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.

The aim of Diminished Reality (DR) is to remove a target object in a live video stream seamlessly. In our approach, the area of the target object is replaced with new texture that blends with the rest of the image. The result is then propagated to the next frames of the video. One of the important stages of this technique is to update the target region with respect to the illumination change. This is a complex and recurrent problem when the viewpoint changes. We show that the state-of-the-art in DR fails in solving this problem, even under simple scenarios. We then use local illumination models to address this problem. According to these models, the variation in illumination only affects the specular component of the image. In the context of DR, the problem is therefore solved by propagating the specularities in the target area. We list a set of structural properties of specularities which we incorporate in two new models for specularity propagation. Our first model includes the same property as the previous approaches, which is the smoothness of illumination variation, but has a different estimation method based on the Thin-Plate Spline. Our second model incorporates more properties of the specularity's shape on planar surfaces. Experimental results on synthetic and real data show that our strategy substantially improves the rendering quality compared to the state-of-the-art in DR.

The latest advancements in Augmented Reality (AR) and Diminished Reality (DR) have allowed the development of many consumer-oriented applications (such as sales and driving aid, or education). To increase the realism in rendering, estimating the illumination in the scene is a key element. A lot of works tackle this problem but rarely discuss the color of the reconstructed illumination. The Dichromatic Model indicates that the specular component is not affected in color by the texture underneath and holds the light source's color. Though theoretically sound, in practice consumer cameras are subject to nonlinear behaviors which change RGB ratios and create inconsistencies when estimating the illumination. In this paper, we study the conditioning and limits of inverting local illumination models while relying on the Dichromatic Model. We show that the reconstructed specular component has an inconsistent color because it changes depending on the surface's colors.