# MCIDN: Deblurring Network for Metal Corrosion Images
## 1 Introduction  
In industrial and engineering applications, monitoring and analyzing metal corrosion is paramount due to its impact on structural stability, economic risks, and safety hazards. Leveraging image processing technologies for corrosion detection has become a critical area of research. Recent advancements in image processing have significantly improved corrosion detection [1], identification [2], and segmentation [3]. However, image quality is often compromised by blurring and noise from environmental conditions and capture limitations, affecting detection and recognition accuracy. Therefore, effective image deblurring techniques for metal corrosion images are essential. The improved clarity of reconstructed images has significant implications for materials science research, enablingdetailed analysis of corrosion patterns. It also holds great potential for industrial applications, including bridge degradation assessment, pipeline corrosion detection, and quality control. In these areas, accurate identification of corrosion can enhance safety, optimize maintenance schedules, and reduce associated costs. Image deblurring is a fundamental low-level vision challenge encountered across various fields, including remote sensing, medical imaging, photography, autonomous driving, and underwater imaging [4; 5; 6; 7; 8]. Traditional non-blind deblurring assumes a known blur kernel and employs techniques like Wiener filtering and Richardson-Lucy deconvolution [9; 10]. Traditional blind deblurring, where both the blur kernel and the sharp image are unknown, relies on manually crafted priors such as L0 sparsity, low-rank, and dark channel priors [11; 12; 13; 14]. These traditional methods often involve complex optimization processes and suboptimal performance. Recent advances in deep learning have catalyzed the development of Convolutional Neural Networks (CNNs) for image deblurring. These networks effectively translated blurry images into their sharp counterparts [15; 16; 17]. Although these methods improved performance, their limited receptive field restricted their ability to model long-range dependencies effectively. Transformer-based approaches addressed the limitation of CNNs by leveraging self-attention mechanisms to enhance image deblurring performance [18; 19]. However, this led to a significant increase in computational complexity. Despite some efforts to mitigate the quadratic complexity, the computational overhead remained substantial, and the performance gains over CNNs were relatively modest [20]. In this paper, we propose an efficient spatial channel attention module (SCAM) that combines the benefits of group convolution and self-attention. Our SCAM, normalized by [PERSON] and sharing weights across the group dimension, emulates self-attention operations through a group convolution mechanism. Unlike traditional self-attention, which aggregates information across extensive regions, SCAM focuses on localized areas to reduce computational complexity. It generates integration weights for every pixel pair based on contextual information, enhancing efficiency through shared weights. Most deep learning-based deblurring methods focus on the spatial domain, often neglecting frequency domain differences. Recent efforts have aimed to reduce the frequency gap between clear and blurred image pairs. For instance, [PERSON] et al. [21] introduced a wavelet reconstruction module that decouples features into different frequency bands, though it adds computational complexity. [PERSON] et al. [22] process high-frequency signals through normal residual branches and use Fourier transform-based branches for full-frequency signals, but shared inputs can cause interference. [PERSON] et al. [23] use multiple complex OctConvs for frequency separation, but frequent downsampling and upsampling introduce additional computational burdens.