# MCIDN: Deblurring Network for Metal Corrosion Images
## 4 Experiment
### Experiment Result
#### 4.3.3 Qualitative Comparison  
Figure 7 presents the visual deblurring effects of various methods on blurry metal corrosion images. The \"Blurry Image\" denotes the original low-quality image, while the \"Reference\" signifies the sharp and high-quality image. Each image also displays the PSNR values of the deblurring results from different methods.  
Observing the results from the Stripformer method, we notice that the corrosion details remain relatively blurred, indicating insufficient improvement. The results from Restormer and DMPHN also exhibit noticeable blurriness with discontinuous boundaries, highlighting their limited restoration capabilities. HINet's results, although improved, still lack sharpness in edge details, which is critical for accurate corrosion detection. NAFNet performs relatively well but still presents unclear edges and corrosion details that require further refinement.  
In contrast, our MCIDN method demonstrates the best visual results, with significantly clearer corrosion contours and richer detail. This superior performance underscores the  
Figure 7: Qualitative comparison of image deblurring methods on blurry metal corrosion dataset.  
effectiveness of our approach to enhancing image clarity and preserving critical details, which are essential for accurate analysis in practical applications. The MCIDN method's ability to produce sharp, detailed images from blurry inputs highlights its robustness and generalizability across different types of corrosion images.  
The clear delineation of corrosion features in images processed by MCIDN not only facilitates better visual interpretation but also enhances the potential for automated analysis and decision-making in industrial applications. The comprehensive evaluation demonstrates that our MCIDN method sets a new benchmark for deblurring tasks, effectively bridging the gap between blurred and clear images. This advancement is particularly valuable in industrial contexts where the accurate depiction of corrosion can lead to more precise diagnostics and maintenance decisions.  
More examples of original images and their deblurred versions using our method are shown in Figure 8. Figure 8 presents the original low-quality corrosion image, Figure 8 displays the high-quality reference image, and Figure 8 illustrates the deblurred image generated by our method. We can observe that the deblurred metal corrosion images produced by our method exhibit clear edges and corrosion details, closely resembling the sharp, original images. This demonstrates the effectiveness of our approach to restoring the clarity and detail of metal corrosion images, making them highly similar to their high-quality counterparts.  
To clearly demonstrate the deblurring effectiveness of our method, Figure 9 presents the edge maps and high-frequency maps of the metal corrosion images. It is evident that there are significant differences in edge information and high-frequency details between the blurry corrosion images and the sharp corrosion images. By examining the edge maps and high-frequency maps of the deblurred images produced by our method, we can observe more distinct edges and detailed texture information. This improvement is attributed to the SCAM and FCAM proposed in this paper.  
Upon examining these images, our model demonstrates excellent deblurring performance, effectively restoring complex structures and intricate corrosion details in blurry metal corrosion images. The results indicate that MCIDN not only enhances the overall image clarity but also preserves essential features that are critical for accurate corrosion analysis.  
Figure 8: Examples of images after deblurring using the proposed method.