Universiti Teknologi Malaysia Institutional Repository

Super resolution of car plate images using generative adversarial networks

Tan, K. L. and Abbas, A. F. and Abdu, A. M. and Sheikh, U. U. and Mokji, M. and Khalil, K. (2019) Super resolution of car plate images using generative adversarial networks. In: 15th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2019, 8-9 March 2019, Parkroyal Penang Resort Penang, Malaysia.


Official URL: http://www.dx.doi.org/10.1109/CSPA.2019.8696010


Car plate recognition is used in traffic monitoring and control systems such as intelligent parking lot management, finding stolen vehicles, and automated highway toll. In practice, Low-Resolution (LR) images or videos are widely used in surveillance systems. In low resolution surveillance systems, the car plate text is often illegible. Super-Resolution (SR) techniques can be used to improve the car plate quality by processing a series of LR images into a single High-Resolution (HR) image. Recovering the HR image from a single LR is still an ill-conditioned problem for SR. Previous methods always minimize the mean square loss in order to improve the peak signal to noise ratio (PSNR). However, minimizing the mean square loss leads to overly smoothed reconstructed image. In this paper, Generative Adversarial Networks (GANs) based SR is proposed to reconstruct the LR images into HR images. Besides that, perceptual loss is proposed to solve the smoothing issue. The quality of the GAN based SR generated images is compared to existing techniques such as bicubic, nearest and Super-Resolution Convolution Neural Network (SRCNN). The results show that the reconstructed images using GANs based SR achieve better results in term of perceptual quality compared to previous methods.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:car plate, generative adversarial networks, super resolution
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:89637
Deposited By: Narimah Nawil
Deposited On:09 Feb 2021 16:25
Last Modified:09 Feb 2021 16:25

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