Per-channel regularization for regression-based spectral reconstruction

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Spectral reconstruction algorithms seek to recover spectra from RGB images. This estimation problem is often formulated as least-squares regression, and a Tikhonov regularization is generally incorporated, both to support stable estimation in the presence of noise and to prevent over-fitting. The degree of regularization is controlled by a single penalty-term parameter, which is often selected using the cross validation experimental methodology. In this paper, we generalize the simple regularization approach to admit a per-spectral-channel optimization setting, and a modified cross-validation procedure is developed. Experiments validate our method. Compared to the conventional regularization, our per-channel approach significantly improves the reconstruction accuracy at multiple spectral channels, by up to 17% increments for all the considered models.

Original languageEnglish
JournalCEUR Workshop Proceedings
Publication statusPublished - 17 Sep 2020
Event10th Colour and Visual Computing Symposium, CVCS 2020 - Virtual, Gjoivik, Norway
Duration: 16 Sep 202017 Sep 2020


  • Hyperspectral imaging
  • Multispectral imaging
  • Spectral reconstruction

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