# A Denoising Method for Multi-Noise on Steel Surface Detection
## 4 Super-Resolution Adversarial Data Processing
A number of real high-resolution images are randomly selected in the network system, and using the real low-resolution images after scaling, they are passed to the generator to obtain a number of virtual high-resolution images; with the label information of the real image defined as 1 and the label information of the virtual image defined as 0, the real image and the virtual image are jointly used as the training set into the discriminator to be trained. For the training of the generator, it is expected that it will obtain extremely realistic virtual images, so when training the generative network system, it is necessary to master the criteria for the discriminator to judge the authenticity of the input image. In the training network, the real low-resolution images provided are first processed by the training generator, and after the virtual real high-resolution images are obtained, the loss is calculated and obtained by comparing the judgment results obtained from the virtual real high-resolution images with those obtained at the beginning, which means that the training generator unit completes the training according to the judgment results of the discriminator. Subsequently, the real high-resolution image and the virtual high-resolution image are put into the VGG network system to obtain the characteristics of the two images, and another loss is calculated by comparing the characteristics of the two images. The formula for each loss of the super-resolution generative adversarial network is as follows:  
\[l^{SRGAN}=l_{c}^{SR}+10^{-3}l_{generator}^{SR} \tag{6}\]  
\[l_{c}^{SR}=mse(vgg(G(LR)),vgg(HR)) \tag{7}\]  
\[l_{generator}^{SR}=\sum_{n=1}^{N}-\log D_{\hat{\beta}_{D}}(G_{\hat{\beta}_{G}} (I^{LR})) \tag{8}\]  
where \(LR\) represents the low-resolution image, \(HR\) represents the real high-resolution image, and \(D_{\hat{\beta}_{D}}(G_{\hat{\beta}_{G}}(I^{LR}))\) represents the chance that the image produced by the generator is a real high-resolution image by identifying it. In the super-resolution task, the estimation usually includes three losses: the error loss represented by MSE, the cognitive loss, and the adversarial loss of the generative adversarial network; the loss of the generative adversarial network system is used to train the network, and the cognitive loss is used by the network for the super-resolution task as a way to evaluate the performance of the generative adversarial network [30]. Content loss and adversarial loss are used together as loss functions for the optimization of the network. After augmenting the dataset using Super Resolution Generative Adversarial Network (SRGAN), the image data were then rotated and misplaced to further augment it, and some of the data results are shown in Figure 6.  
The first column is the original image, the second column is the super-resolution adversarial neural network processed image, which is scaled to the same size as the original image for typographic purposes, the third column is a series of images obtained by rotation, and the last column is a series of images obtained by cutting. After these operations, a new dataset of four times the original images are obtained; Table 1 shows the number of defective images in each category. The diversity of the information set is enhanced and the generalization function of the detection network system is improved. Combining the labeling information of the defects in the original dataset, the defects in the  
Figure 6: Image amplification using different methods. newly constructed dataset are re-labeled with the help of open-source labeling software (Labelme v.3.0. ), which is then fed into the subsequent classification and detection network to extract the feature maps at each level and finally complete the detection task, and the final weight information obtained from the network can be applied to the actual measurement data of the plant. As can be seen from Figure 6 and Table 1, the super-resolution generative adversarial network proposed in this paper processes the image samples.