## 1 Introduction  
With the development of science and technology, more and more data are collected using sensors, and more and more information is obtained from them. From black and white images and color images taken by optical cameras at the beginning to hyperspectral, infrared images, and radar images taken by various sensors today. With the increase in image types, the use of images is also increasing. However, these images are generated by a single sensor, which has some shortcomings. A single sensor has limitations, so it is difficult to collect all the information about the scene, which leads to no image containing all the information about the scene. Image fusion technology is to fuse the image information collected by different sensors into a new image by some means. The new image contains both the information of the original image and reduces redundant information between many images, which improves the use rate of the image. Traditional image fusion methods can extract image features and fuse them well, but these algorithms have defects, resulting in noise and poor image quality in the fused image. The appearance of deep learning brings a new research direction for image fusion algorithms. A large number of image fusion algorithms based on deep learning have emerged. The convolutional neural network has a good ability to extract image features. However, the traditional convolutional neural network entering the bottleneck period, researchers have gradually abandoned the convolutional neural network and started to study Transformer. However, some scholars are still studying convolutional neural networks, and proposed a pure convolutional neural network ConeNeXt [1] in 2022. Fewer activation functions and larger convolution kernels are used in ConeNeXt. AlthoughConvNeXt does not propose new structures and methods, ConvNeXt reduces the use of activation functions, making ConvNeXt have faster reasoning speed and higher accuracy than Swin Transformer [1]. There is no doubt that convolution neural networks have a good ability to extract image features. Researchers use pre-trained AlexNet [2], VGGNet [3], GoogleNet [4; 5], ResNet [6], DenseNet [7], CNN [8], etc. to extract image depth features and restore them to images after fusion. Besides being a tool for extracting image features, neural networks can also be used in end-to-end image fusion. The representative network models are FusionGAN [9], IFCNN [10], PPTFusion [11]. In the self-coding network framework, the network is composed of an encoder and a decoder. The encoder is used to extract image features, and the decoder is used to restore features to images. The encoder and decoder are independent, so the network structure of the self-coding network is very flexible. Because the self-coding network fusion framework is flexible and extensible, a large number of fusion algorithms based on the self-coding networks have been produced. In 2018, researchers proposed the first image fusion algorithm based on self coding network and named it DenseFuse [12; 13]. NestFuse [14; 15] was proposed in 2020, etc. This paper uses the idea from the Inception module in GoogleNet for reference to build an encoder to extract features from the input images. The basic structure of the Inception module is shown in the Figure 1. There are three important parts to the Inception module. Firstly, \(1*1\) convolution kernel for lifting dimensions to reduce the calculation of subsequent feature maps. Secondly, the convolution kernel size and output feature map size of each branch is different, which ensures the multi-scale of the image. Finally, the feature maps of each branch are spliced to obtain all feature maps. This article mainly has the following work and contributions:  
* The Inception module is added to the network model to increase the feature extraction ability of the network. * Dense blocks are added to the branches of the network, and dense modules are used for feature extraction and image generation.