Title: Asymmetric Dual-Lens Video Deblurring
Venue: NeurIPS 2025
Accepted: yes
Keywords: Image deblurring

Video deblurring
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

Abstract

Modern smartphones often feature asymmetric dual-lens systems, capturing wideangle 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 dualcamera 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.

1 Introduction

Modern smartphones increasingly feature asymmetric dual-lens systems, combining wide-angle 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 differences introduce blur discrepancies. For example, on the iPhone 14 Plus, ultra-wide lenses typically have shorter focal 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 a deeper depth of field, reducing defocus blur but making them more prone to motion blur due to the longer exposure time required for sufficient light intake. In contrast, wide-angle

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lenses with a larger aperture capture more light, allowing for faster shutter speeds and reducing motion blur while maintaining a shallower depth of field that enhances subject-background separation. These complementary characteristics provide valuable cues for video deblurring, as shown in Figure 2.

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 spatiotemporal self-attention to aggregate information across video frames. However, these methods do not leverage the cross-lens redundancy inherent in mobile dual-camera systems. Although several

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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 as 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 in asymmetric multi-camera setups.

AsLeD-Net employs a dual-branch architecture built upon the BasicVSR framework to exploit asymmetric dual-lens characteristics. The base branch uses a 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 -nearest neighbor feature aggregation, effectively transferring structural details. (2) The difference compensation (DC) module: Bridges feature discrepancies between the branches, preserving edge sharpness in fast-moving regions and enhancing spatial-temporal consistency. (3) The reference-guided motion compensation (RMC) module: Aligns features across time steps using optical flow guided by ultra-wide reference frames, eliminating cumulative errors in traditional flow-based warping. The base branch progressively

fuses ALM-refined features, DC-compensated residuals, and RMC-aligned references via attentionguided fusion blocks. This hierarchical integration enables the model to leverage the base branch's reconstruction capabilities and the reference branch's blur-insensitive motion priors. As shown in Figure 2, AsLeD-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 as 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-nearest-neighbor reference aggregation, DC for cross-view spatial consistency, and RMC for temporal alignment. (3) AsLeD-Net 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 learning-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. STFAN [100] employs dynamic filters, while PVDNet [63] integrates a blur-invariant 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-lens image restoration. Asymmetric dual-lens systems, commonly found in smartphones, consist of an ultra-wide lens and a wide lens with different focal lengths. These systems capture the same scene with varying FoVs. 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 as high-resolution references to 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 a synchronized sharp ultra-wide reference. We extend this line from images to video by integrating reference-guided cross-view alignment with recurrent temporal propagation to preserve frame-to-frame coherence.

Reference-based image restoration. Another related topic is reference-based image restoration, which includes tasks such as 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 to enhance the restoration of a degraded input. In this work, we address reference-based video deblurring in the context of asymmetric 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.

3 Method

3.1 Overview

The proposed AsLeD-Net reconstructs a high-quality video ˆ I ∈ R T × H × W × 3 from a blurry input I B ∈ R T × H × W × 3 captured with a wide lens, leveraging a relatively sharper reference video

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I Ref ∈ R T × H × W × 3 taken simultaneously by an ultra-wide len in a dual-lens camera ( e.g. , iPhone). The objective is to restore ˆ I to closely match the ground truth I GT , 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 BasicVSR, reconstructs blurry videos I B captured with a wide lens, using bidirectional frame propagation based on optical flow to extract features F Base . To exploit the unique properties of the AsLeD task, we introduce a 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, we center-crop and resize the reference frames to match the blurry frame size. We leverage the reference branch to extract asymmetric reference information despite potential colour discrepancies due to lens differences.

For the blurry frame I B t and its corresponding reference frame I Ref t at time t , we first feed them to separate feature encoders with non-shared weights to obtain feature representations F B t and F Ref t , each consisting of N 1 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 F t ALM , F DC t , and F t RMC are merged with the base branch's output F Base t by feeding them to residual blocks, resulting in F t . Finally, F t is input into a frame decoder, composed of N 2 residual blocks, to generate the final reconstructed result ˆ I t , 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.

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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-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 or no blur. These frames retain rich structural cues that can guide the restoration of the blurry frame, providing a 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 of the blurred image.

Without loss of generality, we focus on the time step t to illustrate the ALM module. Given the extracted blurry frame feature F B t ∈ R C × H × W at timestamp t , the ALM module aims to refine these blurry local features by incorporating the complementary reference feature F Ref t ∈ R C × H × W captured under similar conditions but with less blur. These deep features can be represented as a set of H × W local representations, each of size C -dimensional. To match the blurry frame with the reference features, we first compute a similarity map S t to measure the cosine similarity between each blurry local feature F B ( i ) t and each reference feature F Ref ( j ) t

where i ∈ { 1 , . . . , N B t } and j ∈ { 1 , . . . , N Ref t } , and N B t and N Ref t represent the number of local representations in the blurry and reference frames, respectively. S ( i,j ) t represents the similarity between the i -th blurry local representation and the j -th reference local representation.

For each blurry local representation, we select its K -nearest neighbors from the reference features and fuse these local representations into a reference representation ϕ ( i ) t,ref

where ∑ K k =1 α ( k ) t = 1 , α ( k ) t > 0 , and α ( k ) t is obtained using the softmax function. The reference representations then adaptively refine the corresponding blurry features. Finally, the blurry and reference representations are concatenated and processed by an adaptive layer g θ to obtain the fused local representations

where ϕ t,fuse represents the fused local representations, and g θ denotes the adaptive fusion layer. The cosine similarities in Eq. (1) form a query-reference affinity used to rank reference tokens; the topK are aggregated with softmax-normalized weights. The adaptive layer g θ in Eq. (3) is a lightweight 1 × 1 convolution applied to the concatenated [ ϕ t, ref , F B t ] , enabling content-aware fusion with low overhead. This process facilitates information fusion by incorporating relevant reference features to enhance the blurry frame representation. Warping 𝑡±1 𝑅𝑒𝑓 𝐼 𝑡 𝐵 SpyNet 𝑂𝑡±1 𝐼𝑛𝑖𝑡 C Residual Blocks 𝐹𝑡 𝐵 𝐹𝑡 𝑅𝐴𝑊

3.3 Difference Compensation

Residual Block 𝑭0→1 𝐼𝑛𝑣 𝑭0→1 𝑟𝑒𝑠 In AsLeD, while blurry and reference images share structural consistency, focus, exposure, and sensor processing differences create subtle feature mismatches, making direct concatenation suboptimal. The DC module provides a

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where e t 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.

3.4 Reference-Guided Motion Compensation

In the base branch of AsLeD-Net, optical flow is computed between the current and previous blurry frames for warping. While this approach has its merits, it often introduces limitations that degrade deblurring performance. Specifically, both the current and previous blurry frames may contain significant motion blur and noise, leading to inaccuracies in optical

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The refined flow is not directly supervised; it is learned implicitly via end-to-end reconstruction loss, in line with prior video restoration designs where flow acts as an auxiliary representation to 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 I B t . The result is then combined with the residual information to obtain the motion-compensated feature F t RMC , which is more accurate in capturing the motion and structural details of the scene. This process ensures that the reference frame's motion is effectively transferred to the blurry frame, improving alignment and reducing artifacts. The above process can be formalized as

By refining the optical flow and leveraging the residual information, the RMC module ensures a 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 multiview 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 as the ultra-wide video but have × 2 and × 4 higher resolutions, respectively. For the

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AsLeD task, we adapt the dataset by using the ultra-wide video as the reference input and the wideangle 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 an iPhone 14 Plus whose two lenses introduce a physical baseline and distinct imaging characteristics, yielding cross-view shifts and photometric differences. We intentionally avoid spatial or color pre-alignment and ensure frame-level temporal synchronization with the DoubleTake 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 × 128 . We apply data augmentation techniques to the training data, including horizontal flips and random rotations of 90 ◦ , 180 ◦ , and 270 ◦ . AsLeD-Net is trained for 300K iterations using the Adam optimizer with a Cosine Annealing learning rate scheduler. Network architecture parameters are set to N 1 = 1 and N 2 = 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 = ∥ ˆ I - I GT ∥ 2 + ε 2 , where ε is set to 1 × 10 -3 in our experiments. We omit the subscript t for simplicity. The initial learning rate for AsLeD-Net is 1 × 10 -4 . 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 a diverse set of baseline methods to evaluate its effectiveness for the AsLeD task. These baselines encompass a wide range of potential approaches, aiming to cover as many varied and rich methodologies as possible: (1) Single-image deblurring and restoration methods: including MIMOUNet [13], MIMOUNet++ [13], NAFNet [11], and Restormer [86]. (2) Video deblurring and restoration methods: including IFIRNN [49], DBN [65], EDVR [71], BasicVSR [9], and BasicVSR++ [8]. (3) Reference-based video restoration method: specifically RefVSR [31]. Notably, some of these methods are not initially designed for deblurring. For approaches primarily intended for super-resolution tasks, we remove the upsampling operation and instead apply a

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 a resolution of 256 × 256 using an NVIDIA GTX 1080 Ti GPU.

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. However, due to limited computational resources (with only a 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.

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 in 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

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(25.15 dB), our approach achieves gains of 0.74 dB and 1.19 dB, respectively. For SSIM, AsLeD-Net achieves 0.9167, improving upon DSTNet (0.9134), BasicVSR++ (0.9121), and RefVSR (0.9124), indicating enhanced structural consistency. Moreover, our method sets a new benchmark in LPIPS (0.1614), significantly surpassing the closest competitor, RefVSR (0.2156), with a 0.0542 reduction, demonstrating the perceptual quality of our restored frames.

Computational cost results. As shown in Table 1, our AsLeD-Net achieves superior performance on the RealMCVSR dataset but comes with a higher model complexity (8.8M parameters) and longer inference time (248.7 ms) compared to lightweight models like IFIRNN (4.1M, 7.6 ms) and DBN (15.3M, 4.2 ms). This limitation hinders its applicability in real-time or resource-constrained

scenarios. This limitation indicates that our method might not be optimal for real-time or resourceconstrained applications. In 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 6 and 7. AsLeD-Net excels in preserving fine texture details, consistently outperforming 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-Net 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 9, highlighting the superior performance of our method in preserving motion coherence and fine details across consecutive frames. Compared to 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.

4.3 Ablation Study

We conduct experiments on RealMCVSR in terms of PSNR/SSIM. Due to space limitations, please refer to the supplementary material.

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 spatial refinement. Within context aggregation, our ALM with k = 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 a 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 bottleneck under realistic motion. Taken together with the ALM study (where k =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 is a practical choice when latency is critical.

5 Conclusion

In this paper, we propose AsLeD-Net, a practical video deblurring method that leverages the complementary 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, AsLeD-Net 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.

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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: The main claims in the abstract and introduction accurately reflect the contributions of AsLeD-Net. 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 are explicitly outlined in Section 1 and Section 3.

2. 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 ms 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 a complete (and correct) proof?

Answer: [No]

Justification: The paper focuses on algorithmic design and empirical validation, with no theoretical derivations or mathematical proofs. Methodology is described through architectural diagrams (Figures 3-5) and equations for feature fusion (Eqs. 1-7).

4. Experimental result reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

Justification: Section 4.1 details datasets (RealMCVSR), 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 repositories or pre-trained models. Reproducibility relies on re-implementation from textual descriptions.

6. Experimental setting/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 to understand the results?

Answer: [Yes]

Justification: Section 4.1 comprehensively describes experimental settings: datasets, blur simulation (7-frame averaging), input resolution ( 128 × 128 ), optimizer (Adam with Cosine Annealing), loss function (Charbonnier), hardware (NVIDIA RTX 3090), and evaluation metrics (PSNR, SSIM, LPIPS).

7. Experiment statistical significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [No]

Justification: Results in Tables 1-3 report PSNR/SSIM/LPIPS values but lack error bars, confidence intervals, or statistical significance tests. Variability factors (e.g., data splits, initialization) are not discussed.

8. 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 to reproduce the experiments?

Answer: [Yes]

Justification: The paper states training hardware (RTX 3090) but omits per-experiment memory usage, total compute costs, or training time. Inference time (248.7 ms) is reported.

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Answer: [Yes]

Justification: The 'Broader impact' section addresses ethical concerns: 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]

Justification: 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.

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA]

Justification: The paper does not release high-risk assets (models, datasets). AsLeD-Net is presented as a research framework without deployment, and no safeguards are discussed.

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Answer: [NA]

Justification: LLMs are not used in methodology, experiments, or analysis.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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?\n\nQUESTIONS:\nOther 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.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-28"}

comments: 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.

STRENGTHS AND WEAKNESSES:
1) Tackles a practically meaningful problem in mobile imaging.
2) Includes thorough experiments on RealMCVSR with clear quantitative gains.

- Weaknesses:

1) 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.
2) Lack of theoretical analysis. The method heavily relies on empirical module stacking; no insights are provided on failure modes, stability, or optimality.
3) 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.

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)?
2) 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.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n--------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.\n\nQUESTIONS:\n- 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.\n\nLIMITATIONS:\nYes, 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\nrefer to the weakness.\n\nLIMITATIONS:\nrefer to the weakness.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-05"}