# PCB Defect Images Super-Resolution Reconstruction Based on Improved SRGAN
## 4 Simulation Experiments and Results Analysis
### Experimental Results  
The experiments in this paper compare the reconstructed images based on the SRVIT super-resolution reconstruction model for PCB defect images with the images generated by five different image reconstruction algorithms, namely Bicubic [7], SRCNN [9], FRSCNN [10], VDSR [11], and SRGAN [14], with all network training environments kept consistent. The Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and Mean Opinion Score (MOS) values are shown in Table 2, and the image pairs of some of the test set images after reconstruction in different models are shown in Figure 9:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **PSNR** & **SSIM** & **MOS** \\ \hline Bicubic [7] & 26.15 & 0.69 & 3.15 \\ SRCNN [9] & 26.25 & 0.71 & 2.55 \\ FSRCNN [10] & 28.34 & 0.79 & 1.95 \\ VDSR [11] & 29.16 & 0.80 & 1.98 \\ SRGAN [14] & 31.45 & 0.86 & 4.41 \\ SRVIT & **32.27** & **0.89** & **4.63** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation results of PCB defect reconstructed image with different models.  
According to Table 2, the quantitative evaluation results and the displayed images in Figure 9, the image reconstructed by the Bicubic method is poor, with the lowest values of PSNR and SSIM, the overall visual effect is blurred, and the MOS score is low. The three algorithms SRCNN, FRSCNN, and VDSR have improved values in the objective evaluation metrics compared to Bicubic. The general outline of the image is clearer than Bicubic, based on the CNN's expertise in capturing low-frequency information. However, because they are not good at capturing high-frequency information, the image details are severely lost and distortion occurs, and the MOS scores are all very low. Both SRGAN and SRVIT algorithms showed significant improvements in image reconstruction quality. Looking closely at the defective solder in Figure 9, the reconstructed image of the improved model is sharper at the edge of the inner hole and the SRGAN reconstructed image shows a slight distortion at the copper deposit below the solder hole; the reconstructed image of the improved model shows no distortion at the copper deposit. In addition, in Figure 10, the reconstructed image of the improved model is sharper at the inner wire edge at the defective notch. SRVIT optimizes and improves on the shortcomings of SRGAN, such as inadequate local feature extraction and distortion. The improvements were achieved in both objective PSNR and SSIM values, where PSNR improved by 0.82 dB and SSIM improved by 0.03, and in subjective MOS scores. The comparison of the spatial complexity of SRVIT and SRGAN is shown in Table 3:  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & **Discriminator** & **Generator** \\  & **(Params/Model Size)** & **(Params/Model Size)** \\ \hline SRGAN [14] & 5.22 M/19.9 MB & 0.73 M/2.9 MB \\ SRVIT & **3.21 M/12.4 MB** & **0.71 M/2.8 MB** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Space complexity comparison.  
Figure 9: Comparison of reconstructed images of different models.  
As can be seen from Table 3, the SRVIT algorithm proposed in this paper effectively reduces both the number of parameters and the model size in the discriminator stage compared with SRGAN, in which the number of parameters is reduced by 2.01 M and the model size is reduced by 7.5 MB. The generator is also reduced compared with SRGAN, in which the number of parameters is reduced by 0.02 M and the model size is reduced by 0.1 MB. Combining the two PSNR and SSIM evaluation metrics, the improved super-resolution reconstruction algorithm of PCB defect images based on SRGAN in this paper not only improves in pixel-level image quality with higher human eye evaluation, but also effectively reduces spatial complexity.