Light spectra are a very important source of information for diverse classification problems, e.g., for discrimination of materials. To lower the cost of acquiring this information, multispectral cameras are used. Several techniques exist for estimating light spectra out of multispectral images by exploiting properties about the spectrum. Unfortunately, especially when capturing multispectral videos, the images are heavily affected by noise due to the nature of limited exposure times in videos. Therefore, models that explicitly try to lower the influence of noise on the reconstructed spectrum are highly desirable. Hence, a novel reconstruction algorithm is presented. This novel estimation method is based on the guided filtering technique that preserves basic structures, while using spatial information to reduce the influence of noise. The evaluation based on spectra of natural images reveals that this new technique yields better quantitative and subjective results in noisy scenarios than other state-of-the-art spatial reconstruction methods. Specifically, the proposed algorithm lowers the mean squared error and the spectral angle up to 46% and 35% in noisy scenarios, respectively. Furthermore, it is shown that the proposed reconstruction technique works out of the box and does not need any calibration or training by reconstructing spectra from a real-world multispectral camera with nine channels.

In many image processing tasks it occurs that pixels or blocks of pixels are missing or lost in only some channels. For example during defective transmissions of RGB images, it may happen that one or more blocks in one color channel are lost. Nearly all modern applications in image processing and transmission use at least three color channels, some of the applications employ even more bands, for example in the infrared and ultraviolet area of the light spectrum. Typically, only some pixels and blocks in a subset of color channels are distorted. Thus, other channels can be used to reconstruct the missing pixels, which is called spatio-spectral reconstruction. Current state-of-the-art methods purely rely on the local neighborhood, which works well for homogeneous regions. However, in highfrequency regions like edges or textures, these methods fail to properly model the relationship between color bands. Hence, this paper introduces non-local filtering for building a linear regression model that describes the inter-band relationship and is used to reconstruct the missing pixels. Our novel method is able to increase the PSNR on average by 2 dB and yields visually much more appealing images in high-frequency regions.

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In this paper, a synthetic hyperspectral video database is introduced. Since it is impossible to record ground truth hyperspectral videos, this database offers the possibility to leverage the evaluation of algorithms in diverse applications. For all scenes, depth maps are provided as well to yield the position of a pixel in all spatial dimensions as well as the reflectance in spectral dimension. Two novel algorithms for two different applications are proposed to prove the diversity of applications that can be addressed by this novel database. First, a cross-spectral image reconstruction algorithm is extended to exploit the temporal correlation between two consecutive frames. The evaluation using this hyperspectral database shows an increase in PSNR of up to 5.6 dB dependent on the scene. Second, a hyperspectral video coder is introduced which extends an existing hyperspectral image coder by exploiting temporal correlation. The evaluation shows rate savings of up to 10% depending on the scene. The novel hyperspectral video database and source code is available at https:// github.com/ FAU-LMS/ HyViD for use by the research community.

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