  We investigate efficient algorithmic realisations for robust deconvolution of
grey-value images with known space-invariant point-spread function, with
emphasis on 1D motion blur scenarios. The goal is to make deconvolution
suitable as preprocessing step in automated image processing environments with
tight time constraints. Candidate deconvolution methods are selected for their
restoration quality, robustness and efficiency. Evaluation of restoration
quality and robustness on synthetic and real-world test images leads us to
focus on a combination of Wiener filtering with few iterations of robust and
regularised Richardson-Lucy deconvolution. We discuss algorithmic optimisations
for specific scenarios. In the case of uniform linear motion blur in coordinate
direction, it is possible to achieve real-time performance (less than 50 ms) in
single-threaded CPU computation on images of $256\times256$ pixels. For more
general space-invariant blur settings, still favourable computation times are
obtained. Exemplary parallel implementations demonstrate that the proposed
method also achieves real-time performance for general 1D motion blurs in a
multi-threaded CPU setting, and for general 2D blurs on a GPU.
