  Spectrum denoising is an important procedure for large-scale spectroscopical
surveys. This work proposes a novel stellar spectrum denoising method based on
deep Bayesian modeling. The construction of our model includes a prior
distribution for each stellar subclass, a spectrum generator and a flow-based
noise model. Our method takes into account the noise correlation structure, and
it is not susceptible to strong sky emission lines and cosmic rays. Moreover,
it is able to naturally handle spectra with missing flux values without ad-hoc
imputation. The proposed method is evaluated on real stellar spectra from the
Sloan Digital Sky Survey (SDSS) with a comprehensive list of common stellar
subclasses and compared to the standard denoising auto-encoder. Our denoising
method demonstrates superior performance to the standard denoising
auto-encoder, in respect of denoising quality and missing flux imputation. It
may be potentially helpful in improving the accuracy of the classification and
physical parameter measurement of stars when applying our method during data
preprocessing.
