Super-resolution of license plate images using attention modules and sub-pixel convolution layers

Nascimento, Valfride; Laroca, Rayson; Lambert, Jorge de A.; Schwartz, William Robson; Menotti, David

doi:10.1016/j.cag.2023.05.005

Cited by 14 publications

(2 citation statements)

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“…• A preliminary version of the proposed method was published at the 2022 Conference on Graphics, Patterns and Images (SIBGRAPI) [39]; • The proposed super-resolution method was recently published in the Computers & Graphics journal [48].…”

Section: Discussionmentioning

confidence: 99%

Super-Resolution Towards License Plate Recognition

Nascimento,

Laroca,

Menotti

2023

Anais Estendidos Da XXXVI Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2023)

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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/.

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Section: Discussionmentioning

confidence: 99%

Super-Resolution Towards License Plate Recognition

Nascimento,

Laroca,

Menotti

2023

Anais Estendidos Da XXXVI Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2023)

View full text Add to dashboard Cite

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“…The problem has been effectively mitigated since a single-image super-resolution method based on a deep learning approach was proposed [1]. High-frequency information can be well preserved by introducing deep learning methods, which are widely used in nature images, medical images, satellite aerial images, military fields, and robotics [2][3][4][5]. This is also true in the field of geophysics [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

OmniSR-M: A Rock Sheet with a Multi-Branch Structure Image Super-Resolution Lightweight Method

Liu,

Xu,

Tang

et al. 2024

Applied Sciences

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With the rapid development of digital core technology, the acquisition of high-resolution rock thin section images has become crucial. Due to the limitation of optical principles, thin section imaging involves a contradiction between resolution and field of view. In order to solve this problem, this paper proposes a lightweight, fully aggregated network with multi-branch structure for super resolution of rock thin section images. The experimental results on the rock thin section dataset demonstrate that the improved method, called OmniSR-M, achieves significant enhancement compared to the original OmniSR method and also surpasses other state-of-the-art methods. OmniSR-M effectively recovers image details while maintaining its lightweight nature. Specifically, OmniSR-M reduces the number of parameters by 26.56% and the computation by 27.66% compared to OmniSR. Moreover, this paper quantitatively analyzes both the facies porosity rate and grain size features in the application scenario. The results show that the images generated by OmniSR-M successfully recover key information about the rock thin section.

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