  Numerous recent approaches attempt to remove image blur due to camera shake,
either with one or multiple input images, by explicitly solving an inverse and
inherently ill-posed deconvolution problem. If the photographer takes a burst
of images, a modality available in virtually all modern digital cameras, we
show that it is possible to combine them to get a clean sharp version. This is
done without explicitly solving any blur estimation and subsequent inverse
problem. The proposed algorithm is strikingly simple: it performs a weighted
average in the Fourier domain, with weights depending on the Fourier spectrum
magnitude. The method can be seen as a generalization of the align and average
procedure, with a weighted average, motivated by hand-shake physiology and
theoretically supported, taking place in the Fourier domain. The method's
rationale is that camera shake has a random nature and therefore each image in
the burst is generally blurred differently. Experiments with real camera data,
and extensive comparisons, show that the proposed Fourier Burst Accumulation
(FBA) algorithm achieves state-of-the-art results an order of magnitude faster,
with simplicity for on-board implementation on camera phones. Finally, we also
present experiments in real high dynamic range (HDR) scenes, showing how the
method can be straightforwardly extended to HDR photography.
