## 4 Method
### Metrics  
Image evaluation indicators encompass a wide range of content and are approached from various perspectives. Each evaluation standard has its own strengths and weaknesses. Full reference image quality evaluation involves selecting an ideal reference image, comparing it with the image to be evaluated, and analyzing the level of distortion in the evaluated image to obtain a quality assessment. Objective evaluation methods for full reference image quality commonly focus on three aspects: pixel statistics, information theory, and structural information. Pixel statistics form the basis for evaluation methods such as peak signal-to-noise ratio (\(PSNR\)), mean square error (\(MSE\)), mean absolute error (\(MAE\)), and signal-to-noise ratio (\(SNR\)). These methods assess image quality by quantifying the differences in grayscale values between corresponding pixels in the evaluated and reference images. \(PSNR\) and \(MSE\) specifically measure image quality by calculating the overall magnitude of pixel errors. Higher \(PSNR\) values indicate less distortion and better image quality, while lower \(MSE\) values indicate better image quality. These methods are straightforward to implement and widely used in areas such as image denoising. In the field of EIT, the examination of reconstructed images typically encompasses the assessment of image quality and measures of error. In order to observe the resemblance between the reconstructed image and the original image, it is possible to conduct an evaluation of indicators pertaining to image quality by means of visualization. A comparison between the two images allows for the identification of small discrepancies, which suggests a high-quality reconstruction. Two commonly used measurement indicators for image quality are \(PSNR\) and \(SSIM\), both of which are significant in assessing the quality of reconstructed images (\(PSNR\), \(SSIM\)). On the contrary, error indicators enable a numerical assessment of the accuracy of the reconstruction results. \(MSE\) and \(MAE\) are widely utilized error indicators. While measuring the error output between the reconstructed image and the original image, \(MSE\) evaluates the deviation. In contrast, \(MAE\) assesses the difference between the actual pixel value and the reconstructed value. Therefore, a comprehensive consideration of these indicators is crucial when evaluating the quality of reconstructed images in EIT (\(MSE\), \(MAE\)). We assess the level of variation at the pixel level between the restored image \(I\) and the original image \(K\) via the \(MSE\) defined as  
\[MSE=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}[I(i,j)-K(i,j)]^{2} \tag{21}\]  
The \(MSE\) value is calculated based on the total number of pixels in images \(I\) and \(K\), denoted as \(M\) and \(N\), respectively. A smaller \(MSE\) value indicates a higher similarity between the images. The \(PSNR\), which denotes the ratio between the maximum power of a signal and the power of noise that negatively impacts its accuracy in representation, is an engineering term. To assess \(PSNR\), logarithmic decibel units are often employed, owing to the extensive dynamic ranges exhibited by numerous signals. \[PSNR=10\cdot\log_{10}\frac{L^{2}}{MSE} \tag{22}\]  
\(L\) is a constant denoting the maximum dynamic range of the image data type, signifying the upper limit of the value range. For instance, in the case of image data represented by the float type, where the value range lies between 0 and 1, \(L\) is assigned a value of 1. Conversely, for image data representing uint-8 type, where the value range spans from 0 to 255, \(L\) takes the value of 255. The \(PSNR\) formula presents MSE in the denominator, implying that a higher \(PSNR\) value indicates superior image quality during evaluation, contrary to \(MSE\)'s interpretation. The \(SSIM\) index is a widely employed measure for quantifying the resemblance of two images. To directly evaluate meshes, the \(SSIM\) index has been appropriately adapted.