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 …

Training deep neural networks has become a common approach for addressing image
restoration problems. An alternative for training a" task-specific" network for each …

This article presents a new method for reconstructing regions of interest (ROI) from a limited
number of computed tomography (CT) measurements. Classical model-based iterative …

Deep neural networks are a very powerful tool for many computer vision tasks, including
image restoration, exhibiting state-of-the-art results. However, the performance of deep …

Ill-posed inverse problems appear in many image processing applications, such as
deblurring and super-resolution. In recent years, solutions that are based on deep …

The choice of the sensing matrix is crucial in compressed sensing. Random Gaussian
sensing matrices satisfy the restricted isometry property, which is crucial for solving the …

In recent years, it has become popular to tackle image restoration tasks with a single
pretrained diffusion model (DM) and data-fidelity guidance, instead of training a dedicated …

Training deep neural networks has become a common approach for addressing image
restoration problems. An alternative for training a" task-specific" network for each …

Image restoration involves solving an optimization problem where the objective function is
the sum of a data-fidelity term and a regularization functional that incorporates a desired …

In this article, we develop a novel reconstruction method that uses sophisticated denoising
priors to recover the missing seismic data. First, we construct a regularization item defined …