Per-channel regularization for regression-based spectral reconstruction

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.