Spatially Separable Blind Deconvolution of Long Exposure Astronomical Imagery

In this thesis, a spatially separable blind deconvolution algorithm is demonstrated that achieves a significantly faster processing time and superior sensitivity when processing long-exposure image data of unresolvable objects from a ground-based telescope. The proposed approach takes advantage of the structure of the long exposure point spread functions radial symmetric characteristics to approximate it as a product of one dimensional horizontal and vertical intensity distributions. Objects at geosynchronous or geostationary orbit also can be well approximated as being spatially separable as they are, in general non-resolvable. The algorithms performance is measured by computing the mean-squared error compared with the true object as well as the processing time required to perform the blind deconvolution. It will be shown that images processed by the proposed technique will possess, on average, a lower mean-squared error than images that are processed through the traditional two-dimensional blind deconvolution approach. In addition, the one dimensional will be shown to perform the deconvolution significantly faster. In both cases the seeing parameter, and thus the point spread function, is treated as an unknown variable in the image reconstruction problem