Unraveling Motion Uncertainty for Local Motion Deblurring

Authors: zeyu  xiao
zhiwei  xiong
xinchao 2024 wang
zhihe  lu
michael bi mi
Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-03-12 04:19:14

Abstract: In real-world photography, local motion blur often arises from the interplay between moving objects and stationary backgrounds during exposure. Existing deblurring methods face challenges in addressing local motion deblurring due to (i) the presence of arbitrary localized blurs and uncertain blur extents; (ii) the limited ability to accurately identify specific blurs resulting from ambiguous motion boundaries. These limitations often lead to suboptimal solutions when estimating blur maps and generating final deblurred images. To that end, we propose a novel method named Motion-Uncertainty-Guided Network (MUGNet), which harnesses a probabilistic representational model to explicitly address the intricacies stemming from motion uncertainties. Specifically, MUGNet consists of two key components, i.e., motion-uncertainty quantification (MUQ) module and motion-masked separable attention (M2SA) module, serving for complementary purposes. Concretely, MUQ aims to learn a conditional distribution for accurate and reliable blur map estimation, while the M2SA module is to enhance the representation of regions influenced by local motion blur and static background, which is achieved by promoting the establishment of extensive global interactions. We demonstrate the superiority of our MUGNet with extensive experiments.

Main paper: 
 introduction: 
 Image deblurring is a critical task in the fields of image processing, aiming to recover clear and detailed images from blurry ones caused by various factors, such as camera shake [29, 34] , object motion [30, 43, 56] , and being out-of-focus [1, 2, 20] . Motion blur removal presents a significant challenge in image deblurring, primarily due to arbitrary localized blurs and uncertain blur extents, sparking considerable interest in the research community. This problem can be broadly categorized into two main types: global motion deblurring and local motion deblurring. Global motion blur uniformly affects the entire image, while local motion blur is constrained to specific regions within the image, appearing selectively in certain parts of the scene. Advances in image deblurring techniques have the potential to benefit numerous fields, including photography, multimedia, medical imaging, remote sensing, surveillance, and computer vision, by enabling clearer image representations and facilitating more accurate image-based tasks and analyses.A straightforward strategy is to apply global deblurring techniques to local blurred images to achieve effective local motion deblurring. However, this approach may not yield optimal results (see Figure 1 ), as local motion blur often presents distinct characteristics compared to global motion blur. Specifically, local motion blur is characterized by abrupt changes at object boundaries, while the background remains clear. This discrepancy poses challenges for global deblurring methods and may result in undesired artifacts in originally sharp regions. Therefore, specialized methods that explicitly address the unique problems of local motion blur are essential to ensure accurate and artifact-free deblurring in such scenarios, which has yet to gain limited attention. Thanks to [21] , the first paired real, local motion blur dataset -ReLoBlur, and an end-to-end framework called LBAG for local motion deblurring have been proposed. Specifically, LBAG incorporates a gated structure to alleviate the deblurring impact on non-blurred regions, preventing unwanted distortion in the background and static objects. While LBAG represents a significant step forward in this direction, it still lacks local blur detection. This is because LBAG employs a gated structure to directly multiply estimated blur masks with blurry images, often resulting in sub-optimal results, as shown in Figure 1 . There are several core challenges for identifying local motion blur: (i) addressing abrupt changes at object boundaries, where blur intensity can vary significantly within a confined spatial area; (ii) capturing the intricate and irregular patterns often exhibited by local motion blur, posing challenges for modeling and prediction; (iii) dealing with occlusions and object movements, which introduce additional complexity to accurately identifying local motion blur.In this paper, we introduce a novel method known as the Motion-Uncertainty-Guided Network (MUGNet) to address the intricate challenges of local motion deblurring. Given the complexities posed by random blur localization, elusive extents, and indistinct motion boundaries, our MUGNet involves estimating motion uncertainty maps by applying the motion-uncertainty quantification (MUQ) module. To achieve this, we draw inspiration from the Bayesian probability theory [3, 12, 14, 15, 23, 25, 51, 59] , and structure our MUQ module as a probabilistic representational model, steering clear of pixel-level blur mask estimations and instead learning probability distributions. Concretely, by drawing 𝐾 samples from these acquired distributions, we are able to generate initial estimates and quantify the degrees of motion uncertainties. Incorporating the MUQ module significantly enhances the capability to estimate more precise blur masks, which is crucial for local motion deblurring. Furthermore, we introduce the motion-masked separable attention (M2SA) module, which uses the Multi-Dconv Head Transposed Attention (TA) to handle both blurry objects and stationary backgrounds. Instead of performing the same operations in each head, our M2SA separates the attention heads into two groups: the masked-TA (MTA) enhances the representation of blurry areas, and the global-TA (GTA) can build global interactions for generating high-quality clear images.Utilizing our proposed Motion-Uncertainty-Guided Network (MUGNet) with the newly introduced Motion-Uncertainty Quantification (MUQ) module and Motion-Masked Separable Attention (M2SA) module, we adopt a multi-scale architecture to generate blur-free images. This integration allows MUGNet to produce remarkably enhanced results with finer details, surpassing state-ofthe-art methods. To validate the efficacy of our approach, extensive experiments are conducted on the ReLoBlur testing dataset [21] , demonstrating superior performance across various local motion deblurring scenarios.The contributions of this work are summarized as follows:•  
related work: 
 Global motion deblurring. Image deblurring poses a challenging ill-posed problem [24, 40, [44] [45] [46] [47] [48] , seeking to recover clear images from their blurry counterparts. A range of regularization priors, including heavy-tailed gradient, sparse kernel, 𝑙 0 gradient, normalized sparsity, and dark channels, have been developed to guide the solution space towards sharp latent images [9, 17, 28, 33, 49] . In recent times, the advancement of deep learning has brought transformative progress to image deblurring, notably within the domain of global deblurring [31, 36, 50, 54, 57, 58, 61] . Pioneering deep global motion deblurring efforts have embraced Convolutional Neural Networks (CNNs) as foundational components, yielding substantial improvements in image quality. Among these, DeepDeblur [27] stands out, employing a multi-scale CNN architecture with residual blocks to expedite convergence. Innovations like DeblurGAN [18] and DeblurGAN-v2 [19] have harnessed Generative Adversarial Networks (GANs) and perceptual loss to enhance subjective image quality. HINet [5] h leverages Instance Normalization for performance gains. Harnessing the potential of vision Transformers [8] to capture long-range dependencies, their application in global deblurring tasks has ignited substantial interest. For instance, Uformer [41] employs window-based self-attention coupled with a learnable multi-scale restoration modulator, effectively capturing local and global dependencies. Concurrently, Restormer [52] exploits TA and a feed-forward network, enabling intricate long-range pixel interactions. Unlike these methods, in this paper, the proposed MUGNet aims to deblur locally blurred images.Local motion deblurring. Local motion deblurring, less explored, tackles blurs in specific areas caused by object movements with stationary cameras [21, 22] . A notable stride in this direction has been taken by [21] , who have undertaken a noteworthy endeavor in curating the pioneering ReLoBlur dataset, a foundational resource  
method: 
 Given a blurry image 𝐵 ∈ R 𝐻 ×𝑊 ×3 , the proposed MUGNet aims to reconstruct a high-quality clear image Î ∈ R 𝐻 ×𝑊 ×3 , which should be close to the ground truth image 𝐼 𝐺𝑇 ∈ R 𝐻 ×𝑊 ×3 . 𝐻 and 𝑊 denote the height and width.We utilize a multi-scale encoder to extract the features of the blurry image since multi-scale structures prove effective in image deblurring. Specifically, we feed 𝐵 into the encoder to generate multi-scale feature maps from four stages based on ResNet, which are denoted as {𝐹 𝑖 } 𝑖=2 , and the upscaled feature are concatenated and fed to SE-Residual Blocks (SERBs), followed by mask generators to estimate blur masks {𝑀 𝑖 } 4 𝑖=2 . The mask generator consists of a 3 × 3 convolution, followed by the sigmoid operation. Note that we do not binarize the blur maps but keep them as continuous maps ranging from 0 to 1. Then, the M2SA module is applied at each feature level to enhance the distinction between blur regions and static background regions, generating {𝐹 where Conv(•) denotes the 3 × 3 convolution.Accurately estimating blur masks is crucial for achieving precise deblurring results. In practice, however, the task of identifying and estimating blur masks for local motion blur is notably challenging, primarily due to its distinctive and unpredictable characteristics. Therefore, simply feeding features into the following convolution layers to estimate blur masks is not optimal. Inspired by Bayesian probability theory [3, 12, 14, 15, 23, 60] , we propose to estimate the motion uncertainty maps using the MUQ module and then generate more accurate blur masks. We employ uncertainty estimation to derive blur masks, aligning with local blurry artifacts' distinctive characteristics.As shown in Figure 3 , we design the MUQ module as a probabilistic representational model to measure motion uncertainty. Therefore, what the MUQ module delivers for each pixel (e.g., the pixel 𝑝) is a distribution parameterized by mean 𝝁 𝑝 and variance 𝝈 𝑝 instead of a scalar (e.g., a score). Following [12] , we model the distribution of outputs at each pixel as Gaussian, and therefore, the prediction of the MUQ module is a random variable. We expect that the blur score at the position 𝑝 can be drawn from the learned distribution: 𝑢 𝑝 ∼ N (𝝁 𝑝 , 𝝈 𝑝 ), where 𝝁 𝑝 and 𝝈 𝑝 are learned by a two-branch encoder consists of a 1 × 1 convolutionEQUATIONwhere 𝝁 ∈ R ℎ×𝑤×1 and 𝜹 ∈ R ℎ×𝑤×1 denote the mean map and variance map, respectively. ℎ and 𝑤 denote the height and width of 𝐹 , and we omit the subscript 𝑖. As observed in existing studies [10, 12, 13] , using random samples to train the MUQ module can lead to the lack of error propagation from the output. To address this issue, taking inspiration from [15] , we decompose the direct sampling operation into two components: trainable and random parts. In particular, we start by randomly drawing a sample 𝜖 𝑝 from the standard Gaussian distribution N (0, 𝑰 ), i.e., 𝜖 𝑝 ∼ N (0, 𝑰 ), and then compute the sample as 𝝁 + 𝜎𝜖 𝑝 . This approach allows gradients to propagate backward, enabling the optimization of the MUQ module. We sample 𝐾 initial blur masks from the learned distribution to measure pixel-wise motion uncertainty, denoted as 𝑀 𝑖𝑛𝑖𝑡 = {𝑚 1 , • • • , 𝑚 𝐾 }. According to Bayesian probability theory [3, 12, 14] , we can treat 𝑀 𝑖𝑛𝑖𝑡 as empirical samples from an approximate predictive distribution and measure how confident the model is in its prediction by computing the varianceEQUATIONwhere 𝑈 ∈ R ℎ×𝑤×1 means the motion uncertainty map, Norm(•) is the mean-max normalization operation and Var(•) denotes the operation of computing variance. The estimated motion uncertainty map effectively guides the generation of blur maps within our proposed MUQNet framework, resulting in improved precision and quality.Blurred regions exhibit diverse scales and frequently share visual characteristics with the background, presenting inherent challenges for local motion deblurring. Using estimated blur masks offers a valuable tool to explicitly differentiate and process the blurry objects and the static background. This approach not only enhances the feature representations but also contributes to a more effective deblurring process, ultimately leading to improved results. We therefore propose the M2SA module to utilize a subset of attention heads to independently compute attention scores for the predicted blurry areas and static background regions, enhancing the feature representation.Our M2SA module is based on a modified version of self-attention to save computations, namely TA [52] . Given an input 𝑋 ∈ R ℎ𝑤×𝑐 where ℎ and 𝑤 are respectively the height and width while 𝑐 is the 𝝁 X 𝓝(0, 𝐈) channel number, TA can be formulated as:Sample Quantify Initial Estimations Blur map 𝑈 Enc. X Multiply MTA TAEQUATIONwhere 𝑄, 𝐾, 𝑉 are the query, key, value matrices that can be generated by using three separate 1 × 1 convolutions followed by a 3 × 3 depthwise convolution, and 𝛼 is a learnable scaling parameter. Eq. 4 can also be extended to a multi-head version, as done in the original self-attention [39] , to enhance the feature representations. The attention heads in the above TA are equally utilized for encoding spatial information, and we term these heads as global TA (GTA). In our M2SA, we propose introducing the estimated blur maps at each feature level into TA to better distinguish between blurry objects and static backgrounds and enhance the network's representation. To achieve this, we divide all attention heads into mask-head TA (MTA) and the regular GTA. The detail of MTA is shown in Figure 4 .To be specific, given a predicted blur map 𝑀, the formulation of MTA can be written asEQUATIONwhere 𝑄 𝐹 , 𝐾 𝐹 are the masked query and key matrices that can be produced by multiplying them with 𝑀 and 𝑉 𝐹 is the value matrix without masking. In this way, the features can be refined by building pairwise relationships within blurry objects in the foreground, avoiding the influence of the background, which may contain contaminative information.Other than the MTA heads, the second group of the heads is kept unchanged as in Eq. 4, which is used to build relationships between the foreground and background globally. The outputs of all heads are then concatenated and sent into a 3 × 3 convolution for feature aggregationEQUATIONwhere [•, •] is the concatenation operation.Following [21] , we utilize several loss functions to optimize the proposed MUGNet. Blur mask prediction loss L 𝑀 . We utilize the blur mask prediction loss to constrain our estimated blur masks effectively. Specifically, we employ the Mean Squared Error (MSE) loss to compute the loss across multiple scales, which in turn contributes to the effective regularization and refinement of our blur mask estimation. L 𝑀 can be denoted asEQUATIONwhere 𝑀 𝐺𝑇 denotes the ground-truth local blur mask, and 𝑖 denotes the 𝑖-th level in MUGNet.Reconstrction loss L 𝑟𝑒𝑐 . L 𝑟𝑒𝑐 is proposed to regularize the deblurred results at the pixel level viaEQUATIONwhere Î𝑖 = 𝑀 𝑖 𝐵 𝑖 + 𝐵 𝑖 denotes the multi-scale latent clear result at the 𝑖-th level. SSIM loss L 𝑠𝑠𝑖𝑚 . The SSIM loss is also proposed to regularize the deblurred results at the pixel level viaEQUATIONwhere SSIM(•, •) denotes the SSIM value.Multi-scale frequency reconstruction (MSFR) loss L 𝑚𝑓 . The MSFR loss measures the 𝐿 1 distance between multi-scale groundtruth and deblurred images in the frequency domain as followsEQUATIONwhere 𝑓 (•) denotes the fast Fourier transform that transfers the image signal to the frequency domain.The final loss function for training our MUGNet is determined as followsEQUATIONwhere 𝜆 1 , 𝜆 2 , 𝜆 3 are weighting factors. 
experiments 4.1 experimantal setting: 
 Datasets. We follow the method in [21] and utilize the ReLoBlur dataset [21] , for training and validation. We split the ReLoBlur dataset into 2,010 pairs for training and 395 pairs for testing, without repeated scenes occurring in each split set.Test setting. We evaluate our MUGNet and baseline methods on ReLoBlur testing data with the full image size of 2152 × 1436.To quantitatively evaluate the reconstructed results, we use the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. We follow [21] and calculate the weighted PSNR, the weighted SSIM [32] , and the aligned PSNR [11] specifically for the blurred regions. Implementation details. We employ the Adam optimizer with parameters 𝛽 1 = 0.9 and 𝛽 2 = 0.999 to train MUGNet. The hyperparameters are set empirically as follows: 𝐾 = 50, 𝜆 1 = 100, 𝜆 2 = 100, and 𝜆 3 = 10. The batch size is configured as 12, and the initial learning rate is set to 1 × 10 -4 , which is halved every 100k steps until reaching 300k steps. All training experiments are conducted using PyTorch on an NVIDIA 3090 GPU. During the data sampling process, we employ a blur-aware patch crop strategy [21] . This strategy involves sampling 50% of the training data as blur regions and the remaining 50% as random regions from the 256 × 256 training samples, with the assistance of blur mask annotations. It is worth noting that the model configuration for the baseline methods adheres to their respective original specifications.We conduct a comprehensive evaluation of the proposed MUGNet using the ReLoBlur dataset, benchmarking it against the following existing methods: (1) CNN-based global motion deblurring methods: DeepDeblur [27] , DeblurGAN-v2 [19] , SRN-DeblurNet [36] , MIMO-UNet [6] and HINet [5] . (2) Transformer-based global motion deblurring methods: Restormer [52] and Uformer [41] . (3) State-ofthe-art local motion deblurring method LBAG [21] and its variant LBAG+ pretrained with MIMO-Unet. As depicted in Table 1 , MUGNet significantly excels in both PSNR and SSIM metrics. With a PSNR of 36.31 dB, it notably surpasses its nearest competitor, Uformer, by 1.12 dB. Moreover, MUGNet achieves a substantial PSNR improvement of 1.46 dB over the leading state-of-the-art local motion deblurring method, LBAG. In terms of SSIM, MUGNet outperforms Transformer-based methods by 0.0058 and surpasses LBAG+ by 0.0066, underlining its remarkable prowess in local motion deblurring. Similarly, in terms of both PSNR 𝑤 and PSNR 𝑎 metrics, MUGNet achieves the most superior results. Despite MUGNet exhibiting a slight reduction of 0.0013 in SSIM 𝑤 compared to Uformer, it is noteworthy that our model employs only approximately half of the parameters and incurs a lower computational load. This efficient utilization of resources underscores the practical viability and computational advantage of MUGNet.As demonstrated in Figure 5 , MUGNet exhibits a remarkable superiority over its contemporary counterparts, yielding images characterized by enhanced clarity and intricate details. Notably, there is a substantial reduction in blur, resulting in the faithful preservation of intricate patterns, such as the pristine white stripes on the suit and the intricate nuances of the zipper. These restored details closely resemble the ground truth; notably, no discernible artifacts are introduced. These significant enhancements in visual Effectiveness of each module. We conduct experiments to showcase the contributions of the two core modules in our proposed MUGNet. We design the following ablations: we observe substantial improvements across all evaluation metrics. This performance gain underscores the significant contributions of both components to the overall enhancement. From the visual evidence in Figure 6 , it becomes evident that removing the MUQ module results in noticeable artifacts along the edges of moving objects, while the absence of the M2SA module leads to a loss of fine details in the output. This visual observation harmonizes perfectly with our analysis and the results presented in the tables. It underscores the importance of incorporating motion uncertainty for effective local motion deblurring and further highlights the significance of the M2SA module in enhancing feature representation. This underlines the utility of the M2SA module in improving local motion deblurring results.A close look at the MUQ module. To understand how the MUQ module works, we visualize the motion uncertainty maps estimated by this module in Figure 7 . Our motion uncertainty maps consistently highlight object boundaries, motion boundaries, and regions with indistinguishable textures. By zooming in, one can see that these boundaries indeed mark the moving objects. As the scale increases, imprecise edges are gradually reduced, resulting in a more accurate blur mask at the largest scale. This coarse-to-fine design enhances precision in blur mask estimation. This phenomenon suggests that these particular regions tend to confuse the deblurring model. The uncertainty quantification process serves meaningful purposes: i) it enhances the interpretability of local motion deblurring models, and ii) it exposes the limitations of conventional solutions. The mask generator is trained to focus on these uncertain regions, resulting in significant improvements in accuracy when estimating clear and precise blur masks, as demonstrated in Figure 7 . Additionally, we have conducted experiments to assess the influence of varying 𝐾 on the final results. Results are shown in Table 3 . Higher values of 𝐾 tend to yield improved outcomes. However, taking computational costs into account, we set 𝐾 = 50.A close look at the M2SA module. Table 4 provides an analysis of the two constituents of the M2SA module, namely MTA and GTA. Within the proposed M2SA module, we employ motionmasked separable attention to enhance the feature representations (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l) (m) (n) (o) (p) (q) (r) (s) (t) (u) (v) (w) (x) (y)Global motion deblurring is a critical task, and despite MUGNet's initial design for local motion deblurring, it proves to be effective in global motion deblurring. We retrain MUGNet and compare its performance with advanced methods. Table 6 presents quantitative evaluations on the GoPro dataset, comparing various deblurring methods in terms of PSNR, SSIM, and the number of parameters. Notably, our proposed method, labeled as "Ours" in the table, achieves competitive results compared to state-of-the-art methods. Despite performing slightly lower than FFTformer in terms of PSNR and SSIM, our method still demonstrates promising performance with a PSNR of 34.01 and SSIM of 0.9628. Additionally, our method maintains a reasonable number of parameters (24.7 million), indicating its efficiency in terms of computational complexity. Overall, these results suggest that our approach holds considerable potential for addressing deblurring tasks on the GoPro dataset, showcasing its effectiveness alongside existing state-of-the-art methods.While our proposed MUGNet demonstrates promising performance across various experiments, it does exhibit certain limitations in challenging scenarios. For instance, when dealing with objects in low light conditions, MUGNet might struggle to produce satisfactory results. As depicted in Figure 8 (left), under normal lighting conditions, our method yields satisfactory results. However, it might not perform as well under weaker lighting conditions, failing to generate clear results in darker areas. These limitations highlight the need for further exploration and refinement, especially in challenging scenarios such as low-light conditions. Another challenging scenario is the presence of extensive motion within the scene. Due to the inherent limitations of CNNs, capturing larger receptive fields and more global information, as achievable by Transformers, becomes challenging. Consequently, reconstructing scenes such as those depicted in Figure 8 (right) proves to be difficult. A feasible solution lies in exploring the temporal information provided by consecutive frames for local motion deblurring. This approach could enhance the network's ability to handle scenes with substantial motion and improve the overall deblurring performance. Like other local motion deblurring methods, our proposed MUGNet currently does not offer real-time local motion deblurring capabilities. Nonetheless, we are committed to refining our architecture to achieve real-time performance, which will enable its application in time-sensitive scenarios.  
 
 Appendix:  


Decision: Although this paper received mixed scores in the first round of review, the rebuttal has successfully reversed the opinions of the reviewer on the negative side. AC believes that this paper should be accepted, and encourages authors to improve the camera ready as they said in the rebuttal.

Reviews: review: paper_summary: paper_summary: The paper proposes a novel network structure to deal with local motion blur in an image.
The network learns to estimate pixel-wise probability representation maps for blur masks on multiple scales, with reparameterization trick to enforce tractability for gradient calculation during training phase.
Global and local attention modules are introduced to further facilitate blur removal.
Comprehensive experiments are carried out to prove the advantage of the proposed method.
strength_weakness: strength_weakness: - Novelty in probability representation for blur mask estimation. A spectrum of uncertainty allows larger margin of error for each module than binarized maps. The design also incorporates Bayes' theorem for a robust process, which hasn't been widely adopted for such tasks.
- Extended experiment results to support the paper's contributions. The qualitative and quantitative results are convincing, with several ablation studies to offer more thorough view on each module.
limitations: limitations: - Accuracy of blur mask estimation is not clear. Although carried out in a probabilistic fashion, actual results of such estimations shown in Figure 7 do not have distinct alignment with intuition. Except of subplot (a) in the Figure, the rest show more noise pattern than useful information, which brings up a question of whether the network is estimation blur mask or not. 
- Marginal improvements. Though demonstrating performance gain, exact numbers of metrics increase are not huge. Considering the fact that no obvious runtime is reduced, the cost-effectiveness of the model design remains questionable.
paper_summary: paper_summary: The paper proposes a Motion-Uncertainty-Guided Network (MUGNet) that addresses motion uncertainties with a probabilistic model, comprising motion-uncertainty quantification and motion-masked separable attention modules. MUGNet outperforms existing solutions, as demonstrated through extensive experiments.
strength_weakness: strength_weakness: 1. This paper is easy-to-follow, with detailed introduction for the method and evaluation.
2. The proposed MUQ module works as a probabilistic model for motion uncertainty quantification, which is an inspiring idea.
limitations: limitations: 1. All quantitative experimental results lack the inclusion of LPIPS, a widely-utilized metric for perceptually evaluating restoration outcomes in low-level methods. The absence of LPIPS considerably undermines the experiments' credibility.
2. Table 1 contains a mis-notation for SSIM_w. MUGNet does not attain the second-best results, as LMD-ViT yields higher scores of 0.8938.
3. The qualitative outcomes of LMD-ViT are omitted in Fig. 5. Furthermore, based on Fig. 3 from LMD-ViT's paper, it appears that the reported result in LMD-ViT's paper (the first case) is quite impressive and closely resembles the MUGNet result shown in Fig. 5 of this submission (the second case). A clarification for this is required in the rebuttal.

I am inclined to accept this submission if the rebuttal addresses the following concerns:
1. providing LPIPS results for quantitative experiments.
2. Issues related to the qualitative results in Fig. 5.
paper_summary: paper_summary: In the submitted manuscript, the authors propose to tackle the local motion deblurring task by leveraging a probabilistic representational model MUGNet. The Motion-Uncertainty Quantification (MUQ) module and Motion-Masked Separable Attention (M2SA) module are further designed to estimate the accurate conditional distribution and enhance the regions affected by local motion blur, respectively. The extensive numerical and visualized results are presented to demonstrate the effectiveness of the method. The paper exhibits logical coherence and the accompanying illustrations facilitate comprehension; however, further clarification is required regarding the innovativeness of the module design.
strength_weakness: strength_weakness: 1. The paper is well-organized and easy to follow.  
 
2. The proposed method is technically feasible and provides a comprehensive review of the relevant work.

3. The sufficient quantitative and qualitative evaluations are provided to substantiate the efficacy of the method. 

4. The paper provides a detailed analysis of the limitations and presents several failure cases for future directions.
limitations: limitations: 1. My main concern is the innovation of the Motion-Masked Separable Attention (M2SA) Module. Combining the global spatial information and masked foreground information by the self-attention based approach seems to be common in the previous computer vision tasks. Perhaps further specific improvements are needed. 

2. Additionally, the structural design of the Motion Uncertainty Quantification Module is widely utilized in Variational AutoEncoder (VAE) or its variants. Further analysis is needed to determine the improvements made in this regard. 

3. The MUQ module is designed to estimate the motion uncertainty map. Have any alternative uncertainty estimation methods been attempted for the sake of comparative analysis?

4. Other minor mistakes or improvements, e.g., 1) There is an extra "and" in the caption "... the best and and the second ..." of Table 1;  2) The SSIMw of  LMD-ViT method ("0.8938") should be in bold in Table 1, not the value of Uformer-B ("0.8911"); 3) The last line of PSNRa in Table 4 is mismatched with the previous results, which should be "36.73";  4) The Swin-Former in Table 5 lacks reference.
paper_summary: paper_summary: This paper aims to address the local motion deblurring problems. The authors propose MUGNet including MUQ and M2SA as key modules. MUQ learns a conditional distribution of motion blur maps. M2SA separates the attention heads into MTA and GTA to enhance the representation of blurry areas and promote global interactions. Experiments on local and global blurry images are conducted.
strength_weakness: strength_weakness: (1) Two novel local motion deblurring modules are introduced: MUQ generates motion blur uncertainty maps, and M2SA improves blurry areas and static background regions by calculating their attention scores separately using the estimated blur masks.

(2) Experiments show promising local motion deblurring performances.
limitations: limitations: (1) What mask is used to supervise the learning of motion blur uncertainty maps? Please cite.

(2) The attention masks do not save computation.

(3)  Since the attention operation is conducted on images with 256*256 resolution while training. During inference, is attention done on the entire image?

(4) Many typos such as "based the Multi-Dconv Head Transposed Attention (TA)" in line 134, and "M2SA module" in line 922.

(5) Many expressions have been seen in published papers like Uformer, LBAG, and LMD-ViT. For example: "local motion blur is constrained to specific regions within the image" (line 48); "a straightforward strategy is to apply global deblurring techniques to local blurred images" (lines 87-89). It would be better if this paper could express a more original understanding of local motion blur. And the introduction part could be more concise.
paper_summary: paper_summary: The paper presents a novel method, the Motion Uncertainty Guided Network (MUGNet), to address the challenge of local motion deblurring. The proposed method aims to improve the accuracy and reliability of blur map estimation by incorporating a probabilistic representational model.
strength_weakness: strength_weakness: 1. The proposed MUGNet includes MUQ module and M2SA module to solve the motion uncertainty problem in local motion deblurring.
2. MUGNet has fewer network parameters with efficient resource utilization and computational advantages.
3. The author provided performance comparisons of local motion deblurring on the ReLoBlur dataset and global motion deblurring on the GoPro dataset. They also highlighted the limitations of the proposed method, which is commendable.
limitations: limitations: (1) The main innovation of this paper lies in the design of the network framework, which introduces the MUQ module and M2SA module. However, the level of innovation requires further exploration:
1. The MUQ module claims to achieve accurate blur mask estimation for local motion deblurring by utilizing a probabilistic model to quantify motion uncertainty. While this approach is more conventional, it would benefit from a more detailed explanation of its innovative aspects.
2. The author presents M2SA as a significant contribution, but in reality, its two major components, MTA and GTA, are only minor improvements based on the Restormer's attention modules. MTA performs element-wise multiplication of the Blur map with Q and K, while GTA simply renames the components.
3. The network architecture designed by the author appears to be redundant and complex, despite having fewer parameters.

(2) Although the experimental settings follow those of LBAG, I have the following concerns:
1. In Table 1, while comparing various methods for both "local motion deblurring" and "global motion deblurring," why didn't the authors include FFTformer for comparison?
2. The analysis of the ablation experiments is comprehensive, which deserves praise. However, regarding Table 4(c) and (d), the MTA module, which is an important component of M2SA (receiving the Blur map), shows minimal improvement in performance. This aspect should be analyzed in more detail.