As an emerging paradigm for signal acquisition and reconstruction, compressive sensing
(CS) achieves high-speed sampling and compression jointly and has found its way into …

In recent years, deep learning has achieved remarkable empirical success for image
reconstruction. This has catalyzed an ongoing quest for the precise characterization of the …

Plug-and-play (PnP) priors constitute one of the most widely used frameworks for solving
computational imaging problems through the integration of physical models and learned …

Limited-angle tomographic reconstruction is one of the typical ill-posed inverse problems,
leading to edge divergence with degraded image quality. Recently, deep learning has been …

Physics-driven deep learning methods have emerged as a powerful tool for computational
magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new …

A promising trend in deep learning replaces traditional feedforward networks with implicit
networks. Unlike traditional networks, implicit networks solve a fixed point equation to …

In low-level vision such as single image super-resolution (SISR), traditional MSE or L1 loss
function treats every pixel equally with the assumption that the importance of all pixels is the …

Magnetic particle imaging (MPI) offers unparalleled contrast and resolution for tracing
magnetic nanoparticles. A common imaging procedure calibrates a system matrix (SM) that …

The emergence of deep-learning-based methods to solve image-reconstruction problems
has enabled a significant increase in quality. Unfortunately, these new methods often lack …

This work is concerned with the following fundamental question in scientific machine
learning: Can deep-learning-based methods solve noise-free inverse problems to near …