## 3 Experimental Results and Analysis
### Robustness to Illumination, Color and Brightness
Figures 3-5 further give robustness analysis of the three learning-based image registration algorithms under three different CNN models. The used three CNN models are VGG, Googlenet and Xception, respectively. It can be seen that under the same neural network model, the robustness of [PERSON]'s algorithm is inferior to the other two algorithms. [PERSON]'s algorithm uses L1 norm as a loss function in the unsupervised learning algorithm, requiring the same image augmentation parameters for \(I^{\prime}{}_{A}\) and \(I^{\prime}{}_{B}\) in each pair of samples during the training, otherwise, the model will not converge normally, which results in the poor robustness of the unsupervised learning image registration algorithm. In contrast, [PERSON]'s algorithm and the proposed algorithm do not have this problem, because both of them adopt supervised learning; the label value can supervise the training of the neural network very well, so the model has better robustness. Figure 3: Robustness of [PERSON]’s algorithm, [PERSON]’s algorithm and the proposed algorithm using VGG: (**a**) Failure rate; (**b**) RMSE. Figure 2: Robustness of seven image registration algorithms under different image augmentation degrees: (**a**) Failure rate; (**b**) RMSE. In order to further analyze the influence of different perturbation values on the accuracy of the proposed algorithm, four maximum perturbation values in Step 1 including 24, 28, 32, and 36 are tested on test sets with different image augmentation degrees, respectively. The experimental results are shown in Figure 6, in which the abscissa and ordinate are the image augmentation degree of the test set and RMSE achieved by different image registration algorithms, respectively. It can be seen that as the maximum perturbation value \(\rho\) decreases, the RMSE of image registration also decreases, that is, the higher the accuracy of image registration. Figure 4: Robustness of [PERSON]’s algorithm, [PERSON]’s algorithm and the proposed algorithm using Googlenet: (**a**) Failure rate; (**b**) RMSE. Figure 5: Robustness of [PERSON]’s algorithm, [PERSON]’s algorithm and the proposed algorithm using Xception: (**a**) Failure rate; (**b**) RMSE. Figure 7 gives the visualized homography estimation results. The red boxes in the left images are mapped to the red boxes in the right images. These red boxes are labels, which are generated by the proposed method described in Section 2.3. The yellow boxes in the right images indicate the results of homography estimation. The more the red and yellow boxes in the right images coincide, the higher the accuracy of feature point matching is. From Figure 7, it is also noticed that the proposed algorithm with Xception model is superior to the proposed algorithms with Googlenet and VGG neural network models. Figure 6: Robustness of the proposed algorithm under different perturbation values and CNNs: (**a**) \(\rho\) = 36; (**b**) \(\rho\) = 32; (**c**) \(\rho\) = 28; (**d**) \(\rho\) = 24. Figure 7: Visualization analysis of the proposed algorithm under different CNNs (The red boxes indicate the ground truth, and the yellow boxes are the estimation results): (**a**) accuracy of image registration under VGG (RMSE = 10.154711); (**b**) accuracy of image registration under VGG (RMSE = 2.240815); (**c**) accuracy of image registration under Googlenet (RMSE = 7.2284245); (**d**) accuracy of image registration under Googlenet (RMSE = 1.9681364); (**e**) accuracy of image registration under Xception (RMSE = 3.1798978); (**f**) accuracy of image registration under Xception (RMSE = 1.4085304).