# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 2 Infrared and Visible Image Fusion Methods
### Hybrid Methods  
The aforementioned visible and infrared image fusion techniques each have advantages and drawbacks of their own. The quality of picture fusion will be further enhanced if the benefits of various technologies can be combined. The hybrid approach combines the benefits of various approaches to enhance image fusion performance. Hybrid multi-scale transform and neural network, hybrid multi-scale transform and sparse representation, and hybrid multi-scale transform, sparse representation, and neural network are examples of common hybrid approaches. In order to improve the target region and preserve details, hybrid multi-scale transform and saliency approaches integrate saliency region detection into a multi-scale transform image fusion framework. A multi-scale decomposition picture fusion technique based on local edge preservation filtering and salience detection was proposed by [PERSON] et al. [65] The infrared and visible pictures are separated using a local edge-holding filter first. The significant target region in the infrared image is then found using the improved significance detection approach, and the fundamental layer weight of the fusion strategy is established. The fused image is then created by reconstructing each layer. Aiming to address the drawbacks of conventional multi-scale picture fusion techniques, [PERSON] et al. [47] suggested a multi-scale image fusion method based on visual significance maps and weighted least squares optimization. The input image is first divided into basic and detail layers using a rolling guide filter and a Gaussian filter, which introduces multi-scale decomposition. Second, the majority of the fundamental layers produced by multi-scale decomposition will still include low-frequency information in them. An improved method based on a visual saliency map is suggested to fuse the basic layer in order to tackle this issue. To fuse the detail layer, a novel weighted least squares optimization approach is then suggested. The merged image is then obtained. Sparse representation and neural networks are frequently incorporated into the multi-scale transform infrared and visible image fusion frameworks because they are two of the most frequently used signal and image representation theories in the field of image fusion. A broad image fusion framework was presented by [PERSON] et al. that incorporates multi-scale transform and sparse representation while also addressing the drawbacks of the fusion approach of multi-scale transform and sparse representation. To get the low-pass and high-pass coefficients, a multi-scale transformation is first carried out for each source image that has been previously registered. The high-pass band then uses the absolute value of the coefficient as the activity-level measurement and merges with the low-pass band using a sparse representation-based fusion method. An inverse multi-scale transformation is then applied to the merging coefficient to produce the final fusion image. A visible and infrared image fusion technique based on a non-subsampled shear-wave transformation-spatial frequency-pulse-linked neural network was proposed by [PERSON] et al. [66] NSST not only has advantages in information acquisition and computation savings but also overcomes the disadvantage that shear wave transformation does not have translation invariance. The integration of low-frequency and high-frequency sub-band coefficients into an enhanced IPCNN model is suggested in this research in order to increase function efficiency and protect computing resources. In order to address the issues of poor contrast and ambiguous backdrop features in the existing infrared and visible fusion approaches, [PERSON] et al. [67] suggested a multi-scale fusion method that is based on the combination of a non-sampled contour wave transform, sparse representation, and a pulse-coupled neural network. The source image is split into a low-frequency and a high-frequency sub-band before being subjected to various scales and directions of NSCT.