The performance of a modern multi-frame blind deconvolution (MFBD) technique is characterized versus Strehl ratio utilizing a wave-optics simulation. Modern MFBD performs a nonlinear optimization of the likelihood of the data by varying the underlying image, constrained by positivity and support constraints. Because such algorithms are not believed to be amenable to analysis, the characterization is performed utilizing a wave-optics simulation with varying turbulence strengths, a variety of adaptive optics (AO) configurations in up-looking geometries, and diverse objects. The imaging performance of the MFBD algorithm is characterized in terms of normalized cross-correlation. The results indicate that utilizing both MFBD and low-resolution AO can provide acceptable images that are up to 3 Mv dimmer than either MFBD alone or high-resolution AO alone.
Astronomical images collected by ground-based telescopes suffer from degradation and perturbations attributed to atmospheric turbulence. We investigate the application of convolutional neural networks (CNNs) to ground-based satellite imaging to address two existing problems. First, the multiframe blind deconvolution (MFBD) algorithms that can extract well-resolved images from these degraded data frames are computationally expensive, requiring supercomputing infrastructure for relatively fast performance and currently cannot be done in real time. Because of this, it is difficult to optimize collection parameters to maximize the likelihood of producing a resolved image with MFBD. Second, the space-object National Imagery Interpretability Rating Scale (SNIIRS) allows human analysts to provide a quantitative score of image quality based on identification of target features. It is naturally difficult to automate this scoring process, not only because the scale is based on identifiable features but also because the images may be in an almost-resolved image quality regime that is difficult to handle for traditional computer vision techniques. For both applications, we present our results using CNNs on data collected at the Maui Space Surveillance Site as well as a new synthetic dataset we introduce containing over a million SNIIRS rated pairings of perturbed and pristine ground-based satellite images.