# A Denoising Method for Multi-Noise on Steel Surface Detection
## 6 Conclusions  
In this paper, some improvements are made based on the traditional image denoising methods, then, the advantages and disadvantages of the image multi-noise noise reduction methods in the spatial domain and transform domain are carried out. Through the analysis of the sample data, it is known that the original defect sample data all contain noise; this paper proposes an open-closed transform algorithm for noise reduction in the original data, which can effectively retain the defect information, thus reducing the possibility of the defect target being overwhelmed by noise because of its small size. In order to solve the problem of the small number of samples in the dataset publicly used for detection, a super-resolution generative adversarial network is established to augment the image samples, while combining classical image data enhancement methods, such as rotation and misplacing, without affecting the high-frequency information of the defective target so that the sample data is augmented four times of the original image, which does not only change the characteristics of the defects in the original image, but also can avoid the image defects caused by cutting or brightness change. The proposed method improves the generalization ability of the sample data and the robustness of the algorithm. It is obtained through experiments that the denoising method proposed in this paper not only has a higher denoising accuracy and faster denoising speed, but also has a stronger anti-interference ability and better denoising effect, which lays the foundation for the input of the subsequent classification network and target detection network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Several Detection Methods** & **Denoising Accuracy (\%)** & **Denoising Speed** & \
\begin{tabular}{c} **Noise** \\ **Resistance** \\ \end{tabular} &
\begin{tabular}{c} **Adaptation to** \\ **Environment** \\ \end{tabular} \\ \hline Proposed method & 92.70 & Fastest & Strongest & Most complex \\ Transformation domain & 88.23 & Fast & Stronger & More complex \\ Space domain & 85.65 & Faster & Stronger & General \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comprehensive comparison of proposed and existing denoising methods.  
However, the multi-noise denoising method proposed in this paper has been experimented with only in the case of the existence of multiple noises on the steel surface, which has certain limitations. First, the sample data selected in this paper are only the hot-rolled steel defect data in the open dataset, which is too homogeneous and does not use other types of defect sample data. In addition, the method in this paper needs further enhancement for distortion or occlusion caused by camera acquisition. Future research will consider the bounded nature of the activation function in neural networks, as the output of the activation function is also in a limited range within a certain range of the input space, which helps to prevent problems such as vanishing or exploding gradients. In this paper, further research will be carried out to verify the generalization ability of the proposed network algorithm using more open and other defective datasets, and to expand more application scenarios in order to promote generalization and wide application.  
Conceptualization, Z.C.; methodology, Y.L.; software, H.C.; validation, L.Z.; writing--original draft preparation, W.W.; writing--review and editing, Q.W. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Key Science and Technology Program of Henan Province (22102210084); Key Science and Technology Project of Henan Province University (23A413007), respectively.  
All data generated or analyzed during this study are included in this published article. We do wish to share our raw data, and these data are original.  
This work is supported by [PERSON].  
This manuscript has not been published and is not under consideration for publication elsewhere. We have no conflict of interest to disclose.