Ancient Stone Inscription Image Denoising and Inpainting Methods Based on Deep Neural Networks

Haoming Zhang, Yue Qi, Xiaoting Xue, Yahui Nan and Gengxin Sun

Discrete Dynamics in Nature and Society, 2021, vol. 2021, 1-11

Abstract: 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:7675611

DOI: 10.1155/2021/7675611

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