3-D deconvolution of hyper-spectral astronomical data
  In this paper we present a general forward fitting method for multichannel
image restoration based on regularized chi2. We introduce separable
regularizations that account for the dynamic of the model and take advantage of
the continuities present in the data, leaving only two hyper-parameters to
tune. We illustrate a practical implementation of this method in the context of
host galaxy subtraction for the Nearby SuperNova factory. We show that the
image restoration obtained fulfills the stringent requirements on bias and
photometricity needed by this program. The reconstruction yields sub-percent
integrated residuals in all the synthetic filters considered both on real and
simulated data. Even though our implementation is tied to the SNfactory data,
the method translates to any hyper-spectral data. As such, it is of direct
relevance to several new generation instruments like MUSE. Also, this technique
could be applied to multi-band astronomical imaging for which image
reconstruction is important, for example to increase image resolution for weak
lensing surveys.
