Regional Attention For Shadow Removal

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-04-06 14:38:52

Abstract: Shadow, as a natural consequence of light interacting with objects, plays a crucial role in shaping the aesthetics of an image, which however also impairs the content visibility and overall visual quality. Recent shadow removal approaches employ the mechanism of attention, due to its effectiveness, as a key component. However, they often suffer from two issues including large model size and high computational complexity for practical use. To address these shortcomings, this work devises a lightweight yet accurate shadow removal framework. First, we analyze the characteristics of the shadow removal task to seek the key information required for reconstructing shadow regions and designing a novel regional attention mechanism to effectively capture such information. Then, we customize a Regional Attention Shadow Removal Model (RASM, in short), which leverages non-shadow areas to assist in restoring shadow ones. Unlike existing attention-based models, our regional attention strategy allows each shadow region to interact more rationally with its surrounding non-shadow areas, for seeking the regional contextual correlation between shadow and non-shadow areas. Extensive experiments are conducted to demonstrate that our proposed method delivers superior performance over other state-of-the-art models in terms of accuracy and efficiency, making it appealing for practical applications. Our code will be made publicly available.

Main paper: 
 introduction: 
 When light interacts with objects, shadows are cast. In some cases, shadows can enhance the photography aesthetics. While in others, they act as an interference factor to image quality [38] , which may degrade the performance of various vision and multimedia algorithms [30, 39] , such as object detection and recognition, and image segmentation. Although this problem has been drawing much attention from the community with significant progress over last years, it still remains challenging for practical use. Because the shadow removal often serves as a preprocessing step for downstream tasks and, more and more systems prefer to deal with images on portable devices anytime and anywhere, besides the high accuracy, its computational cost and model size are expected to be marginal, especially when the computation and memory resources are limited. In other words, a satisfactory shadow removal model shall take into consideration all the removal quality, the processing cost and the model size simultaneously.In the literature, a variety of shadow removal methods [6-8, 11, 13, 19] have been proposed over last years, aiming to mitigate the negative impact of shadows on image quality and enhance the performance of vision algorithms. Traditional approaches heavily rely on hand-crafted priors, e.g., intrinsic image properties, which are often violated in complex real-world scenarios, and thus produce unsatisfactory results. Deep learning techniques [1, 3, 9, 12, 17, 18, 20] have emerged as powerful alternatives, enabling more robust and data-driven approaches to shadow removal. However, most existing advanced shadow removal models barely consider severe model stacking, necessitating substantial computational resources. This issue significantly limits their applicability to potential downstream tasks in real-world scenarios.Let us take a closer look at the target problem. Given an image, shadows typically occupy a part of the image. The goal is to convert involved shadow regions into their non-shadow versions, which should be visually consistent with the non-shadow surroundings in the given image. A natural question arises: is all the information in the entire image equally important for the reconstruction of regions affected by the shadow? Intuitively, aside from the darker color of shadowed areas, the most direct way to discern shadows is by the contrast between the shadowed regions and their neighbor nonshadow areas. From this perspective, we can reasonably assume that the critical information for repairing a certain shadow region should be largely from non-shadow areas around the aim region. Based on this assumption, we propose a novel attention mechanism called regional attention, and customize a lightweight shadow removal network, i.e., RASM. Our design is capable of balancing the efficiency and the accuracy of shadow removal in an end-to-end way. As schematically illustrated in Fig. 2 , the proposed RASM distincts from previous global and local attention methods, which can reduce computational burden and enhance the rationality of information aggregation between restricted non-shadow and target shadow areas. Experimental comparisons on widely-used shadow removal datasets (ISTD+ [24] and SRD [32] ) and ablation studies reveal the efficacy and superior performance of RASM over other SOTA methods in terms of the effectiveness and the efficiency.The primary contributions of this paper are summarized as:• We rethink the key to shadow removal and propose that the information from the regions surrounding the shadows is essential for effective shadow removal. Inspired by this insight, we introduce a regional attention mechanism that allows each shadowed area to aggregate information from its adjacent non-shadowed regions. • We develop a shadow removal network, RASM, based on a novel regional attention mechanism that optimizes the interaction between shadowed and non-shadowed areas, effectively balancing accuracy and computational efficiency. • The comprehensive experimental evaluations conducted on the widely recognized ISTD+ [24] and SRD [32] datasets demonstrate that our proposed method achieved a new state-of-the-art performance with a lightweight network architecture. 
related work: 
 Traditional methods for shadow removal rely on image properties such as chromaticity invariance [7, 8] , gradient consistency [6, 13] or human interactions [10] . The early work in this category can be traced back to [7, 8] , where illumination-invariant images are extracted using a pre-calibrated camera. However, the calibration process is laborious, which limits its practical application. To address this issue, work [6] proposes extracting illumination-invariant images through an optimization procedure that does not require any provenance information of the image. Work [13] aims to develop more sophisticated models for the lit and shadowed areas; however, their methods sometimes fail due to the complexity of shadow generalization and imaging procedures. Some works [13, 22] attempt to address this issue by dividing the shadow removal task into two subtasks: shadow detection and mask-based shadow removal. However, since these shadow detectors rely solely on hand-crafted priors, the removal modules may be affected by inaccurate detection results, which can negatively impact overall performance. Furthermore, traditional methods for removing shadows still encounter difficulties when dealing with complex distortions in real-world scenarios, particularly in the penumbra area. Deep learning has enabled significant advancements in datadriven methods, with works [4, 19] investigating unpaired shadow removal training using generative models. However, these models are typically heavy as they model the distribution of shadow-ed images in a generative manner. In parallel, using a large-scale dataset, Qu et al. [32] were among the first to train an end-to-end deep network for recovering shadow regions. Wang et al. [33] proposed the ISTD shadow removal dataset, featuring manually-annotated shadow masks. However, the non-shadow area of shadow images exhibits substantial inconsistency with the corresponding shadowfree images, as noted in [24] , which proposed mitigating this by transforming the non-shadow area of ground-truths using linear regression. Subsequent studies [1, 3, 9, 17, 18, 29] have explored shadow removal with given shadow masks. Recently, some works have sought to improve the computational efficiency of shadow removal. Zhu et al. [41] proposed a deep unfolding framework for removing shadows efficiently. With the great help of normalizing flow, which reuses the encoder as a decoder, work [40] significantly reduced parameter size. However, representation power was also limited since only half the features were used per layer to ensure invertibility. To address the issue of unsatisfactory boundary artifacts persisting after restoration, Guo et al. [12] proposed the first diffusion-based shadow removal model, which can gradually optimize the shadow mask while restoring the image. Work [21] leverages features extracted from Vision Transformers pre-trained models, which unveil a removal method based on adaptive attention and ViT similarity loss. However, such diffusion-based methods incur significant time and space complexity, leading to significant computational overhead. ShadowFormer [11] proposed re-weighting the attention map in the transformer using the shadow mask to exploit global correlations between shadow and non-shadow areas. However, unlike ShadowFormer, our emphasis lies on the information from non-shadow regions closely surrounding the shadows. We enable each shadow region to integrate information from its immediate non-shadow surroundings. This strategy offers greater flexibility than the window attention used by ShadowFormer and is more aligned with the intrinsic characteristics of shadow removal tasks. Our method achieves superior results without increasing the complexity inherent in ShadowFormer. 
methodology 3.1 problem analysis: 
 In everyday situations, determining the location of shadowed regions often relies on the contrast between the shadowed and adjacent non-shadowed areas. Shadows affect specific areas within an image, resulting in significant differences in brightness, color, and texture compared to non-shadowed surroundings. These differences contain the crucial information required for shadow removal. Sharp transitions in lighting at the periphery of shadowed regions Figure 3 : The first column of images presents scenes with shadows. The highlighted regions in the second column of images represent the non-shadowed areas immediately adjacent to the shadows. We posit that the information from these areas is crucial for the task of shadow removal. usually create distinct gradation zones. Therefore, it is crucial to comprehend and utilize the information from the non-shadowed regions surrounding the shadows to achieve accurate shadow removal, shown in Fig. 3 . The importance of the information from non-shadowed regions increases with proximity to shadowed areas. Analyzing the characteristics of these areas not only allows for the accurate identification of shadow boundaries but also provides the necessary reference data for subsequent shadow restoration. Regional attention mechanisms enable models to focus on the critical areas surrounding the shadows, distinguishing which features are important for the task at hand, thereby facilitating more effective information integration. Contrary to prior studies such as those by [26] and Wang et al. [35] , which predominantly address image restoration tasks characterized by global corruption, shadow removal presents a distinct challenge due to its nature of partial corruption. In this context, the nonshadow regions are of critical importance, as they play an essential role in the restoration of shadow-affected areas. Shadow removal tasks necessitate a large receptive field to assimilate surrounding contextual information effectively. This requirement is rooted in the primary method of distinguishing shadow regions by contrasting them with adjacent non-shadowed areas. Therefore, the primary goal during restoration is to ensure that the reconstructed shadow regions closely resemble their surrounding non-shadow areas, rather than relying excessively on longrange global spatial information. This is where regional attention becomes pivotal, focusing on the information from neighboring regions to enhance the restoration process. The window attention mechanism [26] , which projects queries, keys, and values from the information within a specific window to perform self-attention, is less effective for shadow removal. This limitation arises because the non-shadow information required to address shadow discrepancies varies significantly across different locations. Unlike the Window Attention mechanism utilized in prior works [11, 26] , which generalizes the attention within a window, our proposed regional attention mechanism is specifically designed to tailor the attention to the specific needs of each shadowed area. By doing so, it ensures that each shadow region can access and integrate distinct and locally relevant non-shadow information, thus facilitating more precise and context-aware restoration.To fulfill the outlined objectives, we have devised a Transformerbased network leveraging regional attention, termed RASM, specifically for shadow removal. Predicated on our hypothesis that nonshadow information nearer to shadows is of heightened importance, we allocate a minimal proportion of parameters and computational resources to rapidly process global feature interactions, thus allowing greater computational resources and parameters to be concentrated on regional attention processes. RASM operates as a multi-scale encoder-decoder model. Initially, we utilize Channel Attention Module(CA Module) [16] to effectively capture global information. Subsequently, we introduce a module equipped with regional attention, which harnesses spatial and channel-wise contextual information from non-shadow areas to facilitate the restoration of shadow regions during the bottleneck phase.Architecture. For a shadow input 𝐼 𝑠 ∈ R 3×𝐻 ×𝑊 accompanied by a shadow mask 𝐼 𝑚 ∈ R 𝐻 ×𝑊 , a linear projection LinearProj(•) is initially applied to generate the low-level feature embedding 𝑋 0 ∈ R 𝐶 ×𝐻 ×𝑊 , with 𝐶 representing the embedding dimension. Subsequently, 𝑋 0 is processed through an encoderdecoder framework, each composed of CA modules designed to integrate multi-scale global features. Within each CA module are two CA blocks and a scaling layer-specifically, a down-sampling layer in the encoder and an up-sampling layer in the decoder, as depicted in Fig. 2 . The CA block functions by compressing spatial information through CA and subsequently capturing long-range correlations using a feed-forward MLP [5] , structured as follows:EQUATIONEQUATIONwhere LN(•) denotes the layer normalization, GELU(•) denotes the GELU activation layer, and MLP(•) denotes multi-layer perceptron.After passing through 𝐿 modules within the encoder, we receive the hierarchical features {𝑋 1 , 𝑋 2 , . . . , 𝑋 𝐿 }, where𝑋 𝐿 ∈ R 2 𝐿 𝐶 × 𝐻 2 𝐿 × 𝑊2 𝐿 . We calculate the regional contextual correlation via the Regional Attention Module (RAM) according to the pooled feature 𝑋 𝐿 in the bottleneck stage. Next, the features input to each CA module of the decoder is the concatenation of the up-sampled features and the corresponding features from the encoder through skip-connection.3.2.2 Regional Attention Module. Given that shadow removal is a task of partial corruption, existing local attention mechanisms [26, 35] face considerable constraints during the shadow removal process, as the areas within a window may be entirely corrupted. While [11] mitigates this to a certain extent, it is still limited by the constraints of window-based attention, lacking the flexibility to provide each shadow region with unique, spatially relevant nonshadow information. To address this, we propose a novel Regional Attention Module (RAM), which enables each shadowed location to more effectively utilize regional attention information across spatial and channel dimensions.In the development of our Regional Attention Module, we draw inspiration from the Neighborhood Attention Transformer [15] , adapting its core concepts to better suit the specific challenges of shadow removal. Given a feature map 𝑌 ∈ R 𝐶 ×𝐻 ′ ×𝑊 ′ normalized by a LayerNorm (LN) layer and reshape it as 𝑋 ∈ R 𝑛×𝑑 , where 𝑛 = 𝐻 ′ ×𝑊 ′ , 𝑑 = 𝐶. 𝑋 is transformed into 𝑄, 𝐾, and 𝑉 , and relative positional biases 𝐵(𝑖, 𝑗) through linear projections. We define the attention weight for the 𝑖-th input within a region of size 𝑘 as the dot product between the query projection 𝑄 of the 𝑖-th input and the key projections 𝐾 of the 𝑘 elements in the surrounding region:EQUATIONwhere 𝜌 𝑗 (𝑖) denotes 𝑖's 𝑗-th element in the region. We then define values, 𝑉 𝑘 𝑖 , as a matrix whose rows are 𝑘 value projections from elements which in the region of 𝑖-th input :EQUATIONRegional Attention for the 𝑖-th token with region size 𝑘 is then defined as:EQUATIONwhere √ 𝑑 is the scaling parameter. This operation is repeated for every pixel in the feature map. Finally, the output from RAM is subjected to CA for global information interaction and feature finetuning. RAM can be represented as follows: Regional Attention for Shadow Removal MM '24, October 10-14, 2022, Melbourne, AustraliaEQUATIONEQUATION3.2.3 Regional Attention With Larger Receptive Field. Shadows are usually not isolated, they are intimately connected with their surroundings. A larger receptive field can enable a model to more comprehensively understand the relationships between shadowed and non-shadowed areas in an image, such as the shapes and edges of shadows and their relation to lighting conditions. If the receptive field is too small, the model might only see parts of the shadow or objects obscured by the shadow, potentially leading to incorrect shadow perception and removal. A larger receptive field allows the model to observe the entire shadow area and its transitional edges, thus more accurately performing shadow removal. During shadow removal, maintaining the naturalness and coherence of the image is crucial. A larger receptive field helps the model maintain consistency and natural transitions in the surrounding environment while removing shadows, and avoiding unnatural patches or color discrepancies in the processed image. Similarly, regional attention benefits from a larger receptive field, as tokens calculated within this area can access more information. Inspired by DiNAT [14] , to balance model complexity and performance, we propose a regional attention mechanism with a dilation factor. Specifically, we expand the receptive field to a greater range by increasing the stride when selecting regions, thereby maintaining the overall attention span. Using the regional attention mechanism with the dilation factor allows us to extend the receptive field of regional attention without increasing model complexity, further enhancing performance.We employ two loss terms: content loss, and perceptual loss. We provide a detailed description of these loss terms below.Content Loss. The content loss ensures consistency between the output image and the ground truth training data. In the image domain, we adopt the Charbonnier Loss [23] . The content loss can be expressed as:EQUATIONwhere the Î is the output image and 𝐼 𝑔𝑡 is the ground truth shadowfree image. The 𝜖 is set to 10 -6 to ensure numerical stability. Perceptual Loss. Perceptual loss has been widely used in various image restoration and generation tasks to preserve the highlevel features and semantic information of an image while minimizing the differences between the restored image and the ground truth. We minimize the 𝑙 1 difference between the feature of Î𝑙𝑖𝑡 and 𝐼 𝑙𝑖𝑡 in the {conv1_2, conv2_2, conv3_2, conv4_2, conv5_2} of a imagenet-pretrained VGG-19 model. Denoting the 𝑖-th feature extractor as Ψ 𝑖 (•), the perceptual loss we adapt can be expressed as follows:EQUATIONwhere 𝑤 𝑖 is the weight among different layers, the value of which is empirically set as {0.1, 0.1, 1, 1, 1}. The total loss function turns out to be :EQUATIONwhere 𝛼 1 , 𝛼 2 = {0.001, 1} are empirically set. RASM undergoes endto-end supervised training using the loss function L, achieving state-of-the-art results in shadow removal. 
experiments 4.1 implementation details: 
 The proposed model is implemented using PyTorch. We train our model using AdamW optimizer [31] with the momentum as (0.9, 0.999) and the weight decay as 0.02. The initial learning rate is set to 4 × 10 -4 , then gradually reduces to 10 -6 with the cosine annealing [28] . We set the region size of the regional attention to 11 and the dilation factor to 2 in our experiments. Our RASM adopts an encoder-decoder structure (𝐿 = 3). We set the first feature embedding dimension as 𝐶 = 32. During the training stage, we employed data augmentation techniques, including rotation, horizontal flipping, vertical flipping, MixUp [37] , and adjustments in the H and S components of HSV color space.To validate the performance of our model, we conduct experiments on two datasets. SRD [32] is a paired dataset with 3088 pairs of shadow and shadow-free images. We use the predicted masks that are provided by DHAN [2] . For the adjusted ISTD [24] dataset, we use 1330 paired shadow and shadow-free images for training and 540 for testing.Following previous works [3, 13, 18, 34] , we conduct the Root Mean Square Error (RMSE) between output image and ground-truth shadow image in the color space of CIE LAB as a quantitative metric (the lower the better). To make the comparison more comprehensive, we also follow [11] to report the Peak-Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) in RGB space (the higher the better). The FLOPs are reported on 256 × 256 images.4.2.1 Quantitative Comparisons. We first compare our proposed method with the state-of-the-art shadow removal methods on the ISTD+ [24] dataset. The competitors are DC-ShadowNet [19] , BM-Net [40] , DHAN [3] , AutoExposure [9] , G2R [3] , ShadowFormer [11] , ShadowDiffusion [12] , Li et al. [25] and Liu et al. [27] . The input image is all resized to 256 × 256 for benchmarking following most of the existing methods [11, 13, 18, 41] . The results are depicted in Tab. 1. Our approach surpasses existing methods across all metrics in comparisons of Shadow Region, All Image, and also in the PSNR and RMSE metrics for Non-Shadow Regions, achieving SOTA performance. In Non-Shadow Regions, our SSIM is essentially on par with the best-reported results.We also compare our method with the state-of-the-art shadow removal methods on the SRD [32] dataset. The competitors are consist of 11 methods, DSC [17] , DHAN [3] , AutoExposure [9] , DC-ShadowNet [19] , Unfolding [41] , BMNet [40] ShadowFormer [11] , ShadowDiffusion [12] , Li et al. [25] , Liu et al. [27] and DeS3 [20] . Since there exists no ground-truth mask to evaluate the performance in the shadow region and non-shadow region separately, we use a mask extracted from DHAN [3] following existing method [11] for comparison. The results are depicted in Tab. 2. As shown in Tab. 2, our method outperforms existing techniques across all metrics for the Shadow Region. Performance in the Non-shadow region does not quite match that of ShadowDiffusion [12] et al. [27] , possibly due to stricter adherence to the shadow mask guidance. It is anticipated that our method would achieve a more satisfying result by employing a higher-quality shadow mask or utilizing user-provided masks. Notably, despite imprecise masks, our method is still the best among the competitors under RMSE for All Image.To validate the efficiency of our method, we also conduct a comparison of FLOPs and parameter counts. As depicted in Tab. 3, our model has a small number of parameters and low FLOPs, utilizing a negligible amount of computational resources while achieving superior performance, demonstrating that our model effectively balances model complexity and model performance.Comparisons. This part exhibits several examples from SRD and ISTD+ datasets to compare the visual quality shown in Figs. 4 and 5 . Our method achieves state-of-the-art performance with fewer residual shadow components and no visual artifact. Moreover, our total parameters are significantly fewer than most of the previous arts, which demonstrates the efficacy of our practical designs.Discussion on Regional Attention and Window Attention. To validate that our proposed regional attention is more suitable for the shadow removal task than traditional window attention, we choose the baseline model and its variants for comparison. Specifically, we replace all the window attention in the baseline model with regional attention, maintain the same area size of regional attention and window attention, and represent them as {Window Att., Regional Att.}. As shown in Tab. 4, the regional attention selected outperforms window-based attention on all three metrics, proving the superiority of our design. Discussion on Receptive Field of Regional Attention. The size of the receptive field and the final effect of the shadow removal task are closely related. Here, we discuss two prominent parameters that control the receptive field size of our proposed regional attention mechanism: the size of the region 𝑟𝑠 and the dilation factor 𝑑. We tried different region sizes and dilation factors to see how they affect the results. As shown in Tab. 5, we found that as the rs increases, the performance of the model improves while the computational load also increases. When 𝑟𝑠 is greater than or equal to 15, the benefits obtained by adjusting 𝑟𝑠 approach saturation. When 𝑑 is within the appropriate range, the model benefits most. However, when 𝑑 is too small, the spatial attention receptive field is limited. Conversely, when 𝑑 is too large, the spatial attention will choose a sparse distribution of region elements, making it difficult to aggregate non-shadow information from the surrounding shadowed area, leading to performance degradation. To balance performance and computational complexity, we choose a regional attention size of 11 × 11 and a dilation rate of 2 for our model. Visualization of our regional attention. To validate whether our proposed regional attention mechanism truly enables shadow areas to interact with their adjacent non-shadow areas, we selected several points on the image and visualized their attention weight allocation. As shown in Fig. 6 , we can see that in completely illuminated areas or shadow areas, the attention weights of these points are relatively low and even, while points that notice shadows have a much larger attention weight when the attention area can encompass the surrounding non-shadow areas. Moreover, points with different shadow positions are paying attention to different non-shadow areas, corresponding to the fact that each shadow area information interacts with the adjacent non-shadow area information, which is consistent with our proposed region-based attention mechanism. 
concluding remarks: 
 In this work, we rethought the most significant information source for shadow removal, namely, the non-shadow areas adjacent to the shadow region, which plays a crucial role in this task. Based on this, we proposed a novel regional attention mechanism and introduced a lightweight regional attention-based shadow removal model, RASM. The regional attention mechanism introduced by us enables each shadow region to focus on specific information from surrounding non-shadow areas, thereby effectively utilizing this information for shadow removal. We demonstrated that RASM strikes a good balance between model complexity and model performance.Our model uses fewer parameters, lower FLOPs computation, and achieves superior performance on SRD and ISRD datasets.  
 
 Appendix:  


Decision: This paper introduces the Regional Attention Shadow Removal Model (RASM), a lightweight and accurate framework for shadow removal. RASM uses a novel regional attention mechanism to focus on critical areas around shadows, leveraging non-shadow areas to aid in restoring shadow regions. This approach ensures better contextual interactions between shadow and non-shadow areas. The experiments show that RASM is an accurate and efficient model. However, few concerns of the reviewers' are very crucial. First, the novelty of the method is limited, as pointed out by the reviewer. The presented method utilizes off-the-shelf components to remove shadow from images, which limits the technical advancement of the paper. Second, experiments to assess the model's performance in complex scenes are not presented. Third, the limitations of the presented method should be discussed along with the future research directions. The last is not a major concern but it is particularly mentioned by a reviewer.

Reviews: review: paper_summary: paper_summary: "Regional Attention for Shadow Removal" introduces a novel Regional Attention Shadow Removal Model (RASM). 

The key innovation is the regional attention mechanism, which integrates information from non-shadow areas adjacent to shadows, balancing accuracy and computational efficiency.
strength_weakness: strength_weakness: 1.  The regional attention mechanism is a novel, theoretically sound method that enhances shadow removal by focusing on locally relevant information.

2. The authors provide a clear rationale for the regional attention mechanism, supported by detailed problem analysis and ablation studies.
limitations: limitations: 1.  The added complexity of the new attention mechanism is not thoroughly explored in terms of trade-offs between performance gains and complexity.

2. In Fig.5 in the supplementary, does it mean the method relies on mask accuracy?

3. There are missing citations. The following papers discuss the intrinsic property of shadow removal. 
Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning
paper_summary: paper_summary: The paper presents a lightweight and accurate framework for shadow removal. The proposed Regional Attention Shadow Removal Model (RASM) introduces a novel regional attention mechanism that leverages non-shadow areas to assist in restoring shadow regions. This approach ensures rational interactions and better contextual correlations between shadow and non-shadow areas. Extensive experiments demonstrate the superior accuracy and efficiency of RASM compared to state-of-the-art models.
strength_weakness: strength_weakness: The proposed method demonstrated superior balance between efficiency and performance.
The paper is well-structured and easy to read.
limitations: limitations: The concept of region attention in lines 438-450 needs clarification. Specifically, how is the key region determined? In conventional self-attention, the key region is the entire image, while in Swin-attention, it is a small patch. How is it defined in region attention? Furthermore, how does this approach reduce computational complexity compared to conventional self-attention? The introduction emphasizes the significance of non-shadow areas for effective shadow removal. How does the proposed region attention mechanism incorporate this principle into its design?

In line 439, 'k' appears to represent the size of the region, whereas in line 449, 'k' seems to denote the key. Please clarify this discrepancy.

From Table 5, the GFLOPs do not increase significantly with region size, which contrasts with the distinct differences in GFLOPs among the compared methods in Table 3. This suggests that the attention region size may not be the primary factor affecting computational complexity. The proposed method's computational efficiency might not be due to the attention design as suggested in lines 170-172. Analyzing the computational load of each network component could provide more insight.
paper_summary: paper_summary: This paper proposes a lightweight and accurate framework for shadow removal in images. The authors analyze the characteristics of the shadow removal task and introduce a regional attention mechanism to effectively capture the key information required for shadow reconstruction. They customize a Regional Attention Shadow Removal Model (RASM) that leverages non-shadow areas to assist in restoring shadow regions. Experimental results demonstrate that the proposed method outperforms other state-of-the-art models in terms of accuracy and efficiency.
strength_weakness: strength_weakness: - The proposed method outperforms existing shadow removal methods across various metrics on datasets like ISTD+ and SRD, achieving state-of-the-art performance.
- The model has a small number of parameters and low computational requirements, striking a balance between complexity and performance.
- The regional attention mechanism introduced in the model allows for effective utilization of information from non-shadowed areas for accurate shadow removal.
limitations: limitations: - Some typos should be clearly corrected.
- I suggest a visual example of the attention mechanism proposed in the article for visualization purposes.
- Is there any limitation to the methods in the article? Do the methods work for all scenarios?
paper_summary: paper_summary: Based on the large size and high computational complexity of existing models, this paper proposes a lightweight yet accurate shadow removal framework for image shadow removal.
strength_weakness: strength_weakness: To achieve accurate shadow removal, this paper introduces a regional attention mechanism, which enables model to focus on the critical areas surrounding the shadows. The experimental results seem to demonstrate the effectiveness of the proposed method. 
The motivation of this paper is good.
limitations: limitations: The motivation of this paper is good, but the technological innovation of the method is limited. The network designed by the paper are relatively simple. The network structures of the proposed channel attention module and regional attention module are simple superposition of some operations that lack innovation. 

The method introduces a lightweight model. However, from the results in Table 2, we find that the proposed model does not obtain an optimal value when evaluating the lightweight. Does this indicate that the proposed method does not achieve the so-called lightweight?
Moreover, why does does this approach achieve lightweight, and where in the module does it promote this? I am puzzled by this part. The article doesn't describe these details. 

Paper argues that the existing window attention mechanism generates the attention within a window. But from the description, I believe that the proposed regional attention mechanism is also implemented using windows, just with a larger receptive field. I do not find the advantage of this module in terms of network structure.

The ultimate goal of shadow removal task is can process image with complex scenes. Paper only gives results for shadow image with simple scenes. That's not what we want to see. Can this method handle shadow image with complex scenes? I suggest more experiments and discussion about this.