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

arXiv: https://arxiv.org/abs/1910.03876 · HTML: https://mlsanity.com/paper/1910.03876/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [Task-Driven Deep Image Enhancement Network for Autonomous Driving in Bad Weather](https://mlsanity.com/paper/2110.07206/) (2021)
- [Benchmarking Probabilistic Deep Learning Methods for License Plate Recognition](https://mlsanity.com/paper/2302.01427/) (2023)
- [Reading Between the Mud: A Challenging Motorcycle Racer Number Dataset](https://mlsanity.com/paper/2311.09256/) (2023)
- [Beyond the Mud: Datasets and Benchmarks for Computer Vision in Off-Road Racing](https://mlsanity.com/paper/2402.08025/) (2024)
- [Dendritic Convolution for Noise Image Recognition](https://mlsanity.com/paper/2511.18699/) (2025)

## Abstract (arXiv)

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.