Thangka Image Inpainting Algorithm Based on Wavelet Transform and Structural Constraints
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초록
The thangka image inpainting method based on wavelet transform is not ideal for contour curves when the highfrequency information is repaired. In order to solve the problem, a new image inpainting algorithm is proposedbased on edge structural constraints and wavelet transform coefficients. Firstly, a damaged thangka image isdecomposed into low frequency subgraphs and high frequency subgraphs with different resolutions usingwavelet transform. Then, the improved fast marching method is used to repair the low frequency subgraphswhich represent structural information of the image. At the same time, for the high frequency subgraphs whichrepresent textural information of the image, the extracted and repaired edge contour information is used toconstrain structure inpainting in the proposed algorithm. Finally, the texture part is repaired using texturesynthesis based on the wavelet coefficient characteristic of each subgraph. In this paper, the improved methodis compared with the existing three methods. It is found that the improved method is superior to them ininpainting accuracy, especially in the case of contour curve. The experimental results show that the hierarchicalmethod combined with structural constraints has a good effect on the edge damage of thangka images.
Abstract
The thangka image inpainting method based on wavelet transform is not ideal for contour curves when the highfrequency information is repaired. In order to solve the problem, a new image inpainting algorithm is proposedbased on edge structural constraints and wavelet transform coefficients. Firstly, a damaged thangka image isdecomposed into low frequency subgraphs and high frequency subgraphs with different resolutions usingwavelet transform. Then, the improved fast marching method is used to repair the low frequency subgraphswhich represent structural information of the image. At the same time, for the high frequency subgraphs whichrepresent textural information of the image, the extracted and repaired edge contour information is used toconstrain structure inpainting in the proposed algorithm. Finally, the texture part is repaired using texturesynthesis based on the wavelet coefficient characteristic of each subgraph. In this paper, the improved methodis compared with the existing three methods. It is found that the improved method is superior to them ininpainting accuracy, especially in the case of contour curve. The experimental results show that the hierarchicalmethod combined with structural constraints has a good effect on the edge damage of thangka images.
참고문헌 (20)
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