  We present the starblade algorithm, a method to separate superimposed point
sources from auto-correlated, diffuse flux using a Bayesian model. Point
sources are assumed to be independent from each other and to follow a power-law
brightness distribution. The diffuse emission is described as a non-parametric
log-normal model with a priori unknown correlation structure. This model
enforces positivity of the underlying emission and allows for variation in the
order of magnitudes. The correlation structure is recovered non-parametrically
in addition to the diffuse flux and is used for the separation of the point
sources. Additionally many measurement artifacts appear as point-like or
quasi-point-like effects, not compatible with superimposed diffuse emission. An
estimate of the separation uncertainty can be provided as well. We demonstrate
the capabilities of the derived method on synthetic data and data obtained by
the Hubble Space Telescope, emphasizing its effect on instrumental artifacts as
well as physical sources. The performance of this method is compared to the
background estimation of the SExtractor method, as well as to a denoising
auto-encoder.
