  This paper demonstrates a practical method that can correct spatial varying
blur from a set of images of the same object. The algorithm jointly estimates
the object and local point spread functions~(PSF). The method prioritizes
sections with small spatial variation in the PSF for deconvolution. This novel
approach can handle large translations in the local PSFs, hence the algorithm
is able to correct for morph in the images. Robustness to noise is demonstrated
in numerical simulations. Numerical experiments are conducted where the
performance of the algorithm is compared to a state-of-the-art method found in
literature. The algorithm can be used in situation with space-temporal
variation of the PSF and can be applied in situations where the signal-to-noise
ratio is low.
