## 4 Experiments and Analysis of Results  
In this section, we design the following experiments to verify the effectiveness of our proposed method. We divide the experimental part into the reliability validation for the motion blur restoration task, Gaussian blur restoration task, image damage restoration task, and DRL-FVRestore. We test our proposed method on four public datasets. In this paper, peak signal-to-Noise ratio (PSNR) and structural similarity (SSIM) [45] are adopted as quality evaluation metrics to compare different algorithms for restoration tasks. PSNR and SSIM have been widely used in image quality evaluation. The larger the PSNR and SSIM values, the better the image quality.  
The existing finger vein image matching algorithms can be broadly divided into texture feature based matching algorithms and feature point based matching algorithms. Image matching can be used to evaluate the performance improvement of the restored finger vein feature texture image. In order to verify the reliability of the methods, in this experiment we use both methods to simultaneously evaluate the performance improvement of the repaired images by the proposed method. The matching algorithm we adopt is described in detail below.  
1. **Matching algorithm based on texture feature extraction**. In this study, a single finger in each dataset was considered as a single individual. The template used for matching was the image fusion of all ROI using a single finger. We used the template matching method for recognition matching. As an example, for the template matching method, we use the MMCBNU_6000 (6000/10) dataset. The template images were first obtained by fusing 10 images of the same category using ROI images and then the maximum curvature method [28] was used to obtain vein feature images. Matching each original feature image with the same class of feature images of the template, i.e., 6000 times for intra-class matching, and \(6000\times 99=594,000\) times for inter-class matching. False acceptance rate (FAR) and false rejection rate (FRR) were used to assess the performance of the matches, which are two important metrics in the field of finger vein recognition. FRR is the probability that an authorized object is falsely rejected, and FAR is the probability that a non-authorized object is accepted as an authorized one [3]. Different matching thresholds give different FAR and FRR, while the equal error rate (EER) is the value when the FAR and FRR are equivalent. Draw the Receiver Operating Characteristic (ROC) curve according to FAR and FRR. n the ROC curve, that illustrates FAR against FRR at different thresholds on the matching score, the lower the ROC curve is located, the better the image quality is. Each part of the experiment is described in detail below.
2. **Matching algorithms based on feature points**. We use the SURF algorithm [46] for feature point extraction of finger vein images, SURF is a local feature point description algorithm with accelerated robustness, the main process of its algorithm is: (1) construct Hessian matrix; (2) construct Gaussian pyramid scale space; (3) use non-extreme value suppression of initially determined feature points to precisely locate the extreme value points; (4) calculate the feature points in a certain domain of horizontal Haar wavelet features and vertical Haar wavelet features to determine the 64-dimensional feature vector; and (5) finally, Euclidean distance is used for feature point matching to achieve feature point matching of finger vein images. A higher number of feature points for intra-class matching and a lower number of feature points for inter-class matching indicates a better-quality image with richer vein texture information. In this experiment, we use a comparison of the intra-class and inter-class probability density distribution plots and feature point histogram statistical plots of different algorithms to analyze the reliability of the algorithms in this paper.