中圖分類(lèi)號(hào)： TP394.1 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.223211 中文引用格式： 石冬陽(yáng)，張俊林，賈兵，等. 基于改進(jìn)暗通道先驗(yàn)的車(chē)牌圖像去霧方法研究[J].電子技術(shù)應(yīng)用，2022，48(11)：13-18. 英文引用格式： Shi Dongyang，Zhang Junlin，Jia Bing，et al. Research on defogging method of license plate image based on improved dark channel prior[J]. Application of Electronic Technique，2022，48(11)：13-18.
Research on defogging method of license plate image based on improved dark channel prior
Shi Dongyang1，Zhang Junlin1，Jia Bing1，Nie Ling1，Yang Huimin2
1.School of Electrical Engineering，Chongqing University of Science and Technology，Chongqing 401331，China； 2.School of Mathematics and Computing Science，Xiangtan University，Xiangtan 411105，China
Abstract： Aiming at the problem of poor recognition accuracy of license plate recognition system in haze scene, an improved license plate recognition model is proposed. The model uses the improved dark channel apriori defogging algorithm for defogging. Considering the color distortion and other problems when the original defogging algorithm processes the haze image with bright areas, firstly, the atmospheric light value is limited by the threshold value. Secondly, the introduction factor is optimized. And finally, the tolerance mechanism is introduced to correct the transmittance, and the image brightness is adjusted to improve the image visualization effect. The simulation results show that the performance of PSNR, SSIM, enterprise and e improved by 1.934 dB, 0.082, 0.235 and 38.995 respectively. The recognition test of the license plate image before and after defogging shows that the recognition accuracy of the license plate is improved by 22%, which proves the superiority of the proposed model.
Key words : license plate recognition；color distortion；threshold limit；introducing factors；tolerance mechanism
