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- W2891364229 abstract "Massive amounts of data in spectral imagery increase acquisition, storing, and processing costs. Compressive spectral imaging (CSI) methods allow the reconstruction of spatial and spectral information from a small set of random projections. The single pixel camera is a low-cost optical architecture, which enables the compressive acquisition of spectral images. Traditional CSI reconstruction methods obtain a sparse approximation of the underlying spatial and spectral information; however, the complexity of these algorithms increases in proportion to the dimensionality of the data. This paper proposes a multi-resolution (MR) CSI reconstruction approach from single-pixel camera measurements that exploits spectral similarities between the pixels to group them in super-pixels, such that the total number of unknowns in the inverse problem is reduced. Specifically, two different types of super-pixels are considered: rectangular and irregular structures. Simulation and experimental results show that the proposed MR scheme improves the reconstruction quality in up to 6 dB of peak signal-to-noise ratio and reconstruction time in up to 90% with respect to the traditional full resolution reconstructions." @default.
- W2891364229 created "2018-09-27" @default.
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- W2891364229 creator A5057826111 @default.
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- W2891364229 date "2018-12-01" @default.
- W2891364229 modified "2023-10-09" @default.
- W2891364229 title "Multi-Resolution Compressive Spectral Imaging Reconstruction From Single Pixel Measurements" @default.
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- W2891364229 doi "https://doi.org/10.1109/tip.2018.2867273" @default.
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