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Volume: 30 | Article ID: art00007
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A Supervised Learning Approach for Dynamic Sampling (SLADS) in Raman Hyperspectral Imaging
  DOI :  10.2352/ISSN.2470-1173.2018.15.COIMG-132  Published OnlineJanuary 2018
Abstract

A supervised learning approach for dynamic sampling (SLADS) yielded a seven-fold reduction in the number of pixels sampled in hyperspectral Raman microscopy of pharmaceutical materials with negligible loss in image quality (~0.1% error). Following validation with ground-truth samples, sparse sampling strategies were informed in real-time by the preceding set of measurements. In brief, Raman spectra acquired at an initial set of random positions inform the next most information-rich location to subsequently sample within the field of view, which in turn iteratively informs the next locations until a stopping criterion associated with the reconstruction error is met. Calculation times on the order of a few milliseconds were insignificant relative to the timeframe for spectral acquisition at a given sampling location. The SLADS approach has the distinct advantage of being directly compatible with standard Raman instrumentation. Furthermore, SLADS is not limited to Raman imaging, providing a time-savings in image reconstruction whenever the single-pixel measurement time is the limiting factor in image generation.

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Shijie Zhang, Zhengtian Song, G. M. Dilshan P. Godaliyadda, Dong Hye Ye, Atanu Sengupta, Gregery T. Buzzard, Charles A. Boumanb, Garth J. Simpsona, "A Supervised Learning Approach for Dynamic Sampling (SLADS) in Raman Hyperspectral Imagingin Proc. IS&T Int’l. Symp. on Electronic Imaging: Computational Imaging XVI,  2018,  pp 132-1 - 1323,  https://doi.org/10.2352/ISSN.2470-1173.2018.15.COIMG-132

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