摘要
车牌图像重建是实现智能交通的重要步骤.在经过不断的重复实验后,本文提出了一种新的基于生成对抗网络(GAN)的超分辨率车牌图像重建模型.所提出的办法主要包括4个部分:(1)预处理输入图像,包括调整图片大小和筛选对比度差的图片;(2)引入了残差密集网络,能够充分提取车牌图像特征;(3)引入渐进式采样进行图片重建,因其具有较大的感受野,能提供更多的信息细节;(4)引入基于PatchGAN的鉴别器模型,该模型能更加精准地判断,从而引导生成器进行更高质量、更多细节的图像重建.通过在CCPD数据集上与目前较优的算法进行比较,证明本文的模型重建的车牌图像具有较高的PSNR和SSIM,分别达到了26.80和0.77,而且重建单帧图像的花费时间更少,仅为0.06 s,进而证明了我们算法的可行性.
License plate image reconstruction plays an important role in the intelligent transportation system.After repeated experiments,a super-resolution image reconstruction method for license plates is proposed with the help of generative adversarial networks(GANs).The method mainly consists of four parts:(1)pretreatment of the input image,including image resizing and filtering of images with poor contrast;(2)image feature extraction using a residual dense network;(3)introduction of progressive sampling,which can provide a larger receptive field and more information details;(4)introduction of a discriminator based on PatchGAN to make a more accurate judgment,which guides the generator to reconstruct images with higher quality and more details.The comparison with a current superior algorithm on the Chinese City Parking Dataset(CCPD)proves that the proposed model has higher PSNR and SSIM(26.80 and 0.77,respectively)and less time of reconstructing a single-frame image(only 0.06 s),which verifies the feasibility of the proposed approach in license plate image reconstruction.
作者
刘良鑫
林勉芬
周成菊
潘家辉
LIU Liang-Xin;LIN Mian-Fen;ZHOU Cheng-Ju;PAN Jia-Hui(School of Software,South China Normal University,Foshan 528225,China)
出处
《计算机系统应用》
2022年第2期234-240,共7页
Computer Systems & Applications
基金
广州市科技计划项目重点领域研发计划(202007030005)
广东省自然科学基金面上项目(2019A1515011375)
国家自然科学基金面上项目(62076103)。
