Parallelization and Automation of a Blind Deconvolution Algorithm

Often it is of interest to deblur imagery in order to obtain higher-resolution images. Deblurring requires knowledge of the blurring function - information that is often not available separately from the blurred imagery. Blind deconvolution algorithms overcome this problem by jointly estimating both the high-resolution image and the blurring function from the blurred imagery. Because blind deconvolution algorithms are iterative in nature, they can take minutes to days to deblur an image depending how many frames of data are used for the deblurring. Here we present our progress in parallelizing a blind deconvolution algorithm to increase its execution speed. This progress includes sub-frame parallelization and a code structure that is not specialized to any specific computer hardware architecture. We also describe briefly our progress in automating algorithm parameter selection