# A Denoising Method for Multi-Noise on Steel Surface Detection
## 3 Open-Closed Transformation Denoising Method for Defects of Steel Surface  
For the situation that small targets in complex backgrounds are easily obscured or submerged by other objects or noise, this paper proposes an open-closed transformation algorithm to eliminate or weaken the background and noise. A low signal-to-noise ratio and the measurement and acquisition of small target information are currently the main technical problems to be overcome by various advanced measurement systems. The target image occupies fewer pixels in the region and the background environment is so complex that the target is almost background covered by clutter and sometimes lost, thus making the detection of small targets very difficult; its solution is of great practical importance to improve the performance of detection systems. Due to the complexity of the scene, the degree of stability can affect the effectiveness of target detection. For example, the target image can be affected by uneven illumination, changes in objects in the background, etc. To extract targets from images efficiently, it is necessary to propose an algorithm that attenuates or eliminates the effects of background noise. In this paper, it combines mathematical morphology to calculate local maxima and minima to reduce the amount of subsequent processing and minimize the false alarm rate. To select a possible target, a region is grown for each local maximum point as a way to achieve the minima point. This algorithm is used to achieve a high-frequency component with a large area of zero background and a small area target, as well as random dot noise, for each frame after opening and closing the filtered image. Then, according to the correlation of the moving objects between adjacent frames, the multi-frame superposition is carried out. The small target, because of its mobility, in the superposition of the performance of the track is a very strong correlation point, and the high frequency point noise, because of its randomness, offset each other's part of the non-offset part of the geometric area, showing a poor correlation between the random distributions of interference points. For example, using open or closed operations for an input image, the formulas are as follows, respectively. \[g=f-f\circ B \tag{1}\]  
\[g=f\bullet B-f \tag{2}\]  
where, \(f\) is the image gray frame, \(B\) is the structure, \(\circ\) denotes the open operation, and \(\bullet\) denotes the closed operation. Filtering is performed to detect larger changes in the image, which is equivalent to the high-frequency component, and to filter out flatter changes in the image gray value, which is equivalent to the low-frequency component. Using this transform to filter the input image, the low-frequency component is filtered out, which is equivalent to filtering out large areas of the background, leaving the high-frequency component part, including the small target. Due to the differences between image acquisition systems, image data is also often eroded by noise in practical applications. And, various redundant features are mixed together, making it very difficult to conduct the subsequent interpretation of image data. To achieve the accuracy of image data, it becomes important to find a target recognition method that can measure both the intensity discontinuity and the accurate orientation of the image, and the above problems must be overcome by a new type of uncertainty processing. In some cases, most of the acquired images are blurred. In addition to objective reasons, there are some subjective reasons for blurred images, such as segmented and soiled images. For the ambiguity in these images, based on the uncertainty classification of the signal and the impact analysis, the paper uses the threshold method to process the fuzzy and proposes a fuzzy signal processing method, which can improve the image quality with mean square error, peak signal-to-noise ratio, and mean absolute error indicators of \(3.24\times 10^{-6}\), \(1.03\times 10^{2}\), and \(0.0014\), respectively, all of which are superior.