## 1 Introduction  
With the growing concern for information security, biometric technology has been recognized as one of the most reliable security technologies in the field of information security [1]. Compared to other biometric technologies, finger vein recognition has unique advantages, such as low cost, non-contact acquisition, and uniqueness of finger vein information, especially in the current world epidemic situation, its contactless collection method has been widely accepted [2]. In practice, however, the finger vein images are often blurred, noisy, or have missing features [3]. For example, unstable light sources and image sensors will increase blurring and noise of the finger vein images (Figure 1a,b), the temperature of the acquisition environment can cause expansion or contraction of the vein, and oil and dust on the filter of the acquisition device can cause the vein features to be missing (Figure 1c,d). These images are known as low-quality finger vein images. These images will increase the difficulty of finger vein feature extraction and reduce the recognition performance. Hence, the selection of appropriate enhancement and effective restoration methods to enhance and improve the resolution of low-quality images is clearly essential and meaningful. In this paper, low-quality finger vein image restoration tasksare classified into three types: 1. restoration of finger vein images with motions blur and Gaussian blur; 2. restoration of damaged finger vein images with oil and water stains; and 3. restoration of enhanced finger vein images with noise and texture blur. In the field of image deblurring, the current methods for finger vein image deblurring restoration are divided into traditional deblurring algorithms and deblurring algorithms based on deep learning. The typical traditional method [4; 5] requires the use of a blur kernel to perform deconvolution to restore a clear finger vein image, provided that the blur kernel is known. However, the current deblurring approaches are mostly impractical because of several reasons. First, most blur kernels are unknown in the real environment as estimating blur kernels is very time consuming. Second, even if the convolution kernel is known, different acquisition devices have variations in their blur kernels due to environmental, physical, and other factors, and the processed images still result in degraded recognition performance. Several improved algorithms have been proposed to deblur the finger veins based on skin scattering or optical blurring. [PERSON] et al. [6] proposed a blurred finger vein image restoration method to restore blurred finger vein images and improve recognition performance by considering optical blurring components and scattering blur  
Figure 1: Low-quality finger vein images and images after feature extraction. ring components. [PERSON] et al. [6; 7] devised a deblurring optical model by calculating the light scattering component of an organism. However, the traditional approach requires the measurement of multiple parameters to improve the performance of the model, resulting in a large amount of undesired processing time. The recent deblurring algorithms based on deep learning have been widely used to restore clear images by predicting the blur kernel, ref. [8] used generative adversarial networks (GAN) [9] to achieve image deblurring. GAN provides an adversarial game idea of generators and discriminators, and a better deblurring effect can be obtained by inputting the blurred image into the trained generative network. [PERSON] et al. [10] proposed a modified correction-based GAN for restoring the optical blur conditions contained in the original finger vein images; ref. [3] improved the DeblurGAN network [11] and proposed a method that can restore finger vein images with motion blur. GAN provides an adversarial game idea of generators and discriminators and can deliver a better deblurring effect by inputting the blurred image.