# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 2 Infrared and Visible Image Fusion Methods
### Methods Based on Multi-Scale Transformation
#### 2.1.1 Methods Based on Pyramid Transform  
* Gauss Pyramid A Gaussian pyramid is essentially a multi-scale representation of signals [30], which involves continuously applying Gaussian fuzzy to the same signal or picture and using downward sampling to create numerous sets of signals or images at various scales. The highest picture resolution is found at the base of the Gaussian pyramid, and as image resolution increases, image resolution decreases. The Gaussian pyramid's down-sampling technique is as follows: (1) A Gaussian smoothing technique is applied to a given image, i.e., a convolution check image is used for convolution; (2) Take a sample of the image, eliminate all even-numbered rows and columns, and create a picture; (3) By performing operations (1) and (2) on the image again, the Gaussian pyramid can be obtained [31]. * The Pyramid of Laplace Laplacian pyramid [32] fusion is sometimes referred to as multi-band fusion, and its fusion image can be thought of as being formed of information of many frequencies, including a wide variety of features, and the spectrum has a large span. Laplacian pyramid can choose fusion windows of various sizes for various frequency components. The use of a larger fusion window at low frequencies will prevent truncation. Smaller fusion windows will be employed at high frequencies to prevent ghosting. A seamless, ghost-free fusion image is the end product. This is the precise procedure: (1) Create a Laplacian pyramid for the two different types of images that need to be combined; (2) Create a Gaussian pyramid for the designated fusion region; (3) Apply the fusion formula to each layer of the pyramid; (4) Use the output image to reconstruct the fused pyramid. * Gradient pyramid The multi-scale decomposition approach based on the Gaussian pyramid is also used in the image fusion algorithm based on gradient pyramid (GP) decomposition [33]. The gradient operator is used for each layer of the Gaussian pyramid image to create the gradient pyramid representation of the image. Each layer's decomposed image comprises information in the horizontal, vertical, diagonal, and other directions of detail, allowing for greater extraction of the image's edge information and much increased stability and noise resistance. * Low-pass pyramid The low-pass pyramid [7] functions by first applying an appropriate smoothing filter to the picture and then twice sampling the resulting smoothed image, that is, twice sampling along each coordinate direction. Repeat the aforementioned procedures for \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Multi-Scale Transformation Methods** & **Typical Methods** & **Advantages** & **Disadvantages** \\ \hline Based on wavelet transform & Discrete wavelet transform & The anti-noise performance has been improved & The image is prone to aliasing and translation sensitivity \\ transform methods & Displacement invariant wavelet transform & Displacement invariance, good retention of high- and the calculation is complicated \\ \hline Multi-scale geometric analysis & Non-subsampled contour wave transform & Good anti-noise performance and local image processing and scaling \\  & Non-subsampled shear wave transform & Poor detail characterization & Translation invariance is good \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._the generated image numerous times, and after each cycle, a smaller image will be obtained, whose smoothness will grow and whose spatial sampling density, or image resolution, will drop. The smaller images produced by each loop are superimposed on top of the preceding result, creating a pyramid shape if the original image is placed at the bottom. The low-pass pyramid is seen here. * Contrast pyramid The ratio of the contrast pyramid to the low-pass pyramid is close. Contrast itself is defined as the ratio of the difference between the luminance of a location in the image plane and the local background luminance to the local background luminance.