Determining Multi-Frame Blind Deconvolution Resolvability using Deep Learning
Abstract
Astronomical images collected by ground-based telescopes suffer from degradation and perturbations attributed to atmospheric turbulence. The Multi-Frame 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 (on the order of hours to resolve a LEO pass) and currently can't be done in real-time. Because of this, optimal collection parameters cannot be adjusted for maximizing the likelihood of producing a resolved image with MFBD. In this paper we present a neural network that predicts whether an MFBD algorithm will be able to resolve a degraded image (or sequence of images) in real-time, and present experiments conducted on actual data collected at the Maui Space Surveillance Site.
- Publication:
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Advanced Maui Optical and Space Surveillance Technologies Conference
- Pub Date:
- September 2019
- Bibcode:
- 2019amos.confE..62K
- Keywords:
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- Deep learning;
- machine learning;
- multi-frame blind deconvolution