## 3 Experimental Data
### The Introduction of Public Datasets  
In this study, four public datasets were used for experimental validation in this study, including MMCBNU_6000 [38], FV-USM [39], UTFVP [40], and SDUMA-FV [41], the details of which are shown in Table 1. We divide the dataset into training and testing datasets in a ratio of 8:2 to train and test the proposed network model. In particular, the 20% of the training dataset is divided into a validation dataset. We use method [42] to extract ROI from the images in the dataset. To prevent overfitting, we enhance the data by flipping, adding noise, mirroring, and offsetting the images of the input network. Before training, we standardize the size of the input images to \(256\times 256\). In this study, we classify the blurred images captured from finger veins into two general types of blurs: motion blur and Gaussian blur. Among them, Gaussian blur is mainly determined by the standard deviation \(\sigma\) and the convolution kernel, where the larger the standard deviation the greater the blur, and the larger the convolution kernel the greater the blur. The motion blur is generated by a random matrix of motion blur kernels. In this study we use the albumentations library [43] to add motion blur to the images in this public dataset. The albumentations library adds the motion blur operation with the note that the blur_limit is the range of the convolution kernel size, the larger the convolution kernel the more pronounced the blur effect. **Training dataset. ** To ensure that the network model can handle different blur kernels, the motion blur kernel, the standard deviation \(\sigma\) of the Gaussian blur and the convolution \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Datasets** & \begin{tabular}{c} **Number of** \\ **Subjects** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Image** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Fingers for** \\ **Each Subject** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Images for** \\ **Each Subject** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Images for** \\ **Each Subject** \\ \end{tabular} &
\begin{tabular}{c} **Image Size** \\ \end{tabular} \\ \hline MMCBNU\_6000 [38] & 100 & 6000 & 6 & 10 & 640 \(\times\) 480 pxd \\ FV-USM [39] & 123 & 2952 & 4 & 6 & 640 \(\times\) 480 pxd \\ UTFVP [40] & 60 & 1140 & 6 & 4 & 672 \(\times\) 380 pxd \\ SDUMA-FV [41] & 106 & 3816 & 6 & 6 & 320 \(\times\) 240 pxd \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of the experimental datasets. kernel of the Gaussian blur are all randomly generated for the public dataset, which is closer to the real case and improves the training effect and generalization ability of the model. When training, the data input to the network usually consists of several groups of images. Each set of images is a sharp image as the label and a blurred image corresponding to it as the input to the network. Finally, we input the processed data into the network for training to obtain the weights of the finger vein image restoration network with motion deblurring, and the weights of the finger vein image restoration network with Gaussian deblurring. **Testing dataset. ** To test the ability of the trained network to repair finger vein images, in this study, for Gaussian blur, the set of values of standard deviation \(\sigma\) is taken as \(\sigma\{0,4,8,12,16,20\}\) and the set of values of the size of the convolution kernel is taken as \(\{15,19,23,27,31\}\). The images of the test set are processed according to the values of the set as parameters for Gaussian blurring, for example, when \(\sigma=0\), the convolution kernel takes \(\{15,19,23,27,31\}\) to blur the images, respectively. For motion blur, the set of values of the motion blur kernel is taken as \(\{30,40,50,60,70,80,90\}\). By processing the data after randomly obtaining the blurring kernel parameters in the set, a test dataset of finger vein images with different degrees of motion blurring can be obtained.