A Hole-Filling Framework Based on DIBR and Improved Criminisi's Inpainting Algorithm for 3D Videos

Du, Ke; Domas, Stéphane; Wu, Mengdie; Lenczner, Michel

doi:10.1145/3141128.3141148

Cited by 1 publication

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“…Improvement had made which would be divided into three categories. The first category is an improvement to the priority model of Criminisi algorithm to obtain a better filling order, which can reduce structure propagation errors and accumulated errors [2][3][4][5][6][7][8][9][10][11][12][13][14] . The second category improves the patch matching method to reduce the uncoordinated block boundary [2,5,6,7,10,12] .The last category improves the speed by improving the search strategy [3,5,8,9,11,13] .…”

Section: Introductionmentioning

confidence: 99%

Improved image inpainting exemplar-based algorithms by boundary priori-knowledge

et al. 2022

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Image inpainting plays an important role in restoration of cultural relics, pictures beautification. Criminisi algorithm creates good results in large-area inpainting. However, it does still have some deficiencies such as over-extending. In this paper, two improved algorithms based on prior knowledge of the boundary had been proposed by simulating the idea of manual repairing. An algorithm, by simulating the strategy that the next inpainted pixel will be near to the prior one, named nearer neighbor first algorithm, can void the random bounding of the to-be-inpainted pixle. Another algorithm, by simulating the strategy that the inpainting process, named no-inpainted first algorithm, will be in multiple directions, can void the inpainting process in a single direction. The results reveal that the neighborhood-first algorithm performs better than Criminsi algorithm in repairing the missing structure while the unrepaired-first algorithm performs better than Criminsi algorithm in repairing the missing texture.

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