# MCIDN: Deblurring Network for Metal Corrosion Images
## 2 Related Works
### Frequency Learning in Image Restoration  
From the perspective of image restoration, image deblurring requires addressing various frequency signals to achieve clearer images. Low-frequency signals correspond to smooth transitions, while high-frequency signals are associated with abrupt changes such as edges and textures. The Fast Fourier Transform (FFT) facilitates efficient global feature modeling by leveraging the convolution theorem. [PERSON] et al. [54] propose a Fourier-based exposure correction network, employing amplitude and phase sub-networks to reconstruct luminance and structural information. [PERSON] et al. [55] introduce a spatial  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Methods** & **Dataset** & **Network** & **Metrics** & **Application** \\ \hline DeepDeblur [38] & GoPro & CNN & PSNR, SSIM & General images \\ DMPHN [15] & GoPro & CNN & PSNR, SSIM & General images \\ MPRNet [16] & GoPro & CNN & PSNR, SSIM & General images \\ HINet [48] & GoPro & CNN & PSNR, SSIM & General images \\ Restormer [18] & GoPro & Transformer & PSNR, SSIM & General images \\ NAFNet [33] & GoPro & CNN & PSNR, SSIM & General images \\ SRN- & GoPro & RNN & PSNR, SSIM & General images \\ DeblurNet [41] & GoPro & GAN & PSNR, SSIM & General images \\ DeblurGan- & GoPro & GAN & PSNR, SSIM & General images \\ v2 [49] & GoPro & GAN & PSNR, SSIM & General images \\ DBGAN [50] & GoPro & Transformer & PSNR, SSIM & General images \\ SwinIR [45] & GoPro & Transformer & PSNR, SSIM & General images \\ Stripformer [20] & GoPro & Transformer & PSNR, SSIM & General images \\ GRL [47] & GoPro & Transformer & PSNR, SSIM & General images \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of recent advancements in image deblurring methods.  
frequency attention network that uses FFT for global information modeling. [PERSON] et al. [56] embed FFT into a cascaded network to enhance brightness and reduce noise in low-light images. [PERSON] et al. [57] convert spatial features into the frequency domain for learning across frequencies.  
Additionally, wavelet transform has been incorporated into image restoration networks. [PERSON] et al. [58] propose a wavelet-based diffusion model, learning high-quality image distribution from low-quality wavelet spectra. [PERSON] et al. [21] suggest using distinct convolutional layers for wavelet components, while [PERSON] et al. [59] design a wavelet loss using dual-tree wavelet transforms to capture complex snow shapes. Techniques like filtering [60, 61], OctConv [23], and Discrete Cosine Transform [62] have also been explored.  
In this paper, we introduce a comprehensive approach combining spatial and frequency domain mechanisms. Our SCAM enables the model to focus on blurry areas within the image, identifying regions requiring deblurring. Concurrently, our FCAM enhances high-frequency details in these regions, significantly improving overall deblurring performance. This dual-domain approach allows the model to flexibly handle diverse frequency information with lower complexity, effectively aggregating essential frequency components for enhanced image restoration, particularly for metal corrosion images.