Ancient Stone Inscription Image Denoising and Inpainting Methods Based on Deep Neural Networks
Chinese ancient stone inscriptions contain Chinese traditional calligraphy culture and art information. However, due to the long history of the ancient stone inscriptions, natural erosion, and poor early protection measures, there are a lot of noise in the existing ancient stone inscriptions, which...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/7675611 |
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| _version_ | 1832563241517056000 |
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| author | Haoming Zhang Yue Qi Xiaoting Xue Yahui Nan |
| author_facet | Haoming Zhang Yue Qi Xiaoting Xue Yahui Nan |
| author_sort | Haoming Zhang |
| collection | DOAJ |
| description | Chinese ancient stone inscriptions contain Chinese traditional calligraphy culture and art information. However, due to the long history of the ancient stone inscriptions, natural erosion, and poor early protection measures, there are a lot of noise in the existing ancient stone inscriptions, which has adverse effects on reading these stone inscriptions and their aesthetic appreciation. At present, digital technologies have played important roles in the protection of cultural relics. For ancient stone inscriptions, we should obtain more perfect digital results without multiple types of noise, while there are few deep learning methods designed for processing stone inscription images. Therefore, we propose a basic framework for image denoising and inpainting of stone inscriptions based on deep learning methods. Firstly, we collect as many images of stone inscriptions as possible and preprocess these images to establish an inscriptions image dataset for image denoising and inpainting. In addition, an improved GAN with a denoiser is used for generating more virtual stone inscription images to expand the dataset. On the basis of these collected and generated images, we designed a stone inscription image denoising model based on multiscale feature fusion and introduced Charbonnier loss function to improve this image denoising model. To further improve the denoising results, an image inpainting model with the coherent semantic attention mechanism is introduced to recover some effective information removed by the former denoising model as much as possible. The experimental results show that our image denoising model achieves better results on PSNR, SSIM, and CEI. The final results have obvious visual improvement compared with the original stone inscription images. |
| format | Article |
| id | doaj-art-bb0349d665c347f098b418a5c5be7a61 |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-bb0349d665c347f098b418a5c5be7a612025-02-03T01:20:38ZengWileyDiscrete Dynamics in Nature and Society1607-887X2021-01-01202110.1155/2021/7675611Ancient Stone Inscription Image Denoising and Inpainting Methods Based on Deep Neural NetworksHaoming Zhang0Yue Qi1Xiaoting Xue2Yahui Nan3School of Information Science and TechnologyArt SchoolTeaching Affairs Department of Northwestern Polytechnical UniversityDepartment of Computer Science and TechnologyChinese ancient stone inscriptions contain Chinese traditional calligraphy culture and art information. However, due to the long history of the ancient stone inscriptions, natural erosion, and poor early protection measures, there are a lot of noise in the existing ancient stone inscriptions, which has adverse effects on reading these stone inscriptions and their aesthetic appreciation. At present, digital technologies have played important roles in the protection of cultural relics. For ancient stone inscriptions, we should obtain more perfect digital results without multiple types of noise, while there are few deep learning methods designed for processing stone inscription images. Therefore, we propose a basic framework for image denoising and inpainting of stone inscriptions based on deep learning methods. Firstly, we collect as many images of stone inscriptions as possible and preprocess these images to establish an inscriptions image dataset for image denoising and inpainting. In addition, an improved GAN with a denoiser is used for generating more virtual stone inscription images to expand the dataset. On the basis of these collected and generated images, we designed a stone inscription image denoising model based on multiscale feature fusion and introduced Charbonnier loss function to improve this image denoising model. To further improve the denoising results, an image inpainting model with the coherent semantic attention mechanism is introduced to recover some effective information removed by the former denoising model as much as possible. The experimental results show that our image denoising model achieves better results on PSNR, SSIM, and CEI. The final results have obvious visual improvement compared with the original stone inscription images.http://dx.doi.org/10.1155/2021/7675611 |
| spellingShingle | Haoming Zhang Yue Qi Xiaoting Xue Yahui Nan Ancient Stone Inscription Image Denoising and Inpainting Methods Based on Deep Neural Networks Discrete Dynamics in Nature and Society |
| title | Ancient Stone Inscription Image Denoising and Inpainting Methods Based on Deep Neural Networks |
| title_full | Ancient Stone Inscription Image Denoising and Inpainting Methods Based on Deep Neural Networks |
| title_fullStr | Ancient Stone Inscription Image Denoising and Inpainting Methods Based on Deep Neural Networks |
| title_full_unstemmed | Ancient Stone Inscription Image Denoising and Inpainting Methods Based on Deep Neural Networks |
| title_short | Ancient Stone Inscription Image Denoising and Inpainting Methods Based on Deep Neural Networks |
| title_sort | ancient stone inscription image denoising and inpainting methods based on deep neural networks |
| url | http://dx.doi.org/10.1155/2021/7675611 |
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