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| Paper: |
Hybrid, Multi-frame and Blind Astronomical Image Deconvolution Through ℓ1 and ℓ2Minimisation |
| Volume: |
512, Astronomical Data Analysis Software and Systems XXV |
| Page: |
469 |
| Authors: |
Gauci, A.; Abela, J.; Cachia, E.; Hirsch, M.; Adami, K. Z. |
| Abstract: |
The study of images in scientific fields such as remote sensing,
medical imaging and astronomy comes naturally not only because
pictures mimic one of the main sensory elements of humans, but
also because they allow for the visualisation of wavelengths
beyond the sensitive range of the human eye. However, accurate
information extraction from images is only possible if the data
are known to be free of noise, blur and artificial artifacts. In
astronomical images, apart from hardware limitations, biases
arise from image degradation caused by phenomena beyond one's
control such as, for instance, atmospheric and ionospheric
turbulence. Deconvolution attempts to undo such
adverse effects and recover the true intensity values from
measured ones. Having a robust and accurate deconvolution algorithm
is very important especially for large-scale telescopes such as
the Square Kilometre Array (SKA) through which sensitive
investigations including gravitational lensing research and the
detection of faint sources are to be made. In this work, we
investigate the improvements gained if an ensemble of
algorithms is used to minimise the overall restoration
error. We present a blind deconvolution method that is
able to process multiple frames and yields improved
results when compared to the state-of-the-art. |
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