realism and robustness of our training pipeline.\n\nWe appreciate the reviewer’s thoughtful critique and will revise the manuscript to better reflect this discussion and our commitment to future improvements.\n\n\n`>>Q3: ALM Analysis.`\n\nA3: We thank the reviewer for pointing out this inconsistency in our description of the ablation study. Sorry for the mistake here, and we acknowledge that our textual analysis in Section 4.4 was imprecise. As correctly noted, the ALM module (with 𝑘=3) achieves higher PSNR and SSIM scores (26.34 / 0.9167) compared to both DAT (26.28 / 0.9154) and MASA-SR (26.19 / 0.9152), as shown in Table 4. We apologize for the oversight and will revise the text to accurately reflect the quantitative results. Specifically, we will clarify that while DAT and MASA-SR are strong context-aware alternatives, our ALM module demonstrates superior overall performance, supporting its effectiveness for structure-aware reference matching. We sincerely appreciate the reviewer’s attention to detail and will make sure the final version corrects this inconsistency.\n\n`---SUMMARY---`\n\nWe sincerely thank Reviewer FKmP for the detailed feedback. We will revise the manuscript to correct the inconsistency in the ablation discussion, clarify the framing of our contribution with respect to prior dual-lens deblurring works, and better articulate the rationale behind our training data design. That said, we respectfully believe that the strengths of our work remain compelling and are clearly recognized by other reviewers. These include the task formulation of dual-lens video deblurring and architectural design (Reviewer 9xWq), the good experimental results and practical relevance (Reviewer H9KQ), and the clear motivation, effective performance, and candid discussion of limitations (Reviewer Rwp8). Collectively, these reviews affirm that our work introduces a good framework for asymmetric dual-lens video deblurring, a direction that has not been systematically explored before. We would greatly appreciate the opportunity to present this work at NeurIPS. We believe it would serve as a valuable baseline for future research on mobile computational photography and help further advance the field."}, {"review_id": "HMxs6Hnn7d", "reviewer": "Reviewer_9xWq", "summary": "This paper presents AsLeD-Net, a novel video deblurring framework that exploits asymmetric dual-lens inputs (wide and ultra-wide views) to enhance restoration quality under realistic mobile camera setups. The method integrates three key modules – Adaptive Local Matching (ALM) for spatial alignment, Difference Compensation (DC) for structural refinement, and Reference-Guided Motion Compensation (RMC) for improved temporal consistency. Experiments on the RealMCVSR dataset demonstrate substantial performance gains over state-of-the-art deblurring approaches.", "questions": "- Generality to Other Dual-Lens Settings: Have the authors tested generalization to other asymmetric setups, such as wide+telephoto or different focal length ratios, beyond the ultra-wide + wide used in RealMCVSR?\n- Computational Overhead: Table 3 shows runtime comparisons, but it remains unclear how much additional cost ALM and RMC introduce over single-view baselines. Providing FLOPs or per-module latency would strengthen practical relevance.\n- Failure Cases: The paper would benefit from including visual examples where AsLeD-Net fails, e.g. scenes with severe occlusions or large parallax between lenses, to inform future research.", "limitations": "Yes, the method assumes well-calibrated dual-lens systems with known geometric alignment. In real-world deployment, calibration errors or rolling shutter differences may degrade performance.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "--------Strengths----------\n- Quality: The proposed framework is technically well-designed and achieves strong quantitative and qualitative results. The ablation studies clearly verify each module's contribution.\n- Clarity: Overall clear, but the methodology section could benefit from a higher-level intuition before diving into module details.\n- Significance: The dual-lens deblurring task is highly relevant for mobile computational photography, with realistic deployment potential.\n- Originality: Introducing dual-lens information into video deblurring is novel. The combination of KNN aggregation and difference compensation is a fresh design.\n\n--------Weaknesses----------\n- All experiments are conducted on RealMCVSR, which is curated with fixed device configurations. It remains unclear how well AsLeD-Net generalizes to different dual-lens setups or unseen real-world motion blur conditions.\n- Computational cost analysis: While Table 3 reports runtime comparisons, the breakdown of latency or FLOPs per module (ALM, DC, RMC) is missing, making it difficult to assess deployment feasibility on edge devices.\n- Dependency on calibration: The method assumes accurate geometric calibration between lenses. In practice, small misalignments, lens distortions, or rolling shutter discrepancies may reduce performance, but this is not discussed.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank you for your thoughtful review and positive recognition of AsLeD-Net’s performance, especially in perceptual quality, temporal coherence, and texture recovery. Your encouraging feedback affirms its practical value. We address your concerns in detail below.\n\n`>>Q1: Generalization to other dual-lens configurations.`\n\nA1: Thank you for raising this important point. The RealMCVSR dataset we use for training is captured with the iPhone 12 Pro Max, while our real-world test scenes are captured independently using a personal iPhone 14 Plus device. This intentional variation already introduces diversity in device hardware, optics, and imaging pipelines. As shown in Figure 7, AsLeD-Net maintains strong performance under such cross-device settings, indicating good generalization in practice. Regarding blur modeling, although our training blur is synthetically generated via averaging, we evaluate on real captured blur without further tuning. The consistency in performance across synthetic and real scenarios supports the robustness of our approach. This further suggests that the model is not overfitted to the training setup but instead learns transferable representations for motion compensation and structure restoration. Therefore, we do not see this as a weakness.\n\nThat said, we agree that having a larger-scale paired real-world dataset that covers diverse lens combinations and realistic blur would provide a more comprehensive testbed. We consider building or incorporating such datasets a promising direction for future work to further improve generalization and ensure fairer evaluation.\n\n\n`>>Q2: Computational overhead.`\n\nA2: Thank you for the valuable suggestion. While our current implementation is not tailored for real-time deployment, we have taken care to ensure that the overall parameter count remains comparable to existing baselines (as reported in Table 3), striking a balance between model complexity and performance. Since our primary goal is to enhance restoration quality in asymmetric dual-lens settings, we did not provide per-module FLOPs or latency breakdowns in this version. Moreover, such breakdowns may have limited indicative value, as the modules are tightly coupled in the processing pipeline. Although FLOPs can be computed statically, actual runtime latency may vary across hardware and depend on system-level factors such as memory access and parallelism, which are difficult to isolate per module.\n\nWe agree that more detailed profiling would be beneficial for facilitating future deployment. As a next step, we plan to conduct a module-wise analysis of computational efficiency and explore lightweight variants of key components (e.g., ALM, DC, RMC), with the goal of improving inference speed and making the system more suitable for real-time or resource-constrained applications.\n\n`>>Q3: Dependency on calibration.`\n\nA3: Thank you for raising this point. We would like to clarify a common misconception: our method does not assume perfect geometric calibration or strict spatial alignment between the dual-lens inputs. In fact, AsLeD-Net is specifically designed to handle the inherent misalignments and photometric differences that naturally occur in asymmetric dual-lens configurations (see our response A3 and A6 to Reviewer Rwp8).\n\nIn our real-world setup, we use an iPhone 14 Plus, where the two different lenses have a physical baseline and distinct imaging characteristics. As a result, there are observable spatial shifts and color inconsistencies between the two views. We do not apply any spatial or color alignment during preprocessing. Instead, we preserve these discrepancies intentionally, as they represent realistic deployment scenarios. Our design allows the network to learn feature-level alignment in a content-aware and data-driven manner. To ensure temporal synchronization, we employ the “DoubleTake” app from the iPhone App Store, which records both video streams simultaneously. This ensures precise frame-level alignment in time, even if the spatial views differ. We believe this reflects practical mobile applications, where perfect geometric alignment is often unavailable or difficult to maintain.\n\nWe acknowledge that real-world artifacts such as lens distortion or rolling shutter effects may affect performance in extreme cases. Some of these limitations are discussed and visually illustrated in our failure cases. Going forward, we plan to extend our work by collecting a larger-scale real-world dataset across diverse device setups and lens combinations. We also see potential in incorporating calibration-robust learning strategies or optional pre-alignment to further enhance resilience and performance.\n\n`>>Q4: Failure cases.`\n\nA4: Thank you for highlighting the importance of presenting failure cases. We fully agree that such examples are essential for understanding the limitations of our method and guiding future improvements. In response, we provide a detailed discussion in Section 3 of the supplementary material and include representative failure cases in Figure 10.\n\nWe particularly analyze the domain gap between our synthetically generated training blur (via frame averaging) and real-world blur. Real blur is often more complex, arising from non-uniform motion, rolling shutter effects, and optical distortions. These factors can lead to degradation in performance under extreme conditions, such as high-speed motion, large occlusions, or low-light scenarios—where we observe issues like incomplete texture recovery or ghosting artifacts.\n\nTo address these challenges, we propose several future directions:\n- Designing more realistic blur synthesis pipelines that better reflect real-world degradation;\n- Expanding the real-world training dataset to include a broader range of blur types and scene dynamics;\n- Exploring model distillation, pruning, and compression strategies to enhance robustness and deployability.\n\nWe believe these steps will help further improve the reliability and generalization of AsLeD-Net in practical settings. Thank you again for your constructive feedback.\n\n\n`---SUMMARY---`\n\nWe sincerely thank Reviewer 9xWq for the thoughtful and constructive review. We are especially grateful for your recognition of our contributions in task formulation, method originality, and practical relevance to mobile computational photography. Your insightful comments on generalization, computational cost, calibration dependency, and failure analysis are carefully considered. In response, we provide detailed clarifications, including cross-device evaluation to demonstrate generalization, explanation of our design choices regarding efficiency, and an emphasis on the system’s robustness to misalignment without requiring strict calibration. We also discuss failure cases and outline future directions to close the gap between synthetic and real-world degradation. Your suggestions help refine our presentation and broaden our perspective on deployment challenges. We believe the revised version more effectively reflects the significance and rigor of our work. Thank you again for your valuable feedback and support."}, {"review_id": "wUL2Y6RP8w", "reviewer": "Reviewer_H9KQ", "summary": "This paper introduces AsLeD-Net, targeting video deblurring under dual-lens smartphone settings by fusing wide and ultra-wide views. The proposed pipeline integrates multiple standard components – local matching, difference compensation, and motion compensation – adapted to the dual-lens scenario.", "questions": "1) Does AsLeD-Net generalize to dual-lens systems with different baseline distances or sensor noise patterns (e.g. older phones with mismatched lens quality)?\n2) Are all three modules (ALM, DC, RMC) equally necessary? Could a lightweight two-module variant achieve comparable performance with lower latency?", "limitations": "The framework assumes availability of dual-lens video pairs with reliable calibration and synchronization. In practice, hardware discrepancies, rolling shutter mismatch, or lens shading differences may degrade performance.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "1) Tackles a practically meaningful problem in mobile imaging.\n2) Includes thorough experiments on RealMCVSR with clear quantitative gains.\n\n- Weaknesses:\n\n1) The novelty is limited. While the application context is fresh, most technical modules are repurposed from stereo matching, reference-based super-resolution, and multi-frame fusion literature without fundamental algorithmic innovation.\n2) Lack of theoretical analysis. The method heavily relies on empirical module stacking; no insights are provided on failure modes, stability, or optimality.\n3) Dataset limitation: Only RealMCVSR is used, which is curated and may not fully reflect wild camera shake and dual-lens disparities under different devices or lighting conditions.", "was_revised": true, "final_justification": "My concerns have been addressed and I think it is acceptable.", "rebuttal": "We sincerely thank Reviewer H9KQ for the constructive feedback. We appreciate your recognition of the practical relevance of our task and the clarity of our experimental validation. Below, we address your concerns in detail.\n\n`>>Q1: The novelty is limited.`\n\nA1: We respectively disagree and we have three points here. (1) Our novelty lies not in inventing entirely new building blocks, but in reformulating and adapting these strategies to address the task of asymmetric dual-lens video deblurring—a setting that has not been systematically explored. (2) The module design decisions are non-trivial and task-specific innovations that go beyond simple reuse. To support this, we conduct comprehensive ablation studies (see ablation studies) showing that each module contributes significantly to the final performance. Removing any component leads to a measurable drop quantitatively and qualitatively. (3) Our ALM module is customized to handle spatially misaligned cross-view references where standard stereo correspondences break down; the DC module explicitly models cross-view structural discrepancies (e.g., occlusions, FoV gaps), which are typically ignored in previous settings; and the RMC integrates temporal and cross-view motion in a unified pipeline, optimized for non-strictly aligned auxiliary references. We respectfully believe that task novelty, adaptive integration, and demonstrated effectiveness through rigorous evaluation constitute meaningful contributions, even if some modules build upon known paradigms. In future work, we plan to further abstract these insights to inspire more generalizable frameworks beyond dual-lens scenarios. We therefore do not see this as a weakness.\n\n`>>Q2: Lack of theoretical analysis.`\n\nA2: Thank you for raising this insightful point. We acknowledge that the current version of AsLeD-Net is primarily grounded in empirical design, aiming to *address the practical and underexplored challenge of dual-lens video deblurring in mobile settings*. Since this task lacks a well-established theoretical foundation, our first goal is to demonstrate the feasibility and effectiveness of cross-view-guided motion compensation through carefully designed modules. While the framework may appear modular, the overall design reflects *deliberate architectural choices*. For instance, the motion compensation is guided by reference features in a content-adaptive manner, rather than simply stacking modules. We verify the necessity and effectiveness of each component via comprehensive ablation studies (Table 2 and Table 3), which show consistent performance gains when adding each module. Additionally, we provide a failure case analysis in the supplementary material, examining the model’s behavior under challenging real-world scenarios such as rapid motion, occlusion, or low-light conditions. These observations provide valuable empirical insights into the limitations and stability of our method. In future work, we plan to deepen our understanding of the task by exploring theoretical formulations of cross-view temporal consistency, analyzing generalization behavior under varying lens disparities, and investigating more principled designs for dual-lens fusion. We sincerely appreciate your suggestion, which points us toward an important research direction. We will incorporate these insights and discussions in the updated version to clarify the rationale behind our design and highlight the potential directions for theoretical analysis. \n\n`>>Q3: Dataset limitation.`\n \nA3: Thank you for raising this important point. While we train our model on the RealMCVSR dataset, our evaluation is not limited to it. To assess the generalization capability of AsLeD-Net, we additionally test on independently captured real-world dual-lens videos using an iPhone 14 Plus, which differs significantly from the iPhone 12 Pro Max used for RealMCVSR in terms of lens hardware, sensors, and ISP pipelines. This setup naturally introduces diversity in camera characteristics and scene dynamics. The additional real-world scenes include both daytime and nighttime conditions, with complex object motion and natural camera shake. We retain the native spatial and photometric misalignments between wide and ultra-wide views, without applying any spatial registration or color correction. These non-aligned inputs reflect realistic deployment scenarios. Our model is specifically designed to handle such cross-view asymmetries through learned feature-level alignment via modules like ALM and RMC. As demonstrated in Figure 7 and the supplementary Figure 10, AsLeD-Net performs well even in these challenging real-world settings, indicating strong generalization beyond curated datasets. This suggests that our method does not overfit to the training domain and instead learns transferable representations for dual-lens deblurring. We agree that building a larger-scale, cross-device real-world dataset with broader coverage of lighting and motion conditions will further improve the evaluation protocol. We plan to incorporate such data collection and training strategies in future work and will clarify this in the revised version.\n\n`>>Q4: Generalization to different dual-lens systems.`\n\nA4: Thank you for the valuable question. AsLeD-Net is designed to generalize across different devices. While we train on the RealMCVSR dataset (captured with iPhone 12 Pro Max), we evaluate on real-world dual-lens videos from an iPhone 14 Plus. This naturally introduces variations in lens baseline, sensor quality, ISP pipeline, and color response. Despite these differences, AsLeD-Net performs consistently well without additional tuning, as shown in Figure 7 and the supplementary material. This indicates that the model captures robust features for motion compensation and structure restoration, rather than relying on device-specific characteristics. Importantly, our method does not assume strict alignment or uniform lens quality. It explicitly addresses spatial and photometric mismatches through the ALM and RMC modules. These real-world asymmetries are preserved during both training and testing to reflect practical deployment scenarios.\n\n`>>Q5: Are all three modules (ALM, DC, RMC) equally necessary?`\n\nA5: Thank you for the thoughtful question. Each of the three modules plays a distinct and complementary role in the AsLeD-Net framework. Our ablation study shows that removing any of them leads to a clear drop in both PSNR and perceptual quality.\n- ALM learns spatial alignment between the wide and ultra-wide views, addressing the geometric disparities across lenses.\n- DC handles residual structural mismatches, enhancing local detail consistency between views after alignment.\n- RMC refines motion estimation by incorporating cross-view guidance, which is critical for temporal coherence and blur removal.\n\nWhile the full combination yields the best results, we agree that a lightweight variant with fewer modules could be useful in latency-sensitive settings. For instance, in simpler scenes, a reduced configuration such as ALM + RMC may still offer good trade-offs. We plan to explore adaptive module selection and pruning strategies to further optimize efficiency in future work.\n\n`---SUMMARY---`\n\nWe sincerely thank Reviewer H9KQ for the constructive and thought-provoking feedback. We are especially grateful for your recognition of our work’s practical relevance, clarity of presentation, and strong experimental results. Your comments on novelty, theoretical grounding, dataset limitations, and generalization offered valuable perspectives that prompted deeper reflection and improvements.\n\nIn response, we clarify that while our modules build on known techniques, they are thoughtfully adapted and integrated for the novel and underexplored task of asymmetric dual-lens video deblurring. We support this with extensive ablations and qualitative analyses. We acknowledge the importance of theoretical insights and plan to enrich the next version with discussions on limitations, stability, and possible analytical directions. Additionally, we demonstrate generalization through real-world cross-device testing and explicitly handle spatial/photometric misalignments without pre-alignment assumptions. We also address the necessity of each module and outline paths for efficient adaptation.\n\nWe believe your suggestions have helped us better articulate the contributions and guided future work directions toward broader generalization, theoretical understanding, and practical deployment. Thank you again for your insightful and encouraging review."}, {"review_id": "GX2t1XoVV1", "reviewer": "Reviewer_Rwp8", "summary": "The paper extends the task of dual-lens video deblurring to asymmetric lenses and proposes to leverage complementary information inherent in videos captured from wide angle and ultra wide angle lenses. To this end, the paper proposes a method to deblur videos achieving strong results on one synthetic blur dataset (RealMCVSR) and qualitatively evaluates on real-world blur videos captured from an iPhone 14 Plus.", "questions": "Other than the ones mentioned in the weaknesses, I have a few minor questions.\n\n**Q1.** In RGMC module, is there any supervision on the refined flow or is it implicit? If it is implicit, why?\n\n**Q2.** How does the optical flow between $I_{t}^{B}$ and $I_{t-1}^{Ref}$ differ from $I_{t}^{B}$ and $I_{t-1}^{B}$? \n\n**Q3.** There are also spatial and color misalignments even after center crop [1]. In the iPhone 14 Plus videos, were these corrections made?\n\n[1] Cai, Jianrui, et al. \"Toward real-world single image super-resolution: A new benchmark and a new model.\" Proceedings of the IEEE/CVF international conference on computer vision. 2019.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n\n**S1.** The proposition to leverage benefits of dual-lenses, even in asymmetric camera layouts, is fresh and goes beyond the usual single lens video deblurring methods.\n\n**S2.**  The proposed method performs competitively on both datasets, and the restored videos seem pleasing to the eye.\n\n**S3.** The paper thoroughly discusses its strengths and limitations which I find to be very refreshing.\n\n**Weaknesses**\n\n**W1.** A few things are unclear, especially in the ALM (Adaptive Local Matching) module. In eq. 1, the cosine similarity is computed between the base features and the reference features (I assume along the spatial dimension). Next, in eq. 3, weighted k-nearest neighbours are obtained from the reference frame and are fused with the base features in eq. 4. Where is the cosine similarity used here? Further, the fusion in eq. 4 is done through $g_{\\theta}$ which is an adaptive layer. What is an adaptive layer?\n\n**W2.** The authors only test the proposed method on one dataset, RealMCVSR. It is hard to understand the general competitiveness of the method without a thorough analysis. Have the authors considered another dataset e.g., MVSR [1] (which is also dual-lens dataset and for SR like MCVSR)?\n\n[1] Wang, Ruohao, et al. \"Benchmark dataset and effective inter-frame alignment for real-world video super-resolution.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023.\n\n**W3.** Authors mention that they test on a real-world iPhone 14 Plus dataset, but there is no discussion on how many videos (and frames within a video) does the dataset contain? How was the dataset captured in terms of latency in capturing from both lenses i.e., are two frames (from wide and ultra-wide lenses) synchronous? I would assume that in practice, there is some time lag if capture process happens sequentially (shutter open/close to capture a photo from each lens). Further, have the authors considered computing non-reference metric on the restored videos on this dataset to get a more complete picture of the results?", "was_revised": true, "final_justification": "The authors clarified most of my concerns. My only remaining concern is limited evaluation, but I believe the method and approach is interesting enough to be discussed. I encourage the authors to include non-reference metrics for the iPhone 14 dataset they collected (even if small) so that some quantitatively analysis of the method and prior art can be done. Overall, I am positive about this work.", "rebuttal": "We sincerely thank you for your positive feedback. We're glad you found our formulation of asymmetric dual-lens video deblurring to be novel and practical (S1), appreciated the strong visual performance on both synthetic and real-world data (S2), and valued our transparent discussion of strengths and limitations (S3). Your encouraging remarks reinforce the practical relevance and potential impact of AsLeD-Net. We address your concerns in detail below.\n\n`>>Q1: The cosine similarity and the adaptive layer.`\n\nA1: The cosine similarity computed in Eq. 1 is used to build a similarity matrix between blurry (query) and reference features in the spatial dimension. These similarity scores are used to determine the top-K nearest neighbors (Eq. 3) and are then normalized via softmax to compute the aggregation weights for feature fusion. The “adaptive layer” $g_\\theta$ refers to a lightweight 1×1 convolution applied to the concatenated query and aggregated reference features. Its role is to learn an adaptive fusion of the two inputs, enabling the network to selectively emphasize relevant structural cues. While more complex fusion strategies could be adopted, we deliberately choose this lightweight design for its efficiency and clarity. It not only reduces computational overhead, but also highlights the importance of aggregating similar features in a simple and effective manner. We will clarify these in the updated version.\n\n`>>Q2: The MVSR dataset.`\n\nA2: Thanks for pointing this out. We are aware of the MVSR dataset and acknowledge its value for evaluating dual-lens video processing. However, due to computational constraints and differences in task setup, we chose to conduct our initial study on the RealMCVSR dataset, which is smaller in scale and better suited for a proof-of-concept evaluation. Despite its size, RealMCVSR includes diverse real-world mobile scenes and is sufficient to validate the core design of our proposed modules. When computational resources allow, using larger-scale datasets for training is expected to further improve performance.\n\nWe agree that MVSR can serve as a useful benchmark for generalization evaluation. In the updated version, we plan to include cross-dataset testing using MVSR to assess how well AsLeD-Net generalizes to other dual-lens scenarios. However, we note that MVSR does not natively provide blurry inputs for deblurring. Similar to RealMCVSR, it would require synthetic blur generation, which introduces additional design choices and potential bias depending on the simulation strategy. In contrast, our real-world test set contains naturally captured blurry-reference video pairs with real camera shake and dynamic motion, providing a more practical and realistic evaluation setting. Please refer to Figures 7 and 8 for qualitative examples. We will add these clarifications in the updated version.\n\n`>>Q3: Testing on real-world iPhone 14 Plus data.`\n\nA3: Thanks for the question. We would like to clarify a potential misunderstanding: we did not construct a large-scale real-world test dataset. Instead, we captured *several challenging real scenes to assess generalization*, covering both daytime and nighttime scenarios with complex object motion and camera shake. As shown in the main paper and supplementary material, our method demonstrates strong restoration performance in these challenging cases. For data collection, we utilize the “DoubleTake” app from the iPhone App Store, which enables simultaneous video recording from both wide and ultra-wide lenses. This ensures perfect temporal alignment without frame lag or desynchronization. We appreciate your suggestion regarding non-reference metrics. Due to current resource limitations, we conduct our experiments on a limited-scale dataset. In future work, we plan to capture a larger real-world dataset for both training and evaluation, which would enable the use of perceptual metrics such as LPIPS for real-world quality assessment. Interestingly, we also highlight some failure cases in the limitations section, which reflect challenges encountered in real-world capture (e.g., extreme exposure shifts, fast motion). We believe *these issues could be better addressed by training on a broader and more diverse real-world dataset*—a direction we are actively exploring.\n\n`>>Q4: Refined flow in the RMC module.`\n\nA4: Thanks for the question. The refined flow in RMC module is not directly supervised. We adopt an implicit learning strategy for several reasons: First, while we use SpyNet to generate a reliable initial flow, we observe that challenging regions (e.g., fast motion or occlusion) still suffer from noticeable errors (L193-198). The RMC module is thus designed to refine the initial flow in a content-adaptive manner. Second, we do not have ground-truth flow annotations, making direct supervision infeasible. Moreover, even if one supervises the warped frames, it remains an implicit form of supervision. In such cases, the network is still optimized based on its utility in restoring the image, rather than on flow accuracy itself. Third, our goal is *not to estimate precise optical flow*, but to achieve effective motion compensation for better frame restoration. This is *in line with prior works* such as BasicVSR and BasicVSR++, where optical flow is used as an auxiliary intermediate representation and is not directly supervised. These methods supervise the final RGB reconstruction instead, which is also the strategy we adopt. We will clarify this design choice in the updated version to avoid confusion.\n\n\n`>>Q5: Two types of optical flow.` \n\nA5: Thanks for the question. The flow between $I^B_t$ and $I^B_{t-1}$ captures temporal motion within the same camera view, which follows standard inter-frame motion estimation. In contrast, the flow between $I^B_t$ and $I^{Ref}_{t-1}$ involves not only temporal alignment but also cross-view motion between two asymmetric lenses. This flow must account for *differences in field-of-view, perspective distortion, and potentially resolution, making it more complex but also more informative*, especially in challenging regions. We include both types of flow because they serve complementary purposes. While the base-to-base flow provides temporal cues, it can be unreliable under strong blur. The cross-view flow, on the other hand, leverages the sharper reference from the alternate lens to guide compensation where the base view alone is insufficient. Together, they enable more accurate alignment and robust restoration in degraded scenarios. We will clarify this in the updated version.\n\n`>>Q6: Spatial and color misalignments.` \n\nA6: Thanks for the question. We do not apply explicit spatial or color correction to the real-world iPhone 14 Plus videos. This is a deliberate design choice rooted in the nature of our task. Unlike super-resolution, which requires strict pixel-wise alignment, our dual-lens video deblurring task does not assume perfectly paired inputs. On the contrary, it aims to leverage a *cross-view reference* from an asymmetric lens—naturally introducing spatial shifts, field-of-view differences, and mild color inconsistencies. Rather than removing these discrepancies via preprocessing, we embrace them as part of the problem setting. Our goal is to *exploit the complementary sharpness* from the auxiliary lens, even under misalignment. Forcing strict alignment through cropping or calibration may reduce the method’s applicability and robustness. Instead, we rely on our modules (e.g., RMC, ALM) to learn adaptive alignment at the feature level, enhancing generalization across diverse hardware conditions. This design better reflects *real-world deployment scenarios*, where perfect alignment is often infeasible. That said, we acknowledge that advanced pre-alignment techniques (e.g., warping or color normalization) may further improve performance and will consider them in future work.\n\n`---SUMMARY---`\n\nWe sincerely thank Reviewer Rwp8 for your thoughtful, detailed, and constructive feedback. Your recognition of our novel problem formulation, strong visual results on both synthetic and real-world data, and our transparent discussion of strengths and limitations is deeply appreciated and highly encouraging. We have carefully addressed each of your comments with detailed clarifications of our design decisions and implementation choices. Your questions prompted us to reflect more deeply on our methodology, and we believe the resulting revisions will meaningfully improve the clarity, rigor, and completeness of the paper. We hope our responses have resolved your concerns, and we are truly grateful for your insightful suggestions. They have not only strengthened the present work but will also guide our future efforts in dual-lens video restoration and the broader domain of mobile computational photography."}]

Author rebuttal:

Meta-review: This paper received (3) Borderline Accept and (1) Accept. The main contribution of this paper is the introduction of a new framework for video deblurring from asymmetric dual-lens camera system consisting of an adaptive local matching module and a difference compensation module to aggregate local feature and reduce misalignment, respectively to improve framewise consistency for the deblurring process.

All reviewers acknowledge that the proposed method is new and achieves good performance on the synthetic and real datasets. Meanwhile reviewers raise concerns about 1) misleading claim as the first paper on the topic and ALM analysis 2) unclear generalisation to other dual-lens settings; 3) lack detailed discussions on the computational overhead. 4) necessity of using three modules in the framework. 5) unclear setup for testing in real scene.

Authors have provided detailed replies to reviewers regarding their concerns. Consequently, reviewers are satisfied with the response by the authors and encourage the authors to include the clarifications in their final version.

AC concurs with reviewers’ comments and would recommend Accept.
Decision: Accept (poster)