# A Denoising Method for Multi-Noise on Steel Surface Detection
## 2 Related Works  
In this paper, the main focus is on open datasets and images collected from steel mills. The surface defect dataset constructed by Professor [PERSON]'s team in the School of Mechanical Engineering and Automation at Northeastern University was highly evaluated by Professor [PERSON] (ASM Fellow and TMS Fellow, President of the Minerals, Metals, and Materials Society, and member of the Materials Advisory Board) of Carnegie Mellon University. On a corporate level, the Steel Defect Detection Challenge was held using this dataset as the base data. The open data set consists of 1800 images, of which the size of a single defect image is 200 \(\times\) 200 and the size is about 20 KB, including six types of defects, such as scratches, scars, impurities, pitting, etc. The open data collected in the steel plant are 18 CrNiMo7-6+HH and 42 CrMo, including two types of gear steel and alloy steel, central sparseness and a white bright band, and two kinds of defects. The open data collected from the steel mill are 5742 \(\times\) 3648, size 1-2 M. In more steel production enterprises in China, the above mentioned common defects are mainly observed manually through the naked eye, if necessary, with the help of a magnifying glass or microscope at each location of steel for detailed inspection, so as to ensure the reliability of surface defects detection. Finally, according to the standards set by the industry, the detected surface defects are judged to be within a reasonable range, and if they are out of this range, they are defined as scrap and need to be reprocessed. The open data set and the actual measured data from steel companies are shown in Figures 1 and 2 below:  
Figure 1: Open data set.  
The research in this paper helps steel sorting and inspection processors to understand the laws of inspection targets. In addition, there is the influence of other interfering objects to give appropriate decisions. It is essential to find ways to suppress or eliminate the influence of undesirable factors on them all. It can save resources and costs, reduce unnecessary losses or injuries, etc. for the business units using the study, as well as providing an experimental basis for steel inspection. From the above, it can be seen that it is very important to use a steel surface defect detection method with a higher detection efficiency and accuracy in large steel rolling enterprises, especially in the production processing lines. Based on this, this paper researches the problem of multi-noise denoising on steel surfaces by combining computer vision and deep learning, and further refines and verifies the effectiveness of the proposed algorithm through experiments. Figure 3 illustrates the overall structure of this paper.  
The rest of this paper will be discussed in the following sections. Section 3 proposes an open and closed transform algorithm to eliminate or attenuate multiple noises. Section 4 establishes a super-resolution generative adversarial neural network for sample image enhancement. Based on this, a data enhancement method is given to incorporate raw defective high frequency information into classical image enhancement methods such as  
Figure 3: The overall structure of this paper.  
Figure 2: Collected steel samples.  
rotation and error slicing. Section 5 presents the experimental results and analysis and Section 6 is the conclusion.  
Highlights:  
The novelty of present work had been abstracted as follows:  
* an open-closed transformation algorithm is proposed;
* a super-resolution generative adversarial neural network is established;
* a data enhancement method is given.