Abstract:Ancient murals are important cultural heritages for our exploration of ancient civilizations and are of great research value. Due to long-time exposure to the environment, ancient murals often suffer from damage (deterioration) such as cracks, scratches, corrosion, paint loss, and even large-region falling off. It is an urgent work to protect and restore these damaged ancient murals. Mural inpainting techniques refer to virtually filling the deteriorated regions by reconstructing the structure and texture elem… Show more
“…Then the restoration result is improved based on improved exemplar-based region filling method. Experiments taken place in the mural of east wall of the sixth grotto of Yungang Grottoes and simulated mural show that the proposed method can achieve (20) SSIM x, y = (2u x u y + c 1 )(σ xy + c 2 ) (µ 2…”
Section: Discussionmentioning
confidence: 99%
“…Yurui et al [19] addressed extensive missing information on paintings and calligraphy by employing the concept of completing structure before filling in texture, conducting virtual restoration for large-scale missing damages. Deng et al [20] emphasized the significance of structural information in virtual restoration of mural by introducing a dual-branch image restoration model guided by structure. But this model still needs a large amount of real data of mural to train the net.…”
Restoring the murals' various kinds of deteriorations is urgently necessary given the growing awareness of the need to protect cultural relics. Virtual restoration starts with an accurate extraction of deterioration. It is challenging to precisely extract scratches from murals because of their intricate information. Hyperspectral images are used to accentuate scratches of mural in this paper. First, a technique for improving information was put forth that involved the transformation of Principal Component Analysis (PCA) and a high-pass filter. Second, by using multi-scale bottom hat transformation, Otsu threshold segmentation, and non-deterioration mask, the deterioration information was extracted from the enhanced result. Third, the morphological transformation and connected component analysis were used to denoise the extracted results. Additionally, the scratched image was repaired using an improved exemplar-based region filling method. The results of deterioration information under different enhancement methods were discussed, and the deterioration extraction method proposed in this paper was contrasted with other deterioration extraction methods. The extraction accuracy was greatly increased by the suggested method. Additionally, we assessed the accuracy of various virtual restoration techniques for image restoration and discovered that our suggested restoration method did a good job of maintaining the structural integrity of the mural's information.
“…Then the restoration result is improved based on improved exemplar-based region filling method. Experiments taken place in the mural of east wall of the sixth grotto of Yungang Grottoes and simulated mural show that the proposed method can achieve (20) SSIM x, y = (2u x u y + c 1 )(σ xy + c 2 ) (µ 2…”
Section: Discussionmentioning
confidence: 99%
“…Yurui et al [19] addressed extensive missing information on paintings and calligraphy by employing the concept of completing structure before filling in texture, conducting virtual restoration for large-scale missing damages. Deng et al [20] emphasized the significance of structural information in virtual restoration of mural by introducing a dual-branch image restoration model guided by structure. But this model still needs a large amount of real data of mural to train the net.…”
Restoring the murals' various kinds of deteriorations is urgently necessary given the growing awareness of the need to protect cultural relics. Virtual restoration starts with an accurate extraction of deterioration. It is challenging to precisely extract scratches from murals because of their intricate information. Hyperspectral images are used to accentuate scratches of mural in this paper. First, a technique for improving information was put forth that involved the transformation of Principal Component Analysis (PCA) and a high-pass filter. Second, by using multi-scale bottom hat transformation, Otsu threshold segmentation, and non-deterioration mask, the deterioration information was extracted from the enhanced result. Third, the morphological transformation and connected component analysis were used to denoise the extracted results. Additionally, the scratched image was repaired using an improved exemplar-based region filling method. The results of deterioration information under different enhancement methods were discussed, and the deterioration extraction method proposed in this paper was contrasted with other deterioration extraction methods. The extraction accuracy was greatly increased by the suggested method. Additionally, we assessed the accuracy of various virtual restoration techniques for image restoration and discovered that our suggested restoration method did a good job of maintaining the structural integrity of the mural's information.
“…Peng et al [34] proposed a model that uses dual-domain partial convolution to process valid pixels and combines frequency conversion to promote effective fusion of multi-scale features. Deng et al [35] believe that most existing mural inpainting models neglect the importance of structural guidance, making it impossible to fill in complex and diverse damaged content with structures. Thus, the authors proposed a structure-guided model based on GAN for the inpainting of ancient murals.…”
Section: Deep Learning-based Chinese Paintings Inpainting Methodsmentioning
Chinese paintings have great cultural and artistic significance and are known for their delicate lines and rich textures. Unfortunately, many ancient paintings have been damaged due to historical and natural factors. The deep learning methods that are successful in restoring natural images cannot be applied to the inpainting of ancient paintings. Thus, we propose a model named Edge-MSGAN for inpainting Chinese ancient paintings based on edge guidance and multi-scale residual blocks. The Edge-MSGAN utilizes edge images to direct the completion network in order to generate entire ancient paintings. It then applies the multi-branch color correction network to adjust the colors. Furthermore, the model uses multi-scale channel attention residual blocks to learn the semantic features of ancient paintings at various levels. At the same time, by using polarized self-attention, the model can improve its concentration on significant structures, edges, and details, which leads to paintings that possess clear lines and intricate details. Finally, we have created a dataset for ancient paintings inpainting, and have conducted experiments in order to evaluate the model’s performance. After comparing the proposed model with state-of-the-art models from qualitative and quantitative aspects, it was found that our model is better at inpainting the texture, edge, and color of ancient paintings. Therefore, our model achieved maximum PSNR and SSIM values of 34.7127 and 0.9280 respectively, and minimum MSE and LPIPS values of 0.0006 and 0.0495, respectively.
“…In addition, the author also designed knowledge consistency attention to adaptively fuse attention scores and gradually refine the feature map. Deng et al [20] believed that most existing mural inpainting models neglected the importance of structural guidance, making it impossible to fill in complex and diverse damaged content with structures. Thus, the author proposed a structure-guided model based on GAN for the inpainting of ancient murals.…”
Chinese paintings have great cultural and artistic significance, known for their delicate lines and rich textures. Unfortunately, many ancient paintings have been damaged due to historical and natural factors. The deep learning methods that are successful in restoring natural images cannot be applied to ancient paintings inpainting. Thus, we propose a model named Edge-MSGAN for inpainting Chinese ancient paintings based on edge guidance and multi-scale residual blocks. The Edge-MSGAN utilizes edge images to direct the completion network for generating entire ancient paintings. It then applies the multi-branch color correction network to adjust the colors. Furthermore, the model uses multi-scale channel attention residual blocks to learn the semantic features of ancient paintings at various levels. At the same time, by using polarized self-attention, the model can improve its concentration on significant structures, edges, and details, which leads to paintings that possess clear lines and intricate details. Finally, we have created a dataset for ancient paintings inpainting, and have conducted experiments to evaluate the model’s performance. After comparing the proposed model with the state-of-the-art models from qualitative and quantitative aspects, it is found that our model is better at inpainting the texture, edge, and color of ancient paintings.
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