科研之友
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摘要
Numerous image inpainting algorithms are guided by a basic assumption that the known region in the original image itself can provide sufficient prior information for the guess recovery of the unknown part, which is not often the case in actual art image inpainting. In order to solve the challenging inpainting case that there is little image prior in the remainder of the original image, we propose an average-face-based inpainting method based on a sample database with 3 steps: reference images selection, average image generation and exemplar-based image inpainting. In which, average image generation is crucial. In the inpainting framework, the average image can be directly viewed as an inpainting proposal for the severely damaged or absolutely lost image. Moreover, the average image can be applied to exemplar-based inpainting algorithm as a sample image to extend the searching region for match patch, so as to perform the restoration for images with large-scale or irregularly damaged holes. The inpainting experiments over some facial images of Dazu Rock Carvings demonstrate the validity and effectiveness of our method. It is first utilized for two extremely challenging inpainting tasks: reconstruction for the stolen head of Willow Avalokitesvara in Shimenshan No. 6 and the absolutely broken heads of two Avalokitesvaras in Beishan No.180. Compared to the failure of the exemplar-based inpainting algorithm within the original image and the directly duplication of a similar image, the generated average image can be as a more reliable inpainting proposal. The comparative experiments also show the efficiency and advantage of the average face applied to exemplar-based inpainting framework. Compared with some related inpainting algorithms, our method is more competitive when there is little prior information in the original image. The efficient virtual inpainting results are valuable references for both cave art historians and conservators.
