Non-convex sparse regularization for radio interferometric imaging via smoothly clipped absolute deviation

The proposed method utilizes the continuous SCAD penalty function to approximate the L0-norm and efficiently solves the non-convex optimization problem by using an improved proximal gradient algorithm. The improved proximal gradient algorithm introduces a restart strategy and an adaptive non-monotonic step size strategy to improve the convergence speed of the algorithm. Besides, the regularization parameter is adaptively updated by using the prior information of the image.Results: Numerical simulation experiments are carried out on the Very Large Array (VLA) and Square Kilometre Array (SKA).

We compare the proposed method with state-of-the-art imaging methods. The results show that it performs better in terms of reconstruction quality and computational efficiency.