  We investigate possibilities to speed up iterative algorithms for non-blind
image deconvolution. We focus on algorithms in which convolution with the
point-spread function to be deconvolved is used in each iteration, and aim at
accelerating these convolution operations as they are typically the most
expensive part of the computation. We follow two approaches: First, for some
practically important specific point-spread functions, algorithmically
efficient sliding window or list processing techniques can be used. In some
constellations this allows faster computation than via the Fourier domain.
Second, as iterations progress, computation of convolutions can be restricted
to subsets of pixels. For moderate thinning rates this can be done with almost
no impact on the reconstruction quality. Both approaches are demonstrated in
the context of Richardson-Lucy deconvolution but are not restricted to this
method.
