# Lightweight Infrared and Visible Image Fusion Based on Nested Connections and Res2 Net
## 3 Approach  
### An Overview of the Proposed Method  
We propose a lightweight multi-scale infrared and visible image fusion method. The method first divides the original visible-light and infrared images into base and detail layers using mutually guided image filtering (muGIF) [30], which allows for extracting more hierarchical representations in high-frequency and low-frequency domains. The base layer encompasses information such as image content and spatial structure, whereas texture and local shape information are contained within the detail layer. Subsequently, sub-images at the same hierarchical level are input into the image fusion network for fusion. Finally, the fused images from both high-frequency and low-frequency components are merged to derive the final fused image. The flowchart of the proposed algorithm is illustrated in Figure 4.  
The decomposition process of the source image primarily consists of two steps. Firstly, the image's base layer is obtained through the muGIF method, which can be calculated using (4).  
\[I_{\text{base}}\,=\text{muGIF}(I_{\text{i}},\alpha,T) \tag{4}\]  
Here, \(I_{\text{base}}\,\) represents the base layer image, muGIF denotes the mutually guided image filtering operation, \(I_{\text{i}}\) stands for the source image, \(\alpha\) is the parameter controlling the extent of texture removal, and \(T\) represents the number of iterations. We set \(\alpha\) to 0.003 and \(T\) to 3.  
After extracting the base layer, the detail layer image is obtained through the operation in (5):  
\[I_{\text{detail}}\,=\,I_{\text{i}}-I_{\text{base}} \tag{5}\]  
When fusing sub-images at the same hierarchical level, we propose a lightweight multi-scale infrared and visible fusion network. Taking the fusion process of the base layer as an example, its architecture is illustrated in Figure 5. We draw inspiration from the RFN-Net's network structure, where the fusion network consists of encoder, fusion, and decoder modules. We introduce depthwise separable convolution into the encoder and decoder networks, replacing conventional convolutions in the original network to address the issue of the relatively large parameter size. The encoder module of the encoder network comprises two improved bneck layers and a max-pooling layer. Through this combination, the encoder can extract multi-scale depth features with a smaller computational cost. The multi-scale fusion network R2 FN is employed to integrate multimodal depth features extracted at each scale. The fused features are then input into the decoder with a nested  
Figure 4: Two-layer fusion framework of the proposed method.  
connection structure. The advantage of this structure is its ability to avoid information loss from previous layers during convolution operations, thereby fully utilizing multi-scale features for image reconstruction.  
We adopted the Bottleneck (Bneck) structure from MobileNetv3. DSC is capable of reducing the number of model parameters and computational complexity, but it might also adversely affect the convolution's capacity to extract features. Therefore, the attention module CBAM [31] is introduced to enhance the model's attention concentration ability and improve the information-processing mechanism, effectively improving the quality of image fusion and the overall performance of the model. CBAM, as a lightweight and versatile attention mechanism, can be easily added to the convolutional layers of any network at a minimal cost. CBAM applies attention mechanisms simultaneously in both the channel and spatial dimensions, enhancing the model's accuracy. Additionally, the parameter size of the improved network has been further decreased. The enhanced Bneck structure is illustrated in Figure 6.