# A Survey of Multi-Focus Image Fusion Methods
## 2 Fusion Methods and Analysis
### Multi-Focus Fusion Method Based on Deep Learning
#### 2.3.1 Convolutional Neural Network Model  
The convolutional neural network is one of the most popular deep learning models. It can realize parallel computing and has high speed and high efficiency characteristics [52; 53]. CNN is widely used in medical image analysis, remote sensing image analysis, noise signal analysis, and other fields. [PERSON] et al. [54] used a customized 14-layer convolutional neural network resnet-14 architecture to automatically detect and evaluate ACL injuries of athletes. [PERSON] et al. [55] proposed a new method more suitable for farmland vacancy segmentation, using the improved RESNET network as the backbone of signal transmission. [PERSON] et al. [56] proposed a method for the classification of noisy non-stationary time-seriessignals based on [PERSON]'s class of their time-frequency representations (TFRs) and deep learning algorithms. The proposed approach combining deep CNN architectures with [PERSON]'s class TFRs yields high values of performance metrics and significantly improves the classification performance compared to the base model.  
In the field of image fusion, CNN can learn the feature representation mechanism of different abstraction levels for source images, and it is trainable. CNN extracts the features of input images by learning filters to obtain different feature maps of each level. Each unit or coefficient in the feature maps is called a neuron. Generally, three calculation methods, filtering convolution activation function and pooling, are used to connect feature maps between adjacent levels [57]. The typical structure diagram of CNN is shown in Figure 8.  
In 2017, [PERSON] et al. [25] successfully applied CNN to the field of image fusion for the first time. By extending the classification idea of artificial neural networks, a convolutional neural network is used instead of an artificial neural network to classify the pixels of source images. According to the mapping value, the score graph of the source image can be obtained. Then the decision graph can be built by the consistency verification of the score graph. [PERSON] et al. [26] proposed a new CNN-based integrated learning approach to pursue data diversity to reduce the over-fitting problem. This fusion method based on CNN integration is better than the single CNN fusion method. [PERSON] et al. [59] proposed a full end-to-end convolution layer network model. This model chose the feature level fusion, canceled all pooling layers, adopt the strategy of full connection layer directly, gave up on the mapping of the source image pixel resolution, and judged the fusion result loss, thus achieving the purpose of the end-to-end output of fusion image. This method is more concise and effective and avoids the complicated follow-up processing problems of the CNN model.  
The advantages of the multi-focus image fusion method based on CNN lie in the ability of layered learning features, more diversity of feature expression, strong discrimination ability, and better generalization performance. The disadvantage is that the training takes a long time, and there is no special training set, which usually requires particular training and image preprocessing.