# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 2 Infrared and Visible Image Fusion Methods
### Sub-Space-Based Methods  
The sub-space-based method is a common method in infrared and visible image fusion. The image that is difficult to distinguish and recognize in the original space is extended to the sub-space by learning the proper sub-space. Samples can be mapped into sub-spaces to get better classification results because sub-spaces offer certain benefits that the original space does not. Visual features in zero-sample image classification are usually extracted by neural networks, and semantic features are obtained by manually defined attributes or keywords extracted from text, which leads to different distributions of visual and semantic features. Poor zero sample identification performance is easily caused by a weak knowledge transfer capacity when the mapping between visual space and semantic space is acquired by direct learning. We can realize the alignment of semantic space and visual space by learning the sub-space, which will improve our capacity for knowledge transfer. High-dimensional input images can be projected into low-dimensional spaces or sub-spaces using sub-space-based algorithms. Low-dimensional sub-spaces can help capture the inherent structure of the original image because redundant information is present in the majority of natural images. Processing low-dimensional sub-space data will also take less time and memory than processing high-dimensional input images [38]. As a result, the degree of generalization can be increased by using low-dimensional sub-space representations. The most common sub-space-based techniques are principal component analysis (PCA), independent component analysis (ICA), and non-negative matrix decomposition (NMF). 1. Principal component analysis (PCA) The goal of principal component analysis (PCA) is to reduce the dimension while keeping the information of the original data by transforming several variables that may be associated with unrelated variables into the principal components. The pixel values of all source images at each pixel position must be used in the fusion process. Each pixel value must then be given a weighting factor, and the average of the weighted pixel values for fused images at the same pixel position must be calculated. The best weight factor can then be identified using the procedure. PCA can also eliminate unnecessary data and draw attention to similarities and differences. Principal component analysis has been extensively utilized in many different domains in recent years; however, it is particularly susceptible to gross mistakes, which are common in many applications. To solve this issue, [PERSON] et al. [42] presented resilient principal component analysis (RPCA) in 2011. This method divides the matrix into low-rank and sparse components, which solves the aforementioned issue. RPCA can be used in picture fusion to keep important data and eliminate sparse noise. The RPCA approach can also be used to obtain the sparse components of infrared and visible pictures, and the low and high-frequency sub-band coefficients after fusion can be derived using the sparse components. 2. Independent component analysis (ICA) Independent component analysis is a technique for automatically identifying potential factors in a given dataset and is an extension of principal component analysis that aims to transform potentially correlated variables into uncorrelated independent variables. The algorithm has also been successfully used to combine infrared and visible images. ICA-based image fusion techniques typically train a set of bases as the fusion images using a number of natural images with similar content [43]. They can fuse with bases with comparable content once they have a set of bases that have already been educated. A region-based ICA fusion technique was put forth by [PERSON] et al. [44] that would segment the image into various areas. The ICA coefficients of each region are then extracted from the preprocessed pictures.