  In this paper, an iterative method for robust deconvolution with positivity
constraints is discussed. It is based on the known variational interpretation
of the Richardson-Lucy iterative deconvolution as fixed-point iteration for the
minimisation of an information divergence functional under a multiplicative
perturbation model. The asymmetric penaliser function involved in this
functional is then modified into a robust penaliser, and complemented with a
regulariser. The resulting functional gives rise to a fixed point iteration
that we call robust and regularised Richardson-Lucy deconvolution. It achieves
an image restoration quality comparable to state-of-the-art robust variational
deconvolution with a computational efficiency similar to that of the original
Richardson-Lucy method. Experiments on synthetic and real-world image data
demonstrate the performance of the proposed method.
