## 4 Results and Analysis
### Accuracy Validation with Known Noise Levels  
For validating the accuracy of our model, the methods described in Section 2.2 were selected for contrast and analysis. Additionally, the different noise levels described in Section 2.2 were added into Figure 1. Take Figure 1e as an example of a noisy image (shown in Figure 5). The image degrades seriously when the noise variances are 2 and 15, which conflicts with the principles described in Section 2. However, they are still retained to validate the performances of these methods. Different noises are added into each image to manufacture noisy images. In order to facilitate a statistical analysis and display, the estimated noise SDs in the same noise levels are summed, and their mean values are computed. The results are shown in Table 4.  
Table 4 shows the noise estimated by the traditional and neural-network-based algorithms. These methods are tested on noisy images, of which the noise levels are known clearly. The neural-network-based methods obtain less deviation errors than the traditional ones, which are mainly affected by different texture features and uneven distributions of ground surfaces. The proposed CNN trained in the novel method achieves an average RMSE of 3.9%. Meanwhile, the Net-1 trained in the traditional method achieves 8.5%. Other methods achieve larger errors. These statistics do not include the last group of data. This is  
Figure 4: The comparison on RMSE results of Net-1, Net-1-M, Net-2, and Net-2-M. Net-1-M and Net-2-M are the models obtained by Net-1 and Net-2, trained in the multi-branch method, respectively. because the last group of data is gathered from the images of which the noise intensity is higher than the signal. In general, the neural-network-based methods perform better than the traditional methods for different noise levels. Additionally, the proposed method is more accurate than other reference methods. Figure 6 shows that the accuracy of the traditional methods on the images with uniform land surfaces (Figure 1e,f) is higher than the complicated land surfaces. Thus, the traditional methods are applied on images with many uniform or homogeneous areas. For the denoising method DnCNN, the performance is general. The aim of denoising is to improve quality by refining details, enhancing contrast, etc. The residual noise contains part of the image information, which causes high noise estimation. Figure 7 shows the difference between the noise obtained by DnCNN and the true noise distribution. The red box in Figure 7a shows that the texture structure is left in the noisy image when compared with the true noise (shown in Figure 7b), which causes a large estimated variance of 0.04, while the true noise variance is 0.005. The methods based on convolutional neural network try to find the mapping relationship between the noise and the noisy signals. In the mapping relationship, several groups of weight parameters are trained to maximize the satisfaction of the results that conform to the true situation. The noise levels achieved by CNNs are closer to the true noise levels. Figure 8 shows the situation and displays the changing rates between the estimated and the true values.