%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2023/12.13.12.16
%2 sid.inpe.br/sibgrapi/2023/12.13.12.16.03
%T Super-Resolution Towards License Plate Recognition
%D 2023
%A Nascimento, Valfride,
%A Laroca, Rayson,
%A Menotti, David,
%@affiliation Universidade Federal do Paraná
%@affiliation Universidade Federal do Paraná
%@affiliation Universidade Federal do Paraná
%E Clua, Esteban Walter Gonzalez,
%E Körting, Thales Sehn,
%E Paulovich, Fernando Vieira,
%E Feris, Rogerio,
%B Conference on Graphics, Patterns and Images, 36 (SIBGRAPI)
%C Rio Grande, RS
%8 Nov. 06-09, 2023
%S Proceedings
%K PixelShuffle, Reconstruction, Super-Resolution.
%X Recent years have seen significant developments in license plate recognition through the integration of deep learning techniques and the increasing availability of training data. Nevertheless, reconstructing license plates from low-resolution surveillance footage remains challenging. To address this issue, we propose an attention-based super-resolution approach that incorporates sub-pixel convolution layers and an Optical Character Recognition (OCR)-based loss function. We trained the proposed architecture on synthetic images created by applying heavy Gaussian noise followed by bicubic downsampling to high-resolution license plate images. Our results show that the proposed approach for reconstructing these low-resolution images substantially outperforms existing methods in both quantitative and qualitative measures. Our source code is publicly available at https://github.com/valfride/lpr-rsr-ext/.
%@language en
%3 2023_SIBGRAPI_WTD_Valfride.pdf