An efficient texture-structure conserving patch matching algorithm for inpainting mural images

Bhele, Sujata; Shriramwar, Shashank; Agarkar, Poonam

doi:10.1007/s11042-023-15370-5

Cited by 2 publications

(1 citation statement)

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“…Wang et al [21] proposed a sparse model that selects candidate patches based on texture similarity and structural continuity, while using the corresponding line drawing of the mural image to add line structures in the missing areas. Bhele et al [22] proposed a texture-structure conserving patch matching algorithm (TSCPMA). The algorithm improves the capabilities of the Criminisi algorithm in repairing large, damaged areas and small gaps by redefining the minimum similarity distance criterion to select the best-matching patches.…”

Section: Traditional Chinese Paintings Inpainting Methodsmentioning

confidence: 99%

Chinese Ancient Paintings Inpainting Based on Edge Guidance and Multi-Scale Residual Blocks

Sun,

Lei,

2024

Electronics

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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.

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Section: Traditional Chinese Paintings Inpainting Methodsmentioning

confidence: 99%