## 3 Results
### Comparison between the Other Deraining Methods  
The deraining models were evaluated on the Rain14000 with two traditional methods [12; 13] and four rain removal convolutional neural networks SIRR [23], LPNet [18] MSPFN [19], and DTDNet. Specifically, in order to show the advantages of rain removal network in this paper compared with DTDNet, we trained DTDNet on the same dataset. The network training parameter epoch was 100, the batch size was 64, and the learning rate was fixed at 0.001. The Adam optimizer was used to accelerate the training. The default settings of optimizer parameters \(\beta_{1}\) and \(\beta_{2}\) were 0.9 and 0.999. To ensure the fairness of the experiments, all the above comparison methods adopted their publicly released codes and retrained with the same dataset Rain14000 in the experiments, and followed their original settings under the unified training dataset. The quantitative performance of our model and the comparative models are shown in Table 1. As Table 1 shows, the rain removal network designed in this paper is significantly better than the traditional rain removal method and other rain removal networks in quantitative index comparison. Moreover, qualitative comparisons are shown in Figure 6. The four test images were selected from Rain100H and Rain100L with different rain streaks texture and density, respectively. On the basis of comparing with the original picture, the information entropy and contrast were also calculated for each image. The format of \"Entropy/Contrast\" is noted below the image. It can be seen from the experimental results that the rain removal network designed in this paper is superior to other comparison methods, especially for the visual effects. In addition, the overall performance of the rain removal method based on a convolutional neural network is better than the traditional image rain removal method. Specifically, the two traditional rain removal methods could only remove a small part of the rain streaks, and there were obvious removal artifacts. Besides, the removed region of the rain streaks could not be merged with the image background. Compared with the traditional methods, more rain streaks could be removed and the image background could be better restored by SIRR and LPNet. However, there were still more visible rain streaks that had not been removed and the resolution of the output image was limited. MSPFN had a considerably better rain removal effect, but it can be seen from the second picture that some obvious rain streaks still remained. No obvious rain streak residues were found in the rain removal network designed in this paper. Moreover, the information entropy and contrast of MSPFN were lower than that of the proposed rain removal network. It can be seen that the proposed network has a good rain removal effect and strong applicability on synthetic images. For further general verification of the proposed method, additional experiments were conducted on four real-world images collected from the internet, including portraits, still life, and so on. The rain removal results are shown in Figure 7. In addition, the information entropy and contrast were used to further quantitatively evaluate the rain removal performance. It can be seen from Figure 7 that the deraining results of the two traditional methods were limited, and a large number of rain streaks could not be removed. For the third image, SIRR, LPNet, and MSPFN all had better rain removal effect visually, but the values of information entropy and contrast were lower than the rain removal network designed in this paper. For the other three images, more rain streaks remained with SIRR and LPNet compared with MSPFN and the proposed network. In particular, it can be seen from the comparison between the second image and the fourth image that the