Image completion using multispectral imaging

Frédéric Bousefsaf 1 Mohamed Tamaazousti 1 Souheil Hadj Said 1 Rémi Michel 1
1 LVIC - Laboratoire Vision et Ingénierie des Contenus
DIASI - Département Intelligence Ambiante et Systèmes Interactifs : DRT/LIST/DIASI
Abstract : Abstract: Here, the authors explore the potential of multispectral imaging applied to image completion. Snapshot multispectral cameras correspond to breakthrough technologies that are suitable for everyday use. Therefore, they correspond to an interesting alternative to digital cameras. In their experiments, multispectral images are acquired using an ultracompact snapshot camera-recorder that senses 16 different spectral channels in the visible spectrum. Direct exploitation of completion algorithms by extension of the spectral channels exhibits only minimum enhancement. A dedicated method that consists in a prior segmentation of the scene has been developed to address this issue. The segmentation derives from an analysis of the spectral data and is employed to constrain research area of exemplar-based completion algorithms. The full processing chain takes benefit from standard methods that were developed by both hyperspectral imaging and computer vision communities. Results indicate that image completion constrained by spectral presegmentation ensures better consideration of the surrounding materials and simultaneously improves rendering consistency, in particular for completion of flat regions that present no clear gradients and little structure variance. The authors validate their method with a perceptual evaluation based on 20 volunteers. This study shows for the first time the potential of multispectral imaging applied to image completion.
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Journal articles
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https://hal-cea.archives-ouvertes.fr/cea-01836229
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Submitted on : Thursday, July 12, 2018 - 10:45:58 AM
Last modification on : Saturday, July 13, 2019 - 9:02:02 PM

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Frédéric Bousefsaf, Mohamed Tamaazousti, Souheil Hadj Said, Rémi Michel. Image completion using multispectral imaging. IET Image Processing, Institution of Engineering and Technology, 2018, 12 (7), pp.1164 - 1174. ⟨10.1049/iet-ipr.2017.1203⟩. ⟨cea-01836229⟩

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