## 1 Introduction
However, the frequent use of down-sampling techniques in the feature extraction process will essentially result in the loss of crucial vein texture information, especially for finger vein images with blurred texture features, making it difficult to improve the deblurring image effect. In the field of image restoration for damaged regions, there are currently two main types of methods for restoring damaged areas of finger vein images: traditional methods and deep learning-based methods. The representative algorithms among traditional approaches are the total variation (TV) algorithm [12] and the curvature driven diffusions (CDD) algorithm [13]. However, TV and CDD algorithms do not take the texture constraints into account, leading to inaccurate restored finger vein images that lack texture information and are easily distorted. The work [14] proposed a fast-marching method (FMM) that prioritizes the restored by estimating parameters, such as light direction, and then fills the damaged area using a weighted average of neighboring pixels. FMM improves time efficiency but blindly uses all known information in the patch for the weighting calculation, resulting in vein textures that do not consider texture constraints and do not accurately characterize the texture. [PERSON] et al. [1] proposed a restoration algorithm based on Gabor texture feature constraints. During the restoration process, the information of the neighboring blocks is selectively weighted according to the features of similar textures in the local region, making the vein texture of the restored image more coherent. However, although these methods are suitable for cases where the vein structure information is unclear or missing due to image blurring, they are less effective for cases where the vein image itself is broken or missing due to external factors, and do not achieve better generalization capabilities. Recently, deep learning approaches have emerged as a promising technology for image inpainting [15]. Deep learning-based methods are mainly based on convolutional modules. This is because convolutional neural networks (CNN) can learn the depth features of an image and have better generalization capabilities. The deep learning-based methods have demonstrated promising solutions to restore the missing parts of an image. [PERSON] et al. [16] proposed a deep learning-based algorithm for restoring damaged areas of finger veins, but the algorithm did not make full use of texture information in the restoration process, resulting in texture blurring in the restored images, which presented low performance in matching authentication. With this problem in mind, [PERSON] et al. [17] proposed a finger vein restoration algorithm based on neighbor binary Wasserstein generative adversarial networks (NB-WGAN). The method uses texture loss as part of the loss function of the generator to recover more vein texture details. However, the method only constrains the network on the loss and still does not allow the network to adequately learn the finger vein texture features and guide the repair of the defective image based on the vein features. The method still loses detail in the repaired finger vein images. In this study, we propose a novel design of an effective and reliable restored method for damaged finger vein images and the design of finger vein image deblurring methods to address the critical challenges in the field of finger vein image restoration and enhancement. We have designed a DeblurGAN-v2 network based on the Inception-Resnet-v2 [18] backbone to address the problems of existing finger vein deblurring methods. DeblurGAN-v2 [19] is an improvement on DeblurGAN, and incorporates the feature pyramid network (FPN) [20], to achieve faster and better performance in learning the feature information of finger veins and reducing the loss of vein texture information. Furthermore, in the process of feature extraction, we have adopted inception-Resnet-v2 as the skeleton to collaborate with FPN to form a generative network.