2026-10-12T23:47:43Z https://www.scidb.cn/oai

10.57760/sciencedb.36085 2026-01-26T11:15:41Z

2026-01-26 An imaging algorithm based on generalized minimax-concave penalty in radio interferometry doi:10.57760/sciencedb.36085 en  Reconstructing the sky brightness distribution from the incomplete visibilities involves an ill-posed inverse problem. Although compressive sensing methods based on convex optimization have demonstrated outstanding performance in radio interferometry, convex optimization yields a computable but biased approximate solution for compressive sensing. To reduce the bias and efficiently obtain an accurate solution, we proposed an imaging algorithm based on generalized minimax-concave penalty (GMCP), which maintains the convexity of the sparsity-regularized least squares objective function. Furthermore, we employ the forward-backward splitting algorithm to solve the optimization problem and adaptively update the regularization parameter by using a maximum likelihood estimator. We have verified the effectiveness of the proposed method based on the Very Large Array (VLA) and DAocheng Radio Telescope (DART). interferometers; image processing; numerical Yang Xiaocheng Wu Gaofeng Wu Lin Yan Jingye PUBLIC https://api.github.com/licenses/mit dataset http://www.doi.org/10.3847/1538-3881/ae45a1 Science Data Bank