  We consider the simultaneous deblurring of a set of noisy images whose point
spread functions are different but known and spatially invariant, and the noise
is Gaussian. Currently available iterative algorithms that are typically used
for this type of problem are computationally expensive, which makes their
application for very large images impractical. We present a simple extension of
a classical least-squares (LS) method where the multi-image deblurring is
efficiently reduced to a computationally efficient single-image deblurring. In
particular, we show that it is possible to remarkably improve the
ill-conditioning of the LS problem by means of stable operations on the
corresponding normal equations, which in turn speed up the convergence rate of
the iterative algorithms. The performance and limitations of the method are
analyzed through numerical simulations. Its connection with a column weighted
least-squares approach is also considered in an appendix.
