# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 2 Infrared and Visible Image Fusion Methods
### Methods Based on Multi-Scale Transformation  
Multi-scale transformation has been extensively applied in the field of infrared and visible picture fusion throughout the last few decades. Real-world objects are made up of parts at many scales, and multi-scale transformation can break down the original image into parts at several scales, where each part denotes a sub-image at each scale [29]. The three steps that make up a multi-scale transformation are typically as follows: (1) The source picture is decomposed to produce the high-frequency and low-frequency sub-band layers; (2) Fusion rules are chosen by the characteristics of the various sub-band layers to produce a multi-scale representation; (3) To obtain the fused image, an inverse multi-scale transformation is performed. The choice of transformation and fusion rules, which primarily comprise pyramid transformation, wavelet transformation, non-secondary sampling contour transformation, etc., as illustrated in Figure 5, is the most crucial aspect of multi-scale transformation.  
When there is noise or artifacts in the input image during the image fusion process, the quality of the fusion image will be somewhat impacted. For instance, noise and artifacts will impact the image's sharpness and fine details, causing the fused image to lose those details; it may also alter the image's color distribution, causing the fused image to have color distortion. Furthermore, it may result in discontinuous edges in the image, giving the fused image an unsmooth appearance, as well as a reduction in contrast, which would make it difficult to distinguish between light and dark areas in the image. Therefore, among the various picture fusion techniques, the more effective the noise reduction or artifact removal,  
Figure 4: Infrared and visible image fusion methods.  
the higher the quality of the resulting images. Table 1 lists the benefits and drawbacks of several multi-scale transformation techniques so that we may more easily and intuitively understand how well each technique performs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Multi-Scale Transformation Methods** & **Typical Methods** & **Advantages** & **Disadvantages** \\ \hline \multirow{8}{*}{Based on pyramid transformation methods} & \multirow{2}{*}{Low-pass pyramid transformation methods} & Reduced image size and preserved detail & Some information is missing and edges are blurred \\  & & The realization is simple and & The fusion result has \\  & Laplacian pyramid & the operation speed is fast & low contrast \\  & Gradient pyramid & Good anti-noise performance & Poor fusion effect \\  & & The low frequency & Easy to lose high-frequency \\  & & information of the image is & details, high \\  & & retained, which makes the & computational complexity \\  & & fusion result more natural & The anti-noise performance \\  & Contrast pyramid & The image contrast is & is poor \\  & & improved and more detailed & is poor \\  & & The algorithm is simple and & \\  & Filter-subtract extract pyramid & the calculation efficiency is & Image resolution degradation \\  & & high & \\  & Morphological pyramid & Image contrast and brightness & Easy to introduce artifacts \\  & & are enhanced & and distortion \\ \hline \hline \end{tabular}
\end{table}
Table 1: The advantages and disadvantages of multi-scale transformation methods.  
Figure 5: Multi-scale transformation methods.