| dc.description.abstract | Optical systems (telescopes, lasers, microscopes,
etc.) have degraded performance over long distances
due to scintillation caused by Earth’s atmosphere,
where adaptive optics (AO) is often used to enhance
its signal-to-noise (SNR) ratio or image quality. Astronomers
have found success in laser-based adaptive
optics where they survey the atmosphere with a laser
and subtract its effects on the resultant image. Although
effective in most cases, these systems can be extremely
costly, are computationally intensive in real time, and
fall short in some edge cases. We propose an autoencoder/
decoder and a generalized sequence to sequence
model (LSTM) as a cost-effective method to off-load
computational complexity from real time and enhance
performance in edge cases. This study utilizes four
simulated datasets of wavefront sensor frames for a
variety of atmospheric conditions, done in collaboration
with MIT Lincoln Laboratory [1]–found auto-encoding
performance just shy of traditional methodology and
found LSTM performance that predicts well the general
shape on the WFS, but suffers from scaling issues. | en_US |
