High-Fidelity Imaging Using Compact Multi-Frame Blind Deconvolution

Abstract

Multi-frame blind deconvolution (MFBD) has been a cornerstone for ground-based space situational awareness of near-Earth satellites since the early 2000’s. In 2011 a variation of the classic MFBD algorithm was introduced that required solving for fewer variables than in the classic algorithm, but which still used all the available data to constrain the solution. The initial application of the new approach, referred to as compact multi-frame blind deconvolution (CMFBD), was found to be significantly faster than MFBD, and showed an indication that it may be able to provide restorations of higher quality, i.e. fewer artifacts. Since its introduction, the CMFBD approach has become the foundation of several MFBD-based algorithms that have been developed for applications such as high-accuracy wave front sensing from image plane data, and imaging through strong turbulence: both of which contribute to space situational awareness by increasing the area of sky available for surveillance. Here we show that the performance of the CMFBD approach can be improved through the addition of a new ”internal consistency” constraint on the estimated point-spread functions.