# MCIDN: Deblurring Network for Metal Corrosion Images
## 6 Conclusions  
In this paper, we propose a deblurring network for metal corrosion images, called MCIDN. Specifically, we explore the potential of channel interaction in deblurring corrosion images. SCAM generates spatial attention weights and enhances different channels in an efficient convolutional manner, identifying useful aggregated features. Additionally, FCAM refines the spectrum using frequency decomposition and modulation with simpler frequency techniques, integrating useful frequencies with frequency attention weights. To achieve better deblurring performance, we train the model with multiple loss function. The final experimental results demonstrate that our method achieves advanced deblurring performance on blurry metal corrosion images. However, the proposed SCAM and FCAM still have limitations that require further improvement. SCAM demonstrates insufficient robustness in complex scenarios, particularly when dealing with intricate corrosion patterns or significant background noise. To address this, incorporating more context-aware mechanisms and multi-scale feature extraction methods could enhance its adaptability to complex corrosion models. FCAM exhibits limited capability in processing certain high-frequency information. This limitation could be mitigated by integrating advanced techniques such as wavelet transforms or high-order spectral decomposition to capture more comprehensive frequency details and improve performance.  
The image-deblurring technology for metal corrosion has broad applications across industrial inspection, equipment maintenance, fault prediction, energy infrastructure management, and the safety assessment of buildings and bridges. By enhancing the accuracy and reliability of corrosion detection, this technology provides critical support for extending equipment lifespan, ensuring structural integrity, optimizing material development, and advancing environmental evaluation. Moreover, it serves as a cornerstone for promoting automation and intelligent analysis in corrosion monitoring and assessment.  
Conceptualization, [PERSON] ([PERSON]) and P.S.; methodology, [PERSON] ([PERSON]) and M.W.; software, P.Z. and S.C.; validation, H.D. and Y.W.; formal analysis, P.S. and J.W. ([PERSON]); investigation, H.Y.; resources, P.S. and D.S.; data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]) and P.Z.; writing--review and editing, P.S. and M.W.; visualization, S.C.; supervision, P.S. and D.S.; project administration, [PERSON] ([PERSON]) and P.S.; funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (Grant No. 2021 YFA1601104), National Key R&D Program of China (Grant No. 2022 YFA16038004), National Key R&D Program of China (Grant No. 2022 YFA16038002), National Key R&D Program of China (Grant No. 2023 YFC3805703) and National Science and Technology Major Project of China (Grant No. J2019-VI-0004-0117).  
Not applicable.  
Data will be made available on request.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MCIDN & metal corrosion images deblurring network \\ SCAM & spatial channel attention module \\ FCAM & frequency channel attention module \\ PSNR & peak signal-to-noise ratio \\ SSIM & structural similarity \\ CNNs & Convolutional Neural Networks \\ FFT & Fast Fourier Transform \\ LN & Layer Normalization \\ \end{tabular}