## 3 Methodology of the Proposed Concept
### Pre-Image Processing  
Pre-image processing operations aim to improve the raw image data that are suppressed by unwanted distortions on some image features, which is essential for further processing like feature extraction [38]. The pre-image processing is composed of size adjustment and resolution enhancement. The pre-processing technique (e.g., resizing and filtering or denoising) aimed to improve the raw image data suppressed by unwanted distortions of some image features. Adjustments in size were made to ensure uniformity and the proper execution of statistical measurements. Table 3 provides the information of the thermal solar cell images. The pre-processing technique, as demonstrated in Algorithm 1, made use of the MATLAB functions. These functions such as _imread_, _imshow_, _irresize_, _medfit12_ were carried out using the MATLAB 2020b platform, and a brief discussion of each step is included in the following section. These syntaxes can be found in the MATLAB Documentation ([[https://www.mathworks.com/help/matlab/](https://www.mathworks.com/help/matlab/)]([https://www.mathworks.com/help/matlab/](https://www.mathworks.com/help/matlab/)) (accessed on 7 January 2021)). In addition, the filtering procedure is defined in step 3 of the procedure. \begin{table}
\begin{tabular}{c c} \hline \hline
**PV Module Brand/Company Name** & **LUMOS SOLAR (LLC)** \\ \hline Cell technology & Monocrystalline \\ Maximum power (P\({}_{\text{MAX}}\)) & 270 W \\ Short-circuit current (I\({}_{\text{SC}}\)) & 9.74 A \\ Open-circuit voltage (\({}_{\text{VOC}}\)) & 27.78 V \\ Current of maximum power (I\({}_{\text{PMAX}}\)) & 9.08 A \\ Voltage of maximum power (V\({}_{\text{PMAX}}\)) & 33.08 V \\ Module area (Size) & 1645 by 983 by 42 (mm\({}^{3}\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Solar panels technical specifications. Figure 6: Processing blocks of the proposed algorithm. The steps are as follows:  
Step 1: Loading and Reading of Images.In this step, the raw thermal solar cell images are to import the libraries of the samples to use for this proposal. First, the MATLAB function \"_dir_\" prepares the listing of the files and folders in the current folder for the task. Then, the \"imread\" function was implemented at this stage. Below are the description syntaxes of these two functions. A = imread(filename, fmt); %this function reads the image from the file specified by filename and saved in the variable \"A\"  
dir name; %provides the lists files and folders in the current folder  
Step 2: Resizing of Images.In order to visualize the change in the images, the two functions were created; the first is to display the sample (original) image and second is to compare the original and resulting image. In this study, the base sizes of all sample images are set into a 400 \(\times\) 400 pixel value. The MATLAB function \"_imrsize_\" provides the image in the scale size necessary for the required task. The function \"_imshow_\" displays the image for visual verification. Step 3. Image Filtering.In this study, median filtering (\(P_{p}\)) was implemented to remove noise preserving the edges of the sample images. The noise reduction aims to improve the results or the later processing of this study. The median filter operates by moving the image pixel by pixel and replacing each value with neighboring pixel's median value. The pattern of neighbors is known as the \"window,\" and it slides pixel by pixel across the entire image, 2 pixel by pixel. The median is calculated by first sorting all of the pixel values in the window into numerical order and then replacing the pixel under consideration with the pixel with the middle (median) value. Equation (1) moved to the initial dataset (\(D_{O}\)) to produce the pre-processed dataset (\(P_{D}\)). \[P_{D}=D_{O}\,^{*}\,P_{p} \tag{1}\]  
Note: The operator \"*\" in Equation (1) performs the MATLAB function of median filtering on the said image dataset.