An Encoder-Decoder-Based Super Resolution Network for License Plate Images

An encoder-decoder-based super resolution network for license plate images was proposed for the lack of key information on license plate images caused by blur, stain, damage, distortion, and tilt in complex actual scenes and for the recognition difficulty due to low contrast between the license plate background and characters of new-energy vehicles. Firstly, a license plate reconstruction generator network based on the encoder-decoder structure is constructed. The texture and characters of the license plate image are extracted by an encoder, and the license plate features are reconstructed by a decoder. Then, a discriminator network based on semantic supervision is designed, and the adversarial loss and CTC loss are introduced into the network loss to enhance the ability of the generator network to represent the semantic features of license plate images. Finally, the features of the vertex points of the license plate are extracted based on VGG16 network, and a coordinate transformation method is utilized to correct the license plate image and further improve the reconstruction quality and recognition accuracy. Super-resolution reconstruction and recognition tests were performed on the self-built XAUAT-Parking dataset and the public CCPD dataset with the proposed network. The test results showed that the proposed network has an average peak signal to noise ratio(PSNR) of 25.5 dB and a structural similarity(SSIM) of 0.989 on the CCPD dataset. The PSNR and SSIM on the XAUAT-Parking dataset can reach 26.6 dB and 0.997 respectively. According to the research results, the proposed network has a good super-resolution reconstruction effect of license plate images and a strong robustness to the missing of key license plate information. © 2022 Xi'an Jiaotong University. All rights reserved.