Since their inception, computational homogenization methods based on the fast Fourier
transform (FFT) have grown in popularity, establishing themselves as a powerful tool …

Biomedical photoacoustic tomography, which can provide high-resolution 3D soft tissue
images based on optical absorption, has advanced to the stage at which translation from the …

Purpose To allow fast and high‐quality reconstruction of clinical accelerated multi‐coil MR
data by learning a variational network that combines the mathematical structure of …

This monograph covers some recent advances in a range of acceleration techniques
frequently used in convex optimization. We first use quadratic optimization problems to …

Regularization methods are a key tool in the solution of inverse problems. They are used to
introduce prior knowledge and allow a robust approximation of ill-posed (pseudo-) inverses …

While variational methods have been among the most powerful tools for solving linear
inverse problems in imaging, deep (convolutional) neural networks have recently taken the …

This paper studies first order methods for solving smooth minimax optimization problems
$\min_x\max_y g (x, y) $ where $ g (\cdot,\cdot) $ is smooth and $ g (x,\cdot) $ is concave for …

Inverse Problems in medical imaging and computer vision are traditionally solved using
purely model-based methods. Among those variational regularization models are one of the …

Considering the labeled samples may be difficult to obtain because they require human
annotators, special devices, or expensive and slow experiments. Semi-supervised learning …

Demand for low carbon energy storage has highlighted the importance of imaging
techniques for the characterization of electrode microstructures to determine key parameters …