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Then, by deriving the sparse representation of target patches including missing areas based on the generated dictionary, inpainting based on the SSIM index is realized.
Finally, in Figures13,14,15, and16, we show some examples obtained by performing inpainting for larger missing areas based on the proposed method.
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Several missing area reconstruction methods based on sparse representation have been proposed [21 23].
The above existing methods based on texture synthesis and exemplar-based inpainting generally copy pixel values to missing areas directly.
Thus, models using the subspaces based on the MSE can only represent such low-frequency components, and it becomes difficult to reconstruct the missing high-frequency components of the missing areas.
Once missing areas are identified, the proposed procedure computes additional scanning orientations by using the visibility information of missing areas.
(b) Flag image whose white regions correspond to missing areas.
In the inpainting of missing areas, we first clip a patch including missing areas from the target image.
The proposed procedure identifies missing areas from multiple range images (scanning data), and attempts to locate additional scanning orientations to fill the missing areas.
The proposed procedure identifies missing areas from the initial scanning data from default orientations and attempts to locate additional scanning orientations to fill the missing areas.
Therefore, the proposed method clips patches including missing areas and performs inpainting to estimate all missing intensities.
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