Image completion using multispectral data

We propose to explore the potential of multispectral imaging applied to image completion. In our experiments, multispectral images are acquired using an ultra-compact snapshot camera-recorder that senses 16 different spectral channels in the visible spectrum (475-650 nm). We show that 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 of standard methods that were developed by both hyperspectral imaging and computer vision communities. We validate our method on a variety of scenes using a perceptual evaluation based on 20 volunteers.