## 1 Introduction
To solve the various blurs and distortions that exist in the captured images in real situations, we used generative adversarial networks for training. The GAN can generate images by finding the best filter using weights trained from the training data, and the trained network remains robust and generalizable. Moreover, to address the current restoration methods for damaged finger vein images (which suffer from loss of details after restoring the images), we propose a finger vein image restoration method based on vein feature texture guidance. This consists of two stages: (1) a finger vein texture feature restoration network, and (2) an original image restoration network. Specifically, the original image of the finger vein with damage is first subjected to feature extraction to obtain the texture feature image with damage. Then, the texture feature restoration network in the first stage is used to restore the damaged texture feature image, and the texture information of the damaged area can be predicted by the trained weights. Finally, the second stage of the original image restoration network is used to restore the defective original image using the restored texture image feature information as a guide. As a result, a significantly lower loss of vein feature detail in the restored original image would be achieved through feature guidance. In the process of our research, we have found that existing methods are only able to deal with a single finger vein image problem, but when multiple vision problems with the finger vein image (i.e., defects, motion blur, noise, etc. ), it is challenging to select the appropriate restoration method and proper order of image preprocessing steps for different image problem. For example, if the finger vein image is missing structural information and is blurred, as a rule of thumb, the ideal combination of restoration links is to restore the damage first and then to deblur the restored image, whereas if only the damage restoration method is used, the restored finger vein image will still be blurred; or if only the deblurring restoration method is used, the image quality of the finger vein will still be poor, due to the lack of detail. For finger vein images with both blur and noise, using restoration methods that enhance feature details can increase the degree of blur and noise in the image. This makes it difficult to subsequently extract clear vein feature information. If multiple restoration methods are used to restore an image simultaneously, it takes a significant amount of time to find the best combination of links by trial and error. Assuming that there are n restoration methods, there are _n!_ possible combinations of processing steps to generate a restored image, while there is only one optimal method that would effectively generate the high-quality restored image. Therefore, the fast and efficient selection of repair tasks and combinations to process low-quality finger vein images is an essential topic of research significance in this field. In this work, we hereby propose an adaptive selection and restoration method for finger vein images based on Deep Reinforcement Learning, referred to as DRL-FVRestore. We have divided the restoration tasks into three categories: image denoising and enhancement restoration, image deblurring restoration, and image inpainting restoration. We firstly propose to treat the finger vein restoration task as a sequential decision-making process and use the idea of deep reinforcement learning (DRL) to train an agent that can select the image restoration task adaptively according to the state of the finger vein image, to gradually restore and improve the quality of the finger vein image. Very recently, DRL has been used for some image processing applications. [PERSON] et al. [21] were the first to attempt to apply DRL to learn a strategy to gradually restore damaged images by selecting appropriate operations from a predefined toolbox.