This paper presents an adaptive reconstruction method of missing textures based on an inverse projection via sparse representation. The proposed method approximates original and corrupted textures in lower-dimensional subspaces by using the sparse representation technique. Then, this approach effectively solves problems of not being able to directly estimate an inverse projection for reconstructing missing textures. Furthermore, even if target textures contain missing areas, the proposed method enables adaptive generation of the subspaces by monitoring errors caused in their known neighboring textures by the estimated inverse projection. Consequently, since the optimal inverse projection is adaptively estimated for each texture, successful reconstruction of the missing areas can be expected. Experimental results show impressive improvement of the proposed reconstruction technique over previously reported reconstruction techniques.
['Takahiro Ogawa', 'Miki Haseyama']
Adaptive reconstruction method of missing textures based on inverse projection via sparse representation