Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (6A): 250600070-5.doi: 10.11896/jsjkx.250600070

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

SeguGAN:Research on Super-resolution Reconstruction of License Plate Images UtilizingGenerative Adversarial Networks

HUANG Haixin, HOU Guangshuai, HE Tianyu   

  1. School of Automation and Electrical Engineering,Shenyang Ligong University,Shenyang 110159,China
  • Online:2026-06-16 Published:2026-06-12
  • About author:HUANG Haixin,born in 1973,Ph.D,associate professor.Her main research interests include machine learning,artificial intelligence and intelligent grid.
  • Supported by:
    National Key R&D Program of China(2022YFC3302500).

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Abstract: In intelligent transportation systems,super-resolution(SR) reconstruction of license plate images is crucial due to common issues like poor lighting,motion blur,and low resolution in surveillance footage.Existing SR methods often produce artifacts and lose high-frequency details,leading to blurred outputs.To tackle these problems,this paper proposes SeguGAN—a novel framework that integrates semantic cues using CLIP-based features via a semantic-aware module(SeMSCA) in the discriminator.The generator employs a gating mechanism to merge multi-branch features(from RRDB and CAMConv),enhancing reconstruction quality.It also introduces Dynamic Tanh(DyT) to replace layer normalization,simplifying the architecture while improving performance.Evaluated on the CCPD2019、CCPD2020dataset,SeguGAN achieves 33.20 dB PSNR and 0.906 SSIM,outperforming ESRGAN,SRGAN,ECBSR,RCAN,and SwinIR by 5.9% in PSNR and 1.9% in SSIM on average.The results confirm its effectiveness in license plate SR reconstruction.

Key words: Super-resolution, Semantic information, Generative adversarial network, License plate image, Computer vision

CLC Number: 

  • TP183
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