## 4 ADR Algorithms
### Challenges with DLIT Images  
It has been shown in the previous section that the DLIT process can generate images with high signal-to-noise ratio (SNR) to facilitate operators to spot defects, e.g., Figure 10. However, the DLIT images also bring several challenges when dealing with region of interest (ROI) detection, which will be discussed in the following paragraphs. Firstly, the intensity of each wire is not uniform and should be investigated individually. For instance, it can be observed from Figure 11b that the first three wires are clearly visible in grayscale, whereas the last two wires are not visible directly in grayscale, unless scaled as shown in Figure 7a. In this paper, the overall intensity level for each wire is estimated by inspecting averaged pixel magnitude per row, calculated from the equation below:  
\[I_{r}=\frac{\sum_{i=0}^{c}pixel_{r,i}}{c} \tag{1}\]  
where \(r\) is the row number, and \(c\) represents the number of columns in the image. Note that because the images are generated using DLIT method, the pixels are dimensionless and are not actual temperature levels. In Figure 12, an intensity estimation plot is presented, based on the data shown in Figure 11, which were collected from a five-wire sample with no defects. It is evident from Figure 12 that the intensity level of the three wires (ROI from row 0 to 300) are clear. However, as the wire becomes thinner for the last two wires (ROI from row 300 to 500), the intensity level is barely noticeable in relation to the background. Additionally, the intensity curve within the edges of each wire resembles a \"bathtub\" curve,  
Figure 11: Demonstration of input data matrix for ADR and its visualization: (**a**) Input data matrix example, precision set to 2 significant decimals; (**b**) The corresponding visualization of (**a**) in grayscale. Figure 12: Demonstration of intensity estimate per row based on data shown in Figure 11.  
where the lowest intensity occurs in between the edges. This makes it more challenging to distinguish the thinner wires against the background noise, especially when applying traditional thresholding methods, such as [PERSON]'s method [21]. [PERSON]'s method is effective in automatically choosing threshold to separate all the pixels into two classes, namely the foreground and background, but its efficacy deteriorates as the intensity of the thin wires tend to the values of background noise while the thick wires are distinctly stronger. Similarly, other widely adopted edge-based methods such as the Canny edge detector [22] perform less effectively. The Canny edge detector is a multi-stage algorithm that discovers the intensity gradients of the image in both horizontal and vertical directions, and then applies thresholding to determine potential edges. It therefore suffers from lack of contrast in the weak edges. Although it is likely to achieve optimal edge detection results through fine-tuning of the Canny detector parameters including the width of Gaussian filter, the lower and upper threshold for hysteresis thresholding, but the process is tedious. As shown in Figure 12(a), [PERSON]'s method separates the top three wires which are strong, but completely misses the two thin wires at the bottom. The Canny edge detector in Figure 12(b) performs better where all five wires are detected, although some background noise persists. However, the process of finding the optimal parameters for the Canny edge detector are challenging, because the efficacy of the Canny edge detector is highly dependent on the low and high thresholding as well as the Gaussian filter used. In addition to the thresholding and edge-based detection methods, there are other methods proposed based on various algorithms [23; 24; 25; 26], such as region-based methods [27], clustering-based methods [28] and deep-learning methods [29; 30; 31]. However, they all have their own drawbacks.