Multi-Frame Blind Deconvolution Accelerated with Graphical Processing Units (GPUs)

Ground-based observations of Low Earth Orbit (LEO) satellites are significantly degraded by atmospheric turbulence. Multi-Frame Blind Deconvolution (MFBD) is a class of algorithm that attempts to solve this ill-posed inverse problem using a forward model approach. It assumes that a Point Spread Function (PSF) is convolved with a pristine image of the target and then iteratively solves for both simultaneously. Processing one full LEO collection in this way requires an extraordinary amount of processing power and is a task that is normally performed with supercomputers to minimize processing time. In this paper, we describe a new MFBD implementation written with NVidias Compute Unified Device Architecture (CUDA) language to make use of the incredible parallelization capabilities of graphical processing units (GPUs). This reduces the scope of hardware capable of achieving the same processing time from a supercomputer-scale system to a single desktop or server. Measurements of processing time and image quality for several reference objects are compared against results from the latest version of Physically Constrained Iterative Deconvolution (PCID), a gold-standard MFBD implementation that uses the Message Passing Interface (MPI) to make use of numerous CPUs in high-performance computing environments.

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