Implementing the Daylight Object Restoration Algorithm for Imaging in Daylight and Strong Turbulence

ABSTRACT:A new algorithm, the Daylight Object Restoration Algorithm (DORA), has been developed. DORA includes a novel implementation of the multi-frame blind deconvolution image restoration concept which recovers high-quality object estimates from sequences of degraded images. DORA removes the effects of atmospheric aberration in images recorded at ground-based telescopes, and is designed to operate under seeing conditions much more severe than any previous approach can address. We have demonstrated that DORA restores high quality object estimates in seeing as bad as D/r0=70 (D is the telescope diameter and r0 is the Fried parameter). This is more than three times worse than the seeing at which conventional techniques begin to fail.We will implement DORA as a parallel code on a supercomputing platform at the MHPCC. We will begin with an integrated version of DORA for a single processor, and continue to the parallel version. Both will be tested with simulated and real data, to include data from the Maui Space Surveillance System. We will develop the parallel code in a manner that will facilitate its deployment on a variety of computing platforms. This will support the range of commercialization targets that have been identified, which will have unique hardware requirements.BENEFIT:Benefits to the government are the ability to make high resolution images of satellites from ground-based optical surveillance facilities even in daylight conditions. This dramatically reduces the typical wait time to acquire an observation of a particular satellite since it relieves the requirement to wait for a particular geometrical configuration with respect to the sun. Commercial applications include long-path horizontal imaging; post-processing of astronomical observations recorded with partial adaptive optics correction, thus allowing sharper images at shorter wavelengths; image sharpening in the movie industry.