With the explosion in the number of digital images taken every day, the demand for more
accurate and visually pleasing images is increasing. However, the images captured by …

CT imaging is widely used in medical science over the last decades. The process of CT
image reconstruction depends on many physical measurements such as radiation dose …

We introduce a model-based image reconstruction framework with a convolution neural
network (CNN)-based regularization prior. The proposed formulation provides a systematic …

Purpose To develop a strategy for training a physics‐guided MRI reconstruction neural
network without a database of fully sampled data sets. Methods Self‐supervised learning via …

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 …

Multispectral remote sensing images often suffer from the common problem of stripe noise,
which greatly degrades the imaging quality and limits the precision of the subsequent …

Purpose To study and reduce the effect of Gibbs ringing artifact on computed diffusion
parameters. Methods We reduce the ringing by extrapolating the k‐space of each diffusion …

Regularized iterative reconstruction algorithms for imaging inverse problems require
selection of appropriate regularization parameter values. We focus on the challenging …

Image restoration is one of the most fundamental issues in imaging science. Total variation
regularization is widely used in image restoration problems for its capability to preserve …

This paper seeks to combine linear time-invariant (LTI) filtering and sparsity-based
denoising in a principled way in order to effectively filter (denoise) a wider class of signals …