[Advanced Chapter] Image Fusion in MATLAB: Using Deep Learning for Image Fusion

# 2.1 Fundamental Principles of Deep Learning

Deep learning is a type of machine learning technique that utilizes multi-layer neural networks to learn complex patterns in data. A neural network is a computational model inspired by the human brain, capable of learning features and patterns from data.

### 2.1.1 Structure and Mechanism of Neural Networks

A neural network consists of nodes called neurons, which are interconnected through weighted connections. Each neuron receives input data, performs a weighted sum, and then applies an activation function to produce output. By arranging multiple neurons in layers, a neural network can learn complex data representations.

### 2.1.2 Training and Evaluating Deep Learning Models

Deep learning models are trained using a training dataset. The training process involves adjusting the weights in the neural network to minimize the loss function. The loss function measures the difference between the model's output and the true labels. After training, the model is evaluated using a validation dataset to assess its generalization performance.

# 2. Application of Deep Learning in Image Fusion

### 2.1 Fundamental Principles of Deep Learning

#### 2.1.1 Structure and Mechanism of Neural Networks

Deep learning is a machine learning technique that employs multi-layer neural networks to learn intricate data patterns. Neural networks are composed of interconnected neurons that process input data and produce outputs.

The structure of a neural network typically consists of an input layer, hidden layers, and an output layer. The input layer receives raw data, hidden layers process and extract features from the data, and the output layer produces the final results. There can be multiple hidden layers, each containing a certain number of neurons.

The processing steps of a neuron include:

1. **Weighted Sum:** Multiply the input data by the neuron's weights and then sum them up.
2. **Activation Function:***monly used activation functions include ReLU, Sigmoid, and Tanh.

#### 2.1.2 Training and Evaluating Deep Learning Models

Training deep learning models requires a labeled dataset. The training process involves the following steps:

1. **Forward Propagation:** Pass input data through the neural network to obtain output results.
2. **Loss Calculation:** Compare the output results with the true labels and calculate the value of the loss function.
3. **Backpropagation:** Calculate gradients based on the loss function and update the weights of neurons using the gradient descent algorithm.
4. **Iteration:** Repeat the steps of forward propagation, loss calculation, and backpropagation until the model converges or reaches a predetermined number of training iterations.

The performance of deep learning models can be evaluated using the following metrics:

***Accuracy:** The ratio of the number of correctly predicted samples to the total number of samples.
***Recall:** The ratio of the number of samples predicted as positive by the model that are actually positive to the number of actual positive samples.
***F1 Score:** The weighted harmonic mean of precision and recall.

### 2.***

***monly used deep learning models include:

#### 2.2.1 Generative Adversarial Networks (GAN)

GAN is a generative model that contains two neural networks: a generator and a discriminator. The generator produces images, while the discriminator distinguishes between generated images and real images. Through adversarial training, the generator can produce lifelike images.

#### 2.2.2 Autoencoders (AE)

AE is an unsupervised learning model that learns a compressed representation of input data. An AE consists of an encoder and a decoder. The encoder compresses the input image into a low-dimensional representation, and the