## 3 Image Fusion  
Fusion is a valuable process that combines or integrates information from different sensors or sources. Image fusion is the technique of integrating several input images of the same scene into a single fused image, intending to synthesise better quality and perception of the image features. Note that an image is viewed as a light intensity function \(f(x,y)\), where \(x\) and \(y\) are the spatial coordinates of each pixel and \(f\) refers to the proportional value of the brightness at each coordinate [32]. Image fusion can combine the brightness, colour, distance, temperature and other scenery features of images. It should be noted that image fusion does not simply mean \"matching\" or \"overlaying\" of multiple images; it is a process followed to form a renewed and more meaningful image outcome [33]. Image fusion can reduce data dimensionality while keeping important information content, hence supporting a lower storage cost [34]. In principle, image fusion is seen as performing three main steps: _registration_, _information fusion_ and _representation_ (refer to Figure 2) [35]. NDT-based image fusion is a challenging topic due to the varied defect sensitivity and failure mechanisms of the inspection methods. However, these methods incorporate complementary features to obtain reliable and accurate defect characterisation and take the best action against an existing situation [36]. In such a fusion application, registration is the first significant step in having a proper reference for a fused image. The information from NDT sources should be aligned correctly to obtain the sense results. As well as the common feature point-based registration, such as for corners or edges, the authors of [37] presented a type of marker-based referencing which uses the highly reflective or emissive marks on the surface of the inspected material when the feature points are not detectable enough. As a vital matter, especially in the medical image fusion field, registration has been widely investigated with methods ranging from transformation or similarity-based methods [38,39] to novel AI-based strategies [40,41] in the literature. Similar to other fields, NDT-based fusion can be classified as level-based by the forms of information flow: signal-level, pixel-level, feature-level and decision-level fusion [42]. In signal-level fusion, a fused signal has been formed to obtain better quality by combining raw sensor signals, which can be modelled as a random variable. As an example, the authors of [43] applied superposition-based fusion at the signal level to fuse simulated  
Figure 2: The main steps of a generic fusion structure. defect and experimental defect-free noise. Figure 3 shows a superposition outcome that is comparable to the presence of the defect in the sample. The authors have stated that the outcomes have accurate amplitudes for defects as well as back walls and shadowing effects due to the model's destructive signal interfering with the back wall signal. This level of fusion has the highest registration needs in the time and space domain because of considering an optimal signal distribution problem [44]. Pixel-level fusion integrates the physical parameters of the source pixels given at specific spatial locations and their neighbourhoods and creates a pixel for the fused image. The authors of [45] applied the pixel-wise averaging, PCA and wavelet methods by considering the individual pixel values for each method and combining them. Although this combination has a better depth resolution and signal-to-noise ratio than the source images, it is restricted by the large size of the data to be calculated and is time-consuming. The benefit of image fusion based on feature-level fusion, which acts on properties such as the edge, shape and texture, is that it achieves a certain amount of information compression and supports real-time processing.