Researchers from North Carolina State University and the University of Delaware have developed an algorithm that can quickly and accurately reconstruct hyperspectral images using less data. The images are created using instruments that capture hyperspectral information succinctly, and the combination of algorithm and hardware makes it possible to acquire hyperspectral images in less time and to store those images using less memory.

Images at wavelengths from 470 nm to 632 nm within image cubes reconstructed by the new algorithm and another state-of-art algorithm for the LEGO image cube. The top row represents the ground truth; the middle row shows the output of the new algorithm; and the bottom row shows the output of the other algorithm.

In recent years, researchers have developed new hyperspectral imaging hardware that can acquire the necessary images more quickly and store the images using significantly less memory. The hardware takes advantage of “compressive measurements,” which mix spatial and wavelength data in a format that can be used later to reconstruct the complete hyperspectral image. But in order for the new hardware to work effectively, you need an algorithm that can reconstruct the image accurately and quickly. And that’s what the researchers have developed.

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