Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography
This work provides a fast and theoretically grounded solver for TV regularization, addressing the computational bottleneck in large-scale inverse problems for imaging applications.
The paper presents an efficient semismooth* Newton method for TV-regularized linear inverse problems, achieving locally superlinear convergence and outperforming state-of-the-art methods on large-scale tomographic imaging tasks.
In this paper, we consider the efficient numerical minimization of Tikhonov functionals resulting from total-variation (TV) regularization of linear inverse problems. Since the TV penalty is non-smooth, this is typically done either via smooth approximations, which are inexact, or using non-smooth optimization techniques, which can often be numerically expensive, in particular for large-scale problems. Here, we present a numerically efficient minimization approach based on the recently proposed semismooth* Newton method, which employs a novel concept of graphical derivatives and exhibits locally superlinear convergence. The proposed approach is specifically tailored to TV regularization, suitable for large-scale inverse problems, and supported by strong mathematical convergence guarantees. Furthermore, we demonstrate its performance on two (large-scale) tomographic imaging problems and compare our results to those obtained via other state-of-the-art TV regularization approaches.