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.
Highlights
Chinese ancient stone inscriptions contain Chinese traditional calligraphy culture and art information
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. erefore, we propose a basic framework for image denoising and inpainting of stone inscriptions based on deep learning methods
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. e experimental results show that our image denoising model achieves better results on PSNR, structural similarity (SSIM), and comprehensive evaluation index (CEI). e final results have obvious visual improvement compared with the original stone inscription images
Summary
Shi et al [5] use Butterworth low-pass filter to process the high and low frequency components of stone inscription images; this method can effectively remove the burr noise of strokes These traditional image denoising methods can achieve good denoising results, but there still exist many problems, such as relying on prior knowledge, manually setting parameters, and removing few types of noise. Ese methods can ensure that the generated image areas are consistent with the context semantic information, but they are only effective for rectangular missing in the images, and the results have pixel discontinuity at the edges of the images. Erefore, this paper mainly focuses on the automatic generation, multitype noise removal, and image inpainting methods of Chinese ancient stone inscriptions images, so as to obtain the images with good visual effects There are only a few researches on image denoising algorithm of stone inscription images, while there are few works on the automatic generation and image inpainting of stone inscriptions images. e existing denoising methods often result in the loss of effective information, and the small amount of fixed stone inscription images is a natural challenge. erefore, this paper mainly focuses on the automatic generation, multitype noise removal, and image inpainting methods of Chinese ancient stone inscriptions images, so as to obtain the images with good visual effects
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