## 3 The Methods
### Lightweight and Effective Uformer  
In recent years, Transformer models have achieved remarkable progress in computer vision. Vision Transformer (ViT) [38] has demonstrated superior image classification performance over CNNs by leveraging pre-training on large-scale datasets. Recently, the integration of Transformers with U-Net architecture has further expanded their application to image reconstruction and enhancement tasks. By employing a U-shaped structure combined with local window-based attention mechanisms, these methods have achieved effective low-level vision restoration, verifying the feasibility of Transformers for image enhancement tasks [39]. However, these methods still suffer from high computational  
Figure 1: The structure of NRGS-Net. complexity and limited inference efficiency [40]. To address these issues, we propose a lightweight and effective Uformer (LEUformer), as illustrated in Figure 2. Specifically, only a single Transformer block is used in each encoder and decoder layer to effectively reduce computational overhead. Additionally, to further reduce the model's parameter count and computational burden, we introduce L-Downsampling and L-Upsampling modules to replace the conventional downsampling and upsampling operations. In the L-Downsampling module, a DWConv-PWConv structure (depthwise separable convolution followed by pointwise convolution) [41] is used instead of the conventional 4 \(\times\) 4 convolution, enabling efficient feature downsampling while preserving spatial information. For upsampling, we adopt a residual PixelShuffle-based L-Upsampling module instead of deconvolution. This approach suppresses artifacts commonly introduced during upsampling and improves both inference efficiency and restoration quality in nighttime glare suppression tasks. Furthermore, we improve the Feed-Forward Network (FFN) of the original Win-Transformer and propose an LCAWin-Transformer, which incorporates channel attention mechanisms on top of the original structure to further enhance feature representation capabilities. The overall network architecture is depicted in Figure 2.  
In the main branch of L-Upsampling, we first apply a 3 \(\times\) 3 convolution before upsampling to further extract local spatial features (such as textures and edges), enhancing the effective feature representation after upsampling. We then utilize PixelShuffle to rearrange the channel dimensions into spatial dimensions, achieving spatial upsampling while preserving the contextual information extracted by the convolution without introducing additional parameters. Specifically, given an input tensor of shape \(\left(\text{C},r^{2},\text{H},\text{W}\right)\), where r is the upsampling factor, PixelShuffle rearranges the data from the channel dimension into the spatial dimensions, producing an output tensor of shape \(\left(\text{C},\text{H}\cdot\text{r},\text{W}\cdot\text{r}\right)\). The upsampling operation can be expressed as follows:  
\[U_{m}=\text{PixelShuffle}(Conv_{3\times 3}(X)) \tag{5}\]  
In the residual path, we apply a 1 \(\times\) 1 convolution for channel adjustment and linear projection to improve channel expressiveness and flexibility before upsampling. We then perform bilinear interpolation for spatial upsampling, which efficiently and smoothly enlarges the image size while avoiding feature damage and artifacts. This can be expressed as follows:  
\[U_{S}=\text{Bilinear}(Conv_{1\times 1}(X)) \tag{6}\]  
Figure 2: The structure of LEUformer. Finally, ReLU activation is applied to enhance nonlinearity and obtain the upsampled output feature map. This can be expressed as follows:  
\[U=\text{ReLU}(U_{m}+U_{S}) \tag{7}\]  
In the LCAWin-Transformer, we introduce an attention mechanism within the LCA-FFN to further enhance the effectiveness of feature representation. As illustrated in Figure 3, we incorporate a simple yet efficient SE attention module within the feed-forward network (FFN), which effectively improves channel feature extraction, enabling the network to better focus on glare-related features.