# A Denoising Method for Multi-Noise on Steel Surface Detection
## 1 Introduction  
With the rapid development of the country's industrial information technology, the use of steel has spread to all walks of life; from civilian household products to military special products, all need a great deal of steel, and in the field of machinery manufacturing, aerospace, and other areas, the quality of steel products needs ever higher requirements. According to the statistics of relevant researchers in recent years regarding large steel enterprises orders, the unsatisfactory performance of the quality of steel is mostly related to surface defects. Common steel defects include delamination, scarring, pulling, cracking, hairline, bubbles, impurities, pitting, etc. These surface defects will seriously affect the use of steel, but also reduce the corrosion resistance, oxidation resistance, wear resistance, and strength of steel products. Therefore, the accurate and rapid detection of surface defects on steel products is not only very important for large rolling mills and even the steel industry market, but also a key task for metallurgical enterprises. The presence of a variety of noises on the steel surface is an important obstacle to the accurate detection of defects. This paper deals with the multiple noises existing on the steel surface in order to lay a good foundation for the detection and classification of steel defects. Along with the rapid progress of CCD (Charge-Coupled Device) technology [1], line array cameras have been used in the field of steel surface defects in large rolling mills [2; 3]. If the production line is arranged with line array cameras, the collected image data can be monitored in real-time, and the quality control workers can observe the surface defects of steel products through the monitor. [PERSON] et al. [4; 5; 6] proposed the use of ultrasonic detectors for denoising methodologies, and [PERSON] et al. [7; 8; 9; 10] proposed the use of infrared technology for the tracking and detection of small targets. [PERSON] et al. [11; 12] also used bright-field and dark-field illumination techniques to monitor surface defects on steel plates. [PERSON] [13] used LED light sources to design a complete system fordetecting surface defects on rolled steel. Today, more large steel rolling enterprises still use this method to monitor the quality of steel surface defects, and although this method has improved the working environment of quality control workers to a certain extent, its drawbacks are also significant, with still very costly human resources, a method of detection whose results are far from robust, and damage to economic benefits for enterprises. With the progress of image processing technology and the increase in labor costs, the use of machine vision algorithms to replace manual vision has become a trend [14; 15; 16; 17]. On this basis, machine vision-based steel surface defect detection technology is becoming increasingly mature, and larger steel rolling enterprises have thus reduced the defect rate and gradually improved their business efficiency. With the ability to improve production efficiency while also largely relieving manual fatigue, powerful computer arithmetic and efficient vision processing algorithms can make the detection of surface defects more efficient and accurate. Traditional machine learning algorithms are generally composed of structures such as image processing, image noise reduction, feature extraction, classification, and detection, and each of these structures play a significant role in the final result. [PERSON] et al. [18] propose a new method for image denoising based on a motion decomposition framework, but there are limited usage scenarios. A localized discharge signal denoising method based on improved variational modal decomposition was proposed by [PERSON] et al. [19], but the accuracy of the method could be improved. [PERSON] et al. [20] proposed a three-stage denoising method based on a priori deep image using generation and fusion strategies, but there is no wide application. [PERSON] et al.