  We present a blind multiframe image-deconvolution method based on robust
statistics. The usual shortcomings of iterative optimization of the likelihood
function are alleviated by minimizing the M-scale of the residuals, which
achieves more uniform convergence across the image. We focus on the
deconvolution of astronomical images, which are among the most challenging due
to their huge dynamic ranges and the frequent presence of large noise-dominated
regions in the images. We show that high-quality image reconstruction is
possible even in super-resolution and without the use of traditional
regularization terms. Using a robust \r{ho}-function is straightforward to
implement in a streaming setting and, hence our method is applicable to the
large volumes of astronomy images. The power of our method is demonstrated on
observations from the Sloan Digital Sky Survey (Stripe 82) and we briefly
discuss the feasibility of a pipeline based on Graphical Processing Units for
the next generation of telescope surveys.
