Semantic Super-resolution for Extremely Low-resolution Vehicle License Plate
Yuexian Zou, Yi Wang, Wenjie Guan, Wenwu Wang
Abstract
Vehicle license plate (VLP) super-resolution (SR) is of great demand in intelligent traffic systems. Super-Resolution for extremely low-resolution VLP remains challenging and the state-of-the-art SR methods hardly provide satisfying results for low-resolution (LR) VLPs. In this study, from a new perspective, we develop an effective solution to achieve the super-resolution of the extremely LR VLP images, by using the semantic information of the characters. Specifically, we firstly exploit the pervasive sparse prior for the character recognition in LR condition for VLPs. Then the semantic information extracted from the sparse representation-based classification (SRC) results is employed to alleviate the illness of the SR problem. To maximize the benefit brought by the semantic information from SRC, we employ sparse-coding based super-resolution (SCSR) method to upscale VLP images. In the end, an exponential soft labeling method is designed to reduce the possible bias introduced by character classification. Extensive experiments on the self-built Chinese VLP dataset (VLP100) and public UFPR-ALPR dataset validate the feasibility and effectiveness of our proposed VLP-SR system.
BibTeX
@inproceedings{icassp2019_semanticsuperres,
title = {Semantic Super-resolution for Extremely Low-resolution Vehicle License Plate},
author = {Yuexian Zou and Yi Wang and Wenjie Guan and Wenwu Wang},
booktitle = {ICASSP 2019},
year = {2019}
}