Enhanced Image Reconstruction with Quality-Weighted Iterative Deconvolution (QWID)

Multi-frame blind deconvolution (MFBD) algorithms are able to produce high-resolution image reconstructions from severely degraded inputs. Often these algorithms are designed with a number of assumptions about the observing scenario and the data quality, and when these assumptions are violated the reconstruction quality can suffer. However, it can be challenging to automatically assign quality scores to input images for data rejection, especially while observing Low Earth Orbit (LEO) satellites through the turbulent atmosphere as they transit across a large patch of the sky. We report on an algorithm that uses a convolutional neural network (CNN) to assign quality scores to images prior to MFBD processing in a system titled Quality-Weighted Iterative Deconvolution (QWID). This quality assessment represents the likelihood that each input frame can contribute meaningful signal to the frame reconstruction process. The neural network is trained on a simulated dataset of ground-based observations of LEO satellites, where true quality is known. Improvements in performance over the same MFBD implementation in the absence of quality scores are demonstrated on a subset of these simulated observations.