SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition

Younkwan Lee, Juhyun Lee, Hoyeon Ahn, Moongu Jeon; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 0-0

Abstract


In this paper, we present an algorithm for real-world license plate recognition (LPR) from a low-quality image. Our method is built upon a framework that includes denoising and rectification, and each task is conducted by Convolutional Neural Networks. Existing denoising and rectification have been treated separately as a single network in previous research. In contrast to the previous work, we here propose an end-to-end trainable network for image recovery, Single Noisy Image DEnoising and Rectification (SNIDER), which focuses on solving both the problems jointly. It overcomes those obstacles by designing a novel network to address the denoising and rectification jointly. Moreover, we propose a way to leverage optimization with the auxiliary tasks for multi-task fitting and novel training losses. Extensive experiments on two challenging LPR datasets demonstrate the effectiveness of our proposed method in recovering the high-quality license plate image from the low-quality one and show that the the proposed method outperforms other state-of-the-art methods.

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[bibtex]
@InProceedings{Lee_2019_ICCV,
author = {Lee, Younkwan and Lee, Juhyun and Ahn, Hoyeon and Jeon, Moongu},
title = {SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition},
booktitle = {The IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2019}
}