Genser N, Seiler J, Kaup A (2020)
Publication Type: Journal article
Publication year: 2020
Book Volume: 29
Pages Range: 9234-9249
Article Number: 9205584
Recently, many new applications arose for multi-spectral and hyper-spectral imaging. Besides modern biometric systems for identity verification, also agricultural and medical applications came up, which measure the health condition of plants and humans. Despite the growing demand, the acquisition of multi-spectral data is up to the present complicated. Often, expensive, inflexible, or low resolution acquisition setups are only obtainable for specific professional applications. To overcome these limitations, a novel camera array for multi-spectral imaging is presented in this article for generating consistent multi-spectral videos. As differing spectral images are acquired at various viewpoints, a geometrically constrained multi-camera sensor layout is introduced, which enables the formulation of novel registration and reconstruction algorithms to globally set up robust models. On average, the novel acquisition approach achieves a gain of 2.5 dB PSNR compared to recently published multi-spectral filter array imaging systems. At the same time, the proposed acquisition system ensures not only a superior spatial, but also a high spectral, and temporal resolution, while filters are flexibly exchangeable by the user depending on the application. Moreover, depth information is generated, so that 3D imaging applications, e.g., for augmented or virtual reality, become possible. The proposed camera array for multi-spectral imaging can be set up using off-the-shelf hardware, which allows for a compact design and employment in, e.g., mobile devices or drones, while being cost-effective.
APA:
Genser, N., Seiler, J., & Kaup, A. (2020). Camera Array for Multi-Spectral Imaging. IEEE Transactions on Image Processing, 29, 9234-9249. https://dx.doi.org/10.1109/TIP.2020.3024738
MLA:
Genser, Nils, Jürgen Seiler, and André Kaup. "Camera Array for Multi-Spectral Imaging." IEEE Transactions on Image Processing 29 (2020): 9234-9249.
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