PROJECT TITLE :

Exemplar-Based Inpainting: Technical Review and New Heuristics for Better Geometric Reconstructions

ABSTRACT:

This paper proposes a technical review of exemplar-primarily based inpainting approaches with a particular focus on greedy strategies. Many comparative and illustrative experiments are provided to deeply explore and enlighten these methods, and to possess a higher understanding on the state-of-the-art improvements of those approaches. From this analysis, three improvements over Criminisi et al. algorithm are then presented and detailed: 1) a tensor-primarily based information term for a higher choice of pixel candidates to fill in; a pair of) a fast patch lookup strategy to make sure a better world coherence of the reconstruction; and 3) a novel fast anisotropic spatial mixing algorithm that reduces typical block artifacts using tensor models. Relevant comparisons with the state-of-the-art inpainting ways are on condition that exhibit the effectiveness of our contributions.


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