Sparse Recovery of Hyperspectral Signal from Natural RGB Images
Hyperspectral imaging is an important visual modality with growing interest and range of applications. The latter, however, is hindered by the fact that existing devices are limited in either spatial, spectral, and/or temporal resolution, while yet being
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Abstract. Hyperspectral imaging is an important visual modality with growing interest and range of applications. The latter, however, is hindered by the fact that existing devices are limited in either spatial, spectral, and/or temporal resolution, while yet being both complicated and expensive. We present a low cost and fast method to recover high quality hyperspectral images directly from RGB. Our approach first leverages hyperspectral prior in order to create a sparse dictionary of hyperspectral signatures and their corresponding RGB projections. Describing novel RGB images via the latter then facilitates reconstruction of the hyperspectral image via the former. A novel, larger-than-ever database of hyperspectral images serves as a hyperspectral prior. This database further allows for evaluation of our methodology at an unprecedented scale, and is provided for the benefit of the research community. Our approach is fast, accurate, and provides high resolution hyperspectral cubes despite using RGB-only input.
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Introduction
Hyperspectral imagery has been an active area of research since modern acquisition technology became available in the late 1970s [1]. Unlike RGB or multispectral acquisition devices, the goal of hyperspectral imaging is the acquisition of the complete spectral signature reflected from each observable point. The richness of this information facilitates numerous applications, but it also comes with a price – a significant decrease in spatial or temporal resolution (Note that in this sense a typical RGB or other multispectral cameras compromise the third dimension of hyperspectral data, namely the spectral resolution.). As a result, the use of Hyperspectral Imaging Systems (HISs) has been limited to those domains and applications in which these aspects of the signal (either spatial, but mostly temporal resolution) were not central – remote sensing (cf. [2]), agriculture (cf. [3]), geology (cf. [4]), astronomy (cf. [5]), earth sciences (cf. [6]), and others. Even in these cases the HIS is often used for the preliminary analysis of observable signals in order to characterize the parts of the spectrum that carries valuable information for the application. This information is then used to design multispectral devices (cameras with few spectral bands) that are optimized for that application. c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part VII, LNCS 9911, pp. 19–34, 2016. DOI: 10.1007/978-3-319-46478-7 2
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B. Arad and O. Ben-Shahar
Fig. 1. The estimation process: a rich hyperspectral prior is collected, a corresponding hyperspectral dictionary is produced and projected to RGB. Once produced, the dictionary may be used to reconstruct novel images without additional hyperspectral input.
Unlike their use in niche or dedicated applications such as the above, the use of HISs in general computer vision, and in particular in the analysis of natural images, is still in its infancy. The main obstacles are not only the reduced resolution in one of the acquisition “axes”
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