Hybrid, multi-frame and blind astronomical image deconvolution through ℓ 1 and ℓ 2 minimisation

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.