# Lightweight Infrared and Visible Image Fusion Based on Nested Connections and Res2 Net
## 1 Introduction
Then, for image fusion, a hand-crafted fusion approach is used to combine deep features extracted from various source images. [PERSON] et al. [13] proposed a novel autoencoder-based image fusion network called DenseFuse, which adopts the network structure of DenseNet [14] to fully extract image features, enriching the extracted features with more abundant information. Then, a designed fusion method is applied for feature-level fusion, and finally, four convolutional layers are used in feature reconstruction to create fused pictures. During the training phase, a large-scale dataset is used to train the autoencoder designed for the fusion task, facilitating the improvement of feature extraction adaptability in various scenarios. However, this network structure is relatively simple and cannot extract multi-scale deep features. To address this issue, many improved autoencoder image fusion algorithms based on the DenseFuse framework have emerged. In 2019, [PERSON] et al. [15] proposed the MSDNet algorithm, which extracts multi-scale features and fuses data across all scales by adding convolutional kernels of varying sizes after the encoder. However, while introducing multi-scale features enriches the information of deep features, it also makes the overall fusion network more complex, increasing the computational complexity of the model. Subsequently, [PERSON] et al. [16] further improved the network structure based on DenseFuse and proposed the NestFuse image fusion method, which utilizes nest connections to construct the decoder network structure and achieves multi-scale feature extraction. Nevertheless, this model still requires the manual design of fusion methods and cannot perform fusion specifically for the unique information of infrared and visible images. Although autoencoder-based image fusion methods significantly improve fusion performance compared to traditional methods, they lack specific datasets for multimodal images, resulting in limitations in their expressive power when dealing with complex multimodal images. With the emergence of more multimodal datasets, a plethora of end-to-end fusion methods have emerged, incorporating end-to-end training, a fusion strategy, and deep feature extraction as the three main fusion process components. In 2017, [PERSON] et al. [17] introduced the DeepFuse model, which was the first to apply an end-to-end network to image fusion. However, its very simplistic network topology results in information loss, as it just uses the final layer's output. [PERSON] et al. [18] utilized GAN [19] to fuse infrared and visible images. They achieved this by creating an adversarial relationship between the two types of data. However, FusionGAN utilizes content loss and discriminator loss as loss functions, resulting in fused images with fewer texture details. Subsequently, [PERSON] et al. [20] improved upon FusionGAN with FusionGANv2, introducing novel loss functions such as detail loss and target edge-enhancement loss to preserve the detailed information of target edges. To solve many kinds of image fusion challenges, [PERSON] et al. [21] manually assembled a multi-focus image dataset and used a CNN that had already been trained. However, the network's results were constrained when used for other image fusion tasks because it was trained on a dataset of images with several foci. Following this, [PERSON] et al. [22] proposed a unified image fusion method that maintains the adaptive similarity between the fusion results and source images by leveraging adaptiveness. However, the loss function employed in U2 Fusion, designed solely around gradient-based adaptiveness, fails to fully capture the significance of source images across various fusion subtasks. For instance, in the fusion task of infrared and visible-light images, compared to infrared images, visible-light images exhibit more texture details and dominant gradient clues, resulting in fusion results biased toward visible-light images. [PERSON] et al.