Relevant bibliographies by topics / License plate deblurring

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Academic literature on the topic 'License plate deblurring'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "License plate deblurring"

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Et. al., Ria Ambrocio Sagum, MCS. "Incorporating Deblurring Techniques in Multiple Recognition of License Plates from Video Sequences." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (2021): 5447–52. http://dx.doi.org/10.17762/turcomat.v12i3.2194.

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Recognition of license plate is the process of wherein photographic video or images of license plates are being captured and then processed using an application that implements series of algorithms that will provide the alpha numeric conversion of the captured data. In this study, the researchers developed a license plate recognition that incorporates image deblurring to accommodate multiple recognition from video sequences. The approach uses Background Subtraction and Connected Component Analysis for the detection of license plates, Image deblurring to enhance the image and reduce the difficu
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Wijaya, Marvin Chandra. "Research of Indonesian license plates recognition on moving vehicles." EUREKA: Physics and Engineering, no. 6 (November 29, 2022): 185–98. https://doi.org/10.21303/2461-4262.2022.002424.

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The recognition of the characters in the license plate has been widely studied, but research to recognize the character of the license plate on a moving car is still rarely studied. License plate recognition on a moving car has several difficulties, for example capturing still images on moving images with non-blurred results. In addition, there are also several problems such as environmental disturbances (low lighting levels and heavy rain). In this study, a novel framework for recognizing license plate numbers is proposed that can overcome these problems. The proposed method in this study: de
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NERINOORU, DR SREEKANTH, T THARUNI, P SIRI, and R SRIJA. "THE LICENCE PLATE PROOF OF IDENTITY RECKLESS STIRRING VEHICLES." Journal of Nonlinear Analysis and Optimization 15, no. 02 (2024): 173–78. https://doi.org/10.36893/jnao.2024.v15i12.047.

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This study introduces a novel approach aimed at improving Automatic License Plate Recognition (ALPR) systems, addressing the common issue of poor-quality license plate images. The primary goal of this approach is to enhance the performance of ALPR systems, particularly in real-world environments where license plate images often suffer from blurring, distortion, or poor lighting conditions. In such scenarios, even the most advanced ALPR algorithms can struggle to correctly identify the characters and numbers on a license plate. The proposed system integrates sophisticated image deblurring techn
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Wijaya, Marvin Chandra. "Research of Indonesian license plates recognition on moving vehicles." EUREKA: Physics and Engineering, no. 6 (November 29, 2022): 185–98. http://dx.doi.org/10.21303/2461-4262.2022.002424.

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The recognition of the characters in the license plate has been widely studied, but research to recognize the character of the license plate on a moving car is still rarely studied. License plate recognition on a moving car has several difficulties, for example capturing still images on moving images with non-blurred results. In addition, there are also several problems such as environmental disturbances (low lighting levels and heavy rain). In this study, a novel framework for recognizing license plate numbers is proposed that can overcome these problems. The proposed method in this study: de
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Namrata, S. Bolaj*1 Prof.G.R.Padalkar2. "A SURVEY ON LICENSICLESE PLATE DEBLURRING OF FAST MOVING VEHICLES." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 6 (2017): 348–51. https://doi.org/10.5281/zenodo.809215.

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Vehicle license plate recognition (LPR) is one of the important fields in Intelligent Transportation Systems (ITS). LPR systems aim to locate, segment and recognize the license plate from captured car image. As the remarkable recognizable proof of a vehicle, license plate is a key piece of information to reveal over-speed vehicles or the ones included in attempt at manslaughter hit-and-run accidents. Be that as it may, the preview of over-speed vehicle caught by reconnaissance camera is every now and again obscured because of quick movement, which is even unrecognizable by human. Those watched
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Zhao, Chenping, Yingjun Wang, Hongwei Jiao, Jingben Yin, and Xuezhi Li. "$L_p$ -Norm-Based Sparse Regularization Model for License Plate Deblurring." IEEE Access 8 (2020): 22072–81. http://dx.doi.org/10.1109/access.2020.2969675.

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Hijji, Mohammad, Abbas Khan, Mohammed M. Alwakeel, et al. "Intelligent Image Super-Resolution for Vehicle License Plate in Surveillance Applications." Mathematics 11, no. 4 (2023): 892. http://dx.doi.org/10.3390/math11040892.

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Vehicle license plate images are often low resolution and blurry because of the large distance and relative motion between the vision sensor and vehicle, making license plate identification arduous. The extensive use of expensive, high-quality vision sensors is uneconomical in most cases; thus, images are initially captured and then translated from low resolution to high resolution. For this purpose, several traditional techniques such as bilinear, bicubic, super-resolution convolutional neural network, and super-resolution generative adversarial network (SRGAN) have been developed over time t
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Kim, Dogun, Jin Kim, and Eunil Park. "AFA-Net: Adaptive Feature Attention Network in image deblurring and super-resolution for improving license plate recognition." Computer Vision and Image Understanding 238 (January 2024): 103879. http://dx.doi.org/10.1016/j.cviu.2023.103879.

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Vimal, Vrince. "Mixture of Gaussian Blur Kernel Representation for Blind Image Restoration." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, no. 1 (2019): 589–95. http://dx.doi.org/10.17762/turcomat.v10i1.13553.

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The use of blind image restoration, sharpness of edges may frequently be restored using previous information from a picture. De-blurring is the technique of taking out blurring flaws of the steady photographs, including motion or defocus aberration-related blur. the appearance of fast-moving the appearance of fast-moving entities flashing in still images flashing in a still photograph is known as motion blur. When an image is blurred using a Gaussian function, the result is a Gaussian blur. The employment of different sparse priors, either for the implicit photos or the motion blur kernels, co
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Ding, Fang, and Daisheng Zhang. "Spatial correction and Deblurring fusion algorithm for vehicle license plate images based on deep learning." International Journal of Modeling, Simulation, and Scientific Computing, September 11, 2024. http://dx.doi.org/10.1142/s1793962324500454.

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Image correction and deblurring have always posed significant challenges in the field of image processing, and the results of license plate image recognition with low structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) will significantly affect the use of vehicles. To address these problems, we propose a real-time algorithm based on deep learning, which can simultaneously perform image spatial correction and deblurring in the same network, known as [Formula: see text]. First, a 15-layer depthwise separable convolutional neural network is designed as the basic netwo
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Conference papers on the topic "License plate deblurring"

1

Gong, Haoyan, Yuzheng Feng, Zhenrong Zhang, et al. "A Dataset and Model for Realistic License Plate Deblurring." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/86.

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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
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Svoboda, Pavel, Michal Hradis, Lukas Marsik, and Pavel Zemcik. "CNN for license plate motion deblurring." In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. http://dx.doi.org/10.1109/icip.2016.7533077.

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Nguyen, Van-Giang, and Duy Long Nguyen. "Joint Image Deblurring and Binarization for License Plate Images using Deep Generative Adversarial Networks." In 2018 5th NAFOSTED Conference on Information and Computer Science (NICS). IEEE, 2018. http://dx.doi.org/10.1109/nics.2018.8606802.

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Fang, Jin, Yule Yuan, Wei Ji, Peijun Tang, and Yong Zhao. "Licence plate images deblurring with binarization threshold." In 2015 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE, 2015. http://dx.doi.org/10.1109/ist.2015.7294571.

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