## 4 Image Fusion Applications in NDT Inspection
### Learning-Based Approaches
#### 4.5.1 Neural Networks  
Artificial neural networks (ANNs) are one of the most useful forms of machine learning (ML) in several fields, such as classification and pattern recognition. The high-speed \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sources** & **Material** & **Defect Types** & **Aim** & **Reference** \\ \hline Eddy current images (at two different frequencies) & Stainless steel & Machined notches & To eliminate noise & [76] \\ \hline Terahertz and X-ray CT techniques & GFR & Foreign body or delamination & To combine higher contrast of terahertz and higher resolution of X-ray & [20] \\ \hline Infrared, visible light and laser inspections & Copper & Foreign body, voids or cracks & To enhance system stability with the multi-sensor information & [78] \\ \hline Visible camera images & Wall (non-defined) & Cracks & To minimise the complexity and diversity of wall images & [79] \\ \hline Ground-penetrating radar (GPR) and ultrasonic echo array (UEA) & Concrete & Geometry or foreign body & To achieve enhanced images of the interior of concrete & [80] \\ \hline Eddy current pulsed thermography & CFRP-steel structure & Cracks & To remove information on the texture that hides defects & [81] \\ \hline Ultrasonic and Step-heating thermography inspections & Carbon/epoxy- aluminium & Delamination or debonding & To inspect patch and binding quality & [77] \\ \hline Eddy current and pulsed thermography & CFRP & Impact & To provide comprehensive information from impacted CFRP structure & [82] \\ \hline Ultrasonic, thermographic images and CAD model & \(\ast\) 1 & Geometry or voids & To provide defect mapping in the partial CAD model & [83] \\ \hline Eddy current, magnetic flux leakage and flying laser spot thermography inspections & Steel & Cracks & To improve near-surface defect detection in magnetisable and conductive materials & [84] \\ \hline Terahertz images (in various configurations) & GFR & Foreign body or delamination & To ease detecting deeper flaws & [85] \\ \hline \hline \end{tabular}  
* The material is mentioned as aeronautical composites without any details.  
\end{table} 
Table 5: A summary of the current wavelet applications for NDT image fusion. processing offered by a highly parallel implementation represents ANNs' greatest potential. The ability to create simple and accurate models from complicated natural systems with vast inputs is a strong benefit of ANN applications. They can behave as a functionalised management system similar to the human brain's nervous functions. ANNs mimic the operations of an interconnected arbitrary number of nodes by traversing a number of levels with the input characteristics [86]. The basic components of an ANN's structure can be considered to consist of inputs, weights, a transfer function, an activation function and bias [87]. The inputs refer to the measurements of the features that will be modified. The weights are the scalar multiplications responsible for evaluating each input's significance and directionality. The transfer function merges the inputs into a single output value in order to apply the activation function. An activation function captures the information from the transfer function and transforms that into a more useful form, which will be used in the next stage. Bias can be considered a systematic error, given as extra input to the system due to the assumptions during the process [88]. The basic artificial neuron architecture, the so-called threshold logic unit (TLU), is demonstrated in Figure 6.  
ANNs have been constructed by multi-neurons, which use weights that can learn and be changed after each iteration through neurons, depending on the accuracy. An ANN structure is able to make predictions in the form of regression or classification, followed by a training step for a particular dataset.