Vehicle license plate recognition is a crucial task in intelligent traffic
management systems. However, the challenge of achieving accurate recognition
persists due to motion blur from fast-moving vehicles. Despite the widespread
use of image synthesis approaches in existing deblurring and recognition
algorithms, their effectiveness in real-world scenarios remains unproven. To
address this, we introduce the first large-scale license plate deblurring
dataset named License Plate Blur (LPBlur), captured by a dual-camera system and
processed through a post-processing pipeline to avoid misalignment issues.
Then, we propose a License Plate Deblurring Generative Adversarial Network
(LPDGAN) to tackle the license plate deblurring: 1) a Feature Fusion Module to
integrate multi-scale latent codes; 2) a Text Reconstruction Module to restore
structure through textual modality; 3) a Partition Discriminator Module to
enhance the model's perception of details in each letter. Extensive experiments
validate the reliability of the LPBlur dataset for both model training and
testing, showcasing that our proposed model outperforms other state-of-the-art
motion deblurring methods in realistic license plate deblurring scenarios. The
dataset and code are available at this https URL

教育部颁布的第 xxxx 号文件的要求：高校要强化学生思想政治教育，增强学生的理论素养，提高学生的科学素养等。

逼真车牌去模糊数据集和模型

A Dataset and Model for Realistic License Plate Deblurring

Motion deblurring is one of the fundamental problems of computer vision and
has received continuous attention. The variability in blur, both within and
across images, imposes limitations on non-blind deblurring techniques that rely
on estimating the blur kernel. As a response, blind motion deblurring has
emerged, aiming to restore clear and detailed images without prior knowledge of
the blur type, fueled by the advancements in deep learning methodologies.
Despite strides in this field, a comprehensive synthesis of recent progress in
deep learning-based blind motion deblurring is notably absent. This paper fills
that gap by providing an exhaustive overview of the role of deep learning in
blind motion deblurring, encompassing datasets, evaluation metrics, and methods
developed over the last six years. Specifically, we first introduce the types
of motion blur and the fundamental principles of deblurring. Next, we outline
the shortcomings of traditional non-blind deblurring algorithms, emphasizing
the advantages of employing deep learning techniques for deblurring tasks.
Following this, we categorize and summarize existing blind motion deblurring
methods based on different backbone networks, including convolutional neural
networks, generative adversarial networks, recurrent neural networks, and
Transformer networks. Subsequently, we elaborate not only on the fundamental
principles of these different categories but also provide a comprehensive
summary and comparison of their advantages and limitations. Qualitative and
quantitative experimental results conducted on four widely used datasets
further compare the performance of SOTA methods. Finally, an analysis of
present challenges and future pathways. All collected models, benchmark
datasets, source code links, and codes for evaluation have been made publicly
available at this https URL

本文综述了近六年来深度学习在盲运动去模糊中的应用，包括数据集、评估指标和方法的全面概述。通过对不同基本网络的分类和总结，详细探讨了它们的优点和局限性，并进行了最新方法的定性和定量实验结果比较。最后，针对现有挑战和未来路径进行了分析。