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Journal Articles IEEE Transactions on Image Processing Year : 2013

Hierarchical super-resolution-based inpainting

Abstract

This paper introduces a novel framework for examplar-based inpainting. It consists in performing first the inpainting on a coarse version of the input image. A hierarchical super-resolution algorithm is then used to recover details on the missing areas. The advantage of this approach is that it is easier to inpaint low-resolution pictures than high resolution ones. The gain is both in terms of computational complexity and visual quality. However, to be less sensitive to the parameter setting of the inpainting method, the low-resolution input picture is inpainted several times with different configurations. Results are efficiently combined with a loopy belief propagation and details are recovered by a single-image super-resolution algorithm. Experimental results in a context of image editing and texture synthesis demonstrate the effectiveness of the proposed method. Results are compared to five state-of-the-art inpainting methods.
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Origin : Files produced by the author(s)
Format : Figure, Image
Origin : Files produced by the author(s)
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Dates and versions

hal-00876168 , version 1 (21-11-2014)

Identifiers

Cite

Olivier Le Meur, Mounira Ebdelli, Christine Guillemot. Hierarchical super-resolution-based inpainting. IEEE Transactions on Image Processing, 2013, 22 (10), pp.3779-3790. ⟨10.1109/TIP.2013.2261308⟩. ⟨hal-00876168⟩
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