Next-generation radio telescopes such as the Square Kilometer Array (SKA) will observe the sky with unprecedented resolution, sensitivity, and survey speed. However, this precise instrument will demand reliable, precise, and high dynamic range deconvolution techniques to form images. The popular CLEAN algorithm, while efficient, often produces images of suboptimal quality. In recent years convex and nonconvex optimization algorithms have been demonstrated to produce images with superior quality, but at the cost of efficiency and scalability. Deep learning solutions offer a compromise between speed and quality, but at the cost of reliability and generalizability. The proposed project will leverage the expertise between the astronomy and signal processing research groups at EPFL to develop an end-to-end imaging solution that is precise, robust, and scalable. Researchers will develop a Bayesian Neural Network with dedicated architecture to ensure data fidelity and trained on dedicated physical high dynamic range databases. They expect that such a network will deliver scaling to unprecedented image sizes, while bridging the gap with optimisation approaches in terms of enabling high resolution high dynamic range imaging and interpretable solutions.
