## 4 Image Fusion Applications in NDT Inspection
### Learning-Based Approaches
#### 4.5.1 Neural Networks
A recent study [90], as an example, used an ANN structure to train eddy current (EC) impedance and ultrasonic amplitude inputs, aiming to overcome problems as a result of the blind zone of ultrasonics on the near-surface and the electromagnetic skin effect of the EC on the subsurface. The authors employed a database that included a simulation of signal propagation into an aluminium block with holes for estimating the depths and radii of the holes in a regression. The ANNs were also used to characterise and validate the study using real holes experimentally. Mean absolute error (MAE) calculations were performed to assess the accuracy of the defect estimation parameters. The experimental results show that the error of the depths and radius estimates was limited to 4 percent on average, although it showed a peak of 11 percent for one hole. It is stated that this complementary approach might be promising for investigating more complex structures and various defect types. In the theme of the neural network, the recent study on multi-modal infrared image fusion used a hybrid fusion structure that included a non-subsampled shearlet transform and a pulse-coupled neural network [91]. Two types of impact-defected fibre-metal hybrid composite laminates were inspected by vibro-thermography (VT) and pulse thermography (PT). Three-step feature extraction, feature selection and a fusion method were applied as shown in Figure 7. Fourier analysis for the frequency domain features and intensity contrast criterion for the feature selection were used. Feature selection in the time domain was made using a temperature curve that allowed for determining the thermal contrast over time. For the fusion, images were decomposed by a shearlet transform, followed by the weighted mean and pulse-coupled neural network fusion for the low- and high-frequency  
Figure 6: Basic neuron architecture, called the threshold logic unit (TLU), showing inputs (I), weights (W), bias (b), activation function (f) and output (X) [89]. sub-bands, respectively. The fusion results indicated a conspicuous improvement in crack contrast and detecting the boundary of deformation rather than a single excitation mode. Another important structure for fusion studies is the convolutional neural network (CNN). This is one of the most beneficial tools for image analysis applications due to its advanced feature extraction ability through convolution operations instead of standard matrix multiplication. A CNN may use multiple convolution layers to obtain features hierarchically, max pooling layers to minimise image sizes and batch normalisation for reducing internal covariance. A loss function is used to find the difference between the expected and predicted results and tries to minimise them [41]. Historically, after AlexNet [92], various CNN-based algorithms have been proposed to gain a better understanding of image analysis cases. U-Net achieved an efficient feature extraction method even in a small dataset using structural segmentation [93]. ResNet was developed to overcome gradient degradation problems in the training of deep neural networks using a residual network [94]. After that, DenseNet was proposed to obtain a densely connected structure by engaging all layers with each other [95]. GoogLeNet has used an inception module that has multiple kernels of different sizes for convolution to ensure more efficient computation [96]. A couple of studies that have been published recently have focused on the fusion of NDT methods through neural networks to inspect various materials, from wooden cultural heritage to common composites [97; 98; 100; 101; 102]. Here, the text will not give the details of these approaches. (The interested reader can refer to the specific papers cited.) However, Table 6 demonstrates the sources and aims of those neural network-based research works in various fields. The use of learning approaches in NDT inspection can greatly benefit the field (classify and learn image properties, defects characteristics and features).