A dataset and model for realistic license plate deblurring
AUTHORs: 
Haoyan Gong, Yuzheng Feng, Zhenrong Zhang, Xianxu Hou, Jingxin Liu, Siqi Huang, Hongbin LiuAuthors Info & Claims
Haoyan Gong, Yuzheng Feng, Zhenrong Zhang, Xianxu Hou, Jingxin Liu, Siqi Huang, Hongbin LiuAuthors Info & ClaimsIJCAI '24: Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Article No.: 86, Pages 776 - 784
Abstract
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 (LP-Blur), 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 https://github.com/haoyGONG/LPDGAN.
References
[1]
Meng Chang, Chenwei Yang, Huajun Feng, Zhihai Xu, and Qi Li. Beyond camera motion blur removing: How to handle outliers in deblurring. IEEE Transactions on Computational Imaging, 7:463- 474, 2021.
[2]
Sunghyun Cho and Seungyong Lee. Fast motion deblurring. In ACM SIGGRAPH Asia 2009 papers, pages 1-8. 2009.
[3]
Sung-Jin Cho, Seo-Won Ji, Jun-Pyo Hong, Seung-Won Jung, and Sung-Jea Ko. Rethinking coarse-to-fine approach in single image deblurring. In Proceedings of the IEEE/CVF international conference on computer vision, pages 4641-4650, 2021.
[4]
Georgios D Evangelidis and Emmanouil Z Psarakis. Parametric image alignment using enhanced correlation coefficient maximization. IEEE transactions on pattern analysis and machine intelligence, 30(10):1858-1865, 2008.
[5]
Rob Fergus, Barun Singh, Aaron Hertzmann, Sam T Roweis, and William T Freeman. Removing camera shake from a single photograph. In Acm Siggraph 2006 Papers, pages 787-794. 2006.
[6]
Dong Gong, Jie Yang, Lingqiao Liu, Yanning Zhang, Ian Reid, Chunhua Shen, Anton Van Den Hengel, and Qinfeng Shi. From motion blur to motion flow: A deep learning solution for removing heterogeneous motion blur. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2319- 2328, 2017.
[7]
Tae Hyun Kim, Byeongjoo Ahn, and Kyoung Mu Lee. Dynamic scene deblurring. In Proceedings of the IEEE international conference on computer vision, pages 3160-3167, 2013.
[8]
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1125-1134, 2017.
[9]
Seo-Won Ji, Jeongmin Lee, Seung-Wook Kim, Jun-Pyo Hong, Seung-Jin Baek, Seung-Won Jung, and Sung-Jea Ko. Xydeblur: divide and conquer for single image deblurring. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17421-17430, 2022.
[10]
Zhe Jiang, Yu Zhang, Dongqing Zou, Jimmy Ren, Jiancheng Lv, and Yebin Liu. Learning event-based motion deblurring. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3320-3329, 2020.
[11]
Glenn Jocher. YOLOv5 by Ultralytics, May 2020.
[12]
Animesh Karnewar and Oliver Wang. Msg-gan: Multi-scale gradients for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7799-7808, 2020.
[13]
Kiyeon Kim, Seungyong Lee, and Sunghyun Cho. Mssnet: Multi-scale-stage network for single image deblurring. In European Conference on Computer Vision, pages 524-539. Springer, 2022.
[14]
Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
[15]
Rolf Köhler, Michael Hirsch, Betty Mohler, Bernhard Schölkopf, and Stefan Harmeling. Recording and playback of camera shake: Benchmarking blind deconvolution with a real-world database. In Computer Vision-ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part VII 12, pages 27-40. Springer, 2012.
[16]
Orest Kupyn, Volodymyr Budzan, Mykola Mykhailych, Dmytro Mishkin, and Jiří Matas. Deblurgan: Blind motion deblurring using conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8183-8192, 2018.
[17]
Orest Kupyn, Tetiana Martyniuk, Junru Wu, and Zhangyang Wang. Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better. In Proceedings of the IEEE/CVF international conference on computer vision, pages 8878-8887, 2019.
[18]
Wei-Sheng Lai, Jia-Bin Huang, Zhe Hu, Narendra Ahuja, and Ming-Hsuan Yang. A comparative study for single image blind deblurring. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1701-1709, 2016.
[19]
Vladimir I Levenshtein et al. Binary codes capable of correcting deletions, insertions, and reversals. In Soviet physics doklady, volume 10, pages 707-710. Soviet Union, 1966.
[20]
Anat Levin, Yair Weiss, Fredo Durand, and William T Freeman. Understanding and evaluating blind deconvolution algorithms. In 2009 IEEE conference on computer vision and pattern recognition, pages 1964- 1971. IEEE, 2009.
[21]
Haoying Li, Ziran Zhang, Tingting Jiang, Peng Luo, Huajun Feng, and Zhihai Xu. Real-world deep local motion deblurring. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 1314- 1322, 2023.
[22]
Wei Liu, Qiong Yan, and Yuzhi Zhao. Densely self-guided wavelet network for image denoising. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 432- 433, 2020.
[23]
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012- 10022, 2021.
[24]
Seungjun Nah, Tae Hyun Kim, and Kyoung Mu Lee. Deep multi-scale convolutional neural network for dynamic scene deblurring. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3883-3891, 2017.
[25]
Mehdi Noroozi, Paramanand Chandramouli, and Paolo Favaro. Motion deblurring in the wild. In Pattern Recognition: 39th German Conference, GCPR 2017, Basel, Switzerland, September 12-15, 2017, Proceedings 39, pages 65-77. Springer, 2017.
[26]
Sainandan Ramakrishnan, Shubham Pachori, Aalok Gangopadhyay, and Shanmuganathan Raman. Deep generative filter for motion deblurring. In Proceedings of the IEEE international conference on computer vision workshops, pages 2993-3000, 2017.
[27]
Dongwei Ren, Wangmeng Zuo, David Zhang, Jun Xu, and Lei Zhang. Partial deconvolution with inaccurate blur kernel. IEEE transactions on image processing, 27(1):511-524, 2017.
[28]
Wenqi Ren, Jiawei Zhang, Lin Ma, Jinshan Pan, Xiaochun Cao, Wangmeng Zuo, Wei Liu, and Ming-Hsuan Yang. Deep non-blind deconvolution via generalized low-rank approximation. Advances in neural information processing systems, 31, 2018.
[29]
Jaesung Rim, Haeyun Lee, Jucheol Won, and Sunghyun Cho. Real-world blur dataset for learning and benchmarking deblurring algorithms. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXV 16, pages 184-201. Springer, 2020.
[30]
Qi Shan, Jiaya Jia, and Aseem Agarwala. High-quality motion deblurring from a single image. Acm transactions on graphics (tog), 27(3):1-10, 2008.
[31]
Ziyi Shen, Wenguan Wang, Xiankai Lu, Jianbing Shen, Haibin Ling, Tingfa Xu, and Ling Shao. Human-aware motion deblurring. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5572-5581, 2019.
[32]
Baoguang Shi, Xiang Bai, and Cong Yao. An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE transactions on pattern analysis and machine intelligence, 39(11):2298-2304, 2016.
[33]
Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
[34]
Libin Sun, Sunghyun Cho, Jue Wang, and James Hays. Edge-based blur kernel estimation using patch priors. In IEEE international conference on computational photography (ICCP), pages 1-8. IEEE, 2013.
[35]
Jian Sun, Wenfei Cao, Zongben Xu, and Jean Ponce. Learning a convolutional neural network for non-uniform motion blur removal. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 769-777, 2015.
[36]
Xin Tao, Hongyun Gao, Xiaoyong Shen, Jue Wang, and Jiaya Jia. Scale-recurrent network for deep image deblurring. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8174- 8182, 2018.
[37]
Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. Recovering realistic texture in image super-resolution by deep spatial feature transform. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 606-615, 2018.
[38]
Oliver Whyte, Josef Sivic, Andrew Zisserman, and Jean Ponce. Non-uniform deblurring for shaken images. International journal of computer vision, 98:168-186, 2012.
[39]
Zhenbo Xu, Wei Yang, Ajin Meng, Nanxue Lu, Huan Huang, Changchun Ying, and Liusheng Huang. Towards end-to-end license plate detection and recognition: A large dataset and baseline. In Proceedings of the European conference on computer vision (ECCV), pages 255-271, 2018.
[40]
Xiang Zhang, Lei Yu, Wen Yang, Jianzhuang Liu, and Gui-Song Xia. Generalizing event-based motion deblurring in real-world scenarios. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10734-10744, 2023.
[41]
Suiyi Zhao, Zhao Zhang, Richang Hong, Mingliang Xu, Yi Yang, and Meng Wang. Fcl-gan: A lightweight and real-time baseline for unsupervised blind image deblurring. In Proceedings of the 30th ACM International Conference on Multimedia, pages 6220-6229, 2022.
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Published: 03 August 2024
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