Cleanup Sketched Drawings: Deep Learning-Based Model
Rough drawings provide artists with a simple and efficient way to express shapes and ideas. Artists frequently use sketches to highlight their envisioned curves, using several groups’ raw strokes. These rough sketches need enhancement to remove some subtle impurities and completely simplify curves o...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2022/2238077 |
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| _version_ | 1832562538010640384 |
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| author | Amal Ahmed Hasan Mohammed Jiazhou Chen |
| author_facet | Amal Ahmed Hasan Mohammed Jiazhou Chen |
| author_sort | Amal Ahmed Hasan Mohammed |
| collection | DOAJ |
| description | Rough drawings provide artists with a simple and efficient way to express shapes and ideas. Artists frequently use sketches to highlight their envisioned curves, using several groups’ raw strokes. These rough sketches need enhancement to remove some subtle impurities and completely simplify curves over the sketched images. This research paper proposes using a fully convolutional network (FCNN) model to simplify rough raster drawings using deep learning. As input, the FCNN takes a sketch image of any size and automatically generates a high-quality simplified sketch image as output. Our model intuitively addresses the shortcomings in the rough sketch image, such as noises and unwanted background, as well as the low resolution of the rough sketch image. The FCNN model is trained by three raster image datasets, which are publicly available online. This paper demonstrates the efficiency and effectiveness of using deep learning in cleaning and improving the roughly drawn image in an automatic way. For evaluating the results, the mean squared error (MSE) metric was used. From experimental results, it was observed that an enhanced FCNN model reported better accuracy, reducing the prediction error by 0.08 percent for simplifying the rough sketch compared to the existing methods. |
| format | Article |
| id | doaj-art-797103a3956c4a2ea9358e4318f0000a |
| institution | Kabale University |
| issn | 1754-2103 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-797103a3956c4a2ea9358e4318f0000a2025-02-03T01:22:26ZengWileyApplied Bionics and Biomechanics1754-21032022-01-01202210.1155/2022/2238077Cleanup Sketched Drawings: Deep Learning-Based ModelAmal Ahmed Hasan Mohammed0Jiazhou Chen1College of Computer Science and TechnologyCollege of Computer Science and TechnologyRough drawings provide artists with a simple and efficient way to express shapes and ideas. Artists frequently use sketches to highlight their envisioned curves, using several groups’ raw strokes. These rough sketches need enhancement to remove some subtle impurities and completely simplify curves over the sketched images. This research paper proposes using a fully convolutional network (FCNN) model to simplify rough raster drawings using deep learning. As input, the FCNN takes a sketch image of any size and automatically generates a high-quality simplified sketch image as output. Our model intuitively addresses the shortcomings in the rough sketch image, such as noises and unwanted background, as well as the low resolution of the rough sketch image. The FCNN model is trained by three raster image datasets, which are publicly available online. This paper demonstrates the efficiency and effectiveness of using deep learning in cleaning and improving the roughly drawn image in an automatic way. For evaluating the results, the mean squared error (MSE) metric was used. From experimental results, it was observed that an enhanced FCNN model reported better accuracy, reducing the prediction error by 0.08 percent for simplifying the rough sketch compared to the existing methods.http://dx.doi.org/10.1155/2022/2238077 |
| spellingShingle | Amal Ahmed Hasan Mohammed Jiazhou Chen Cleanup Sketched Drawings: Deep Learning-Based Model Applied Bionics and Biomechanics |
| title | Cleanup Sketched Drawings: Deep Learning-Based Model |
| title_full | Cleanup Sketched Drawings: Deep Learning-Based Model |
| title_fullStr | Cleanup Sketched Drawings: Deep Learning-Based Model |
| title_full_unstemmed | Cleanup Sketched Drawings: Deep Learning-Based Model |
| title_short | Cleanup Sketched Drawings: Deep Learning-Based Model |
| title_sort | cleanup sketched drawings deep learning based model |
| url | http://dx.doi.org/10.1155/2022/2238077 |
| work_keys_str_mv | AT amalahmedhasanmohammed cleanupsketcheddrawingsdeeplearningbasedmodel AT jiazhouchen cleanupsketcheddrawingsdeeplearningbasedmodel |