This paper surveys an important class of methods that combine iterative projection methods
and variational regularization methods for large-scale inverse problems. Iterative methods …

In this work, we describe a new approach that uses deep neural networks (DNN) to obtain
regularization parameters for solving inverse problems. We consider a supervised learning …

We devise efficient methods for dynamic inverse problems, where both the quantities of
interest and the forward operator (measurement process) may change in time. Our goal is to …

Cryo-electron microscopy single particle analysis (SPA) represents a vital tool for structure
determination of macromolecules. Discrete inverse problems arising in this field are …

Miniaturized computational spectrometers are opto-electronic instruments that can measure
the intensity of light as a function of its wavelength, providing valuable information for …

Parameter selection is crucial to regularization-based image restoration methods. Generally
speaking, a spatially fixed parameter for the regularization term does not perform well for …

In this paper, we propose a novel model for the decomposition of cartoon–texture images,
which integrates the edge-aware weighted least squares (WLS) with low-rank regularization …

Dynamic inverse problems are challenging to solve due to the need to identify and
incorporate appropriate regularization in both space and time. Moreover, the very large …

Robust Principal Component Analysis (RPCA) is a fundamental technique for decomposing
data into low-rank and sparse components, which plays a critical role for applications such …

Many successful variational regularization methods employed to solve linear inverse
problems in imaging applications (such as image deblurring, image inpainting, and …