دورية أكاديمية
Removal of Color-Document Image Show-Through Based on Self-Supervised Learning
| العنوان: | Removal of Color-Document Image Show-Through Based on Self-Supervised Learning |
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| المؤلفون: | Mengying Ni, Zongbao Liang, Jindong Xu |
| المصدر: | Applied Sciences, Vol 14, Iss 11, p 4568 (2024) |
| بيانات النشر: | MDPI AG, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
| مصطلحات موضوعية: | document image restoration, show-through removal, self-supervised learning, cycle generative adversarial network, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
| الوصف: | Show-through phenomena have always been a challenging issue in color-document image processing, which is widely used in various fields such as finance, education, and administration. Existing methods for processing color-document images face challenges, including dealing with double-sided documents with show-through effects, accurately distinguishing between foreground and show-through parts, and addressing the issue of insufficient real image data for supervised training. To overcome these challenges, this paper proposes a self-supervised-learning-based method for removing show-through effects in color-document images. The proposed method utilizes a two-stage-structured show-through-removal network that incorporates a double-cycle consistency loss and a pseudo-similarity loss to effectively constrain the process of show-through removal. Moreover, we constructed two datasets consisting of different show-through mixing ratios and conducted extensive experiments to verify the effectiveness of the proposed method. Experimental results demonstrate that the proposed method achieves competitive performance compared to state-of-the-art methods and can effectively perform show-through removal without the need for paired datasets. Specifically, the proposed method achieves an average PSNR of 33.85 dB on our datasets, outperforming comparable methods by a margin of 0.89 dB. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2076-3417 46049983 |
| Relation: | https://www.mdpi.com/2076-3417/14/11/4568; https://doaj.org/toc/2076-3417 |
| DOI: | 10.3390/app14114568 |
| URL الوصول: | https://doaj.org/article/8b92891d0f1d460499833830478819df |
| رقم الأكسشن: | edsdoj.8b92891d0f1d460499833830478819df |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20763417 46049983 |
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| DOI: | 10.3390/app14114568 |