# MCIDN: Deblurring Network for Metal Corrosion Images
## 4 Experiment
### Experiment Result  
#### 4.3.1 Training Process Comparison  
Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) are widely used metrics for evaluating the performance of image deblurring algorithms. PSNR quantifies the difference between the deblurred image and the original image by measuring the pixel-wise mean squared error (MSE). Higher PSNR values indicate that the deblurred image is closer to the original image, thus signifying better image quality. SSIM, on the other hand, evaluates the perceptual similarity between the deblurred image and the original image by comparing luminance, contrast, and structural information. Higher SSIM values denote greater similarity and, therefore, better visual quality. In the context of image deblurring, achieving high PSNR and SSIM values signifies that the algorithm effectively restores image details and reduces blurriness, resulting in clearer and more accurate images.  
We compare the training process curves of our method with several state-of-the-art (SOTA) methods on the blurry metal corrosion dataset, as illustrated in Figure 6. The horizontal axis represents 20,000 iterations of the model. Figure 6a shows the PSNR curves for different methods as a function of iteration count, while Figure 6b presents the SSIM curves, with SSIM values ranging from 0 to 1. For enhanced visibility, the Y-axis for SSIM starts from 0.8.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Total** & **Training Set** & **Validation Set** & **Test Set** \\ \hline Original images & 600 & 480 & 60 & 60 \\ Blurry images & 600 & 480 & 60 & 60 \\ Total & 1200 & 960 & 120 & 120 \\ \hline \end{tabular}
\end{table}
Table 2: Detailed divison of the metal corrosion images.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Corrosion** & **Total** & **Training Set** & **Validation Set** & **Test Set** \\ \hline
5 & 120 & 96 & 12 & 12 \\
6 & 120 & 96 & 12 & 12 \\
7 & 120 & 96 & 12 & 12 \\
8 & 120 & 96 & 12 & 12 \\
9 & 120 & 96 & 12 & 12 \\ Total & 600 & 480 & 60 & 60 \\ \hline \end{tabular}
\end{table}
Table 3: Detailed distribution of different classes of corrosion ratings.  
The PSNR curve of Restormer demonstrates the slowest change, underscoring the challenging nature of training on the blurry metal corrosion dataset. HINet and NAFNet exhibit very similar trends in their PSNR and SSIM curves, with their final results being very close. In contrast, our method shows the fastest improvement in both PSNR and SSIM curves, achieving superior final results compared to the other methods. Specifically, our method reaches a final PSNR of 32.8645 dB and an SSIM of 0.9768.  
This superior performance can be attributed to the effective integration of spatial and frequency attention mechanisms, which allow our model to better capture and enhance relevant features in the blurry metal corrosion images. The rapid convergence of our method indicates a more efficient learning process, which is critical for practical applications where training time is a significant factor. Additionally, the high final values of PSNR and SSIM suggest that our model not only learns faster but also generalizes better to the task of deblurring metal corrosion images, making it a robust and reliable solution for industrial applications.