## 4 Experiments and Analysis of Results
### Reliability Verification of the DRL-FVRestore  
In this study, we propose the DRL-FVRestore method to select the appropriate restoration task for image restoration processing based on the current state of the input image. By analyzing the results from the previous experiments, the effectiveness and reliability of the proposed DRL-FVRestore method for the restoration tasks of the motion deblurring restoration task, the Gaussian deblurring restoration task, and the damaged image restoration task were verified. The above restoration methods can effectively improve the quality of images and increase the accuracy of recognition when there is an image problem in the matched image. However, when the matched image is faced with complex, multi-image problems, merely using a single repair method is not effective in improving the performance of the system. In the case of image problems, if the system only relies on a \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **MMCBNU\_6000** & **SDUMA-FV** & **FV-USM** & **UTFVP** \\ \cline{2-5}
**Methods** & **PSNR** & **PSNR** & **PSNR** & **PSNR** \\ \hline Unrestored & 36.57 & 37.88 & 42.89 & 40.35 \\ CDD [13] & 39.25 & 39.22 & 48.76 & 46.89 \\ TV-model [12] & 39.11 & 39.98 & 49.01 & 47.25 \\ FMM [14] & 39.08 & 39.46 & 48.57 & 47.33 \\ GTC [1] & 40.11 & 42.34 & 50.66 & 48.99 \\ Proposed Method & **41.18** & **44.26** & **53.49** & **49.85** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: PSNR (dB) of various image inpainting methods on different public datasets. Figure 10: The ROC curves of various image inpainting methods on the different public datasets. trial-and-error approach to processing, without implementing a restoration task selected according to the image state, it requires a great deal of time and the restoration results are less than optimal. Figure 11: The intra-class and inter-class probability density distribution plots and feature point histogram statistical plots of various image restored methods on the UTFVP and FV-USM datasets. Figure 13 shows the output images of each stage of the DRL-FVRestore restoration. The first and second groups are images of defective finger veins, which were basically repaired by the first stage of restoration, and the repaired images have clear feature information. Figure 12: The intra-class and inter-class probability density distribution plots and feature point histogram statistical plots of various image restored methods on the MMCBNU and SDUMA-FV datasets. The third and fourth groups are images with motion blur, noise and other problems, and after each stage of denoising and deblurring, the image quality is improved and the vein detail is enhanced. Below, the images are the PSNR values for each image, and it can be seen that the quality of the output image has improved at each stage after the DRL-FVRestore restoration. The method proposed in this paper can effectively solve this problem. Based on the idea of reinforcement learning, the restoration task is selected by the trained agent. The agent is restored based on the state of the image. To verify the effectiveness and reliability of the DRL-FVRestore method in solving the multiple complex problems present in low-quality finger vein images, two image problems were randomly added to the test datasets. The ROC curves are plotted using the trained network for processing, as shown in Figure 14. When finger vein images have complex image problems, the restored finger vein images obtained by processing with the DRL-FVRestore method proposed in this paper are compared with those restored by just a single restoration method (Specifically motion deblurring restoration, Gaussian deblurring restoration, and damaged restoration). This reveals that the method in this paper has a lower EER value, a ROC curve closer to the coordinate axis and better recognition performance. The average EER value of the method in this paper was reduced by 3.98% on the four datasets compared to the single method.