Medical imaging is crucial in modern clinics to provide guidance to the diagnosis and
treatment of diseases. Medical image reconstruction is one of the most fundamental and …

To date, frames have established themselves as a standard notion in applied mathematics,
computer science, and engineering as a means to derive redundant, yet stable …

The variational techniques (eg the total variation based method) are well established and
effective for image restoration, as well as many other applications, while the wavelet frame …

Sparsity-based regularization methods for image restoration assume that the underlying
image has a good sparse approximation under a certain system. Such a system can be a …

We propose a compressive sensing approach for multi-energy computed tomography (CT),
namely the prior rank, intensity and sparsity model (PRISM). To further compress the multi …

This paper presents a new approach for assembling graph neural networks based on
framelet transforms. The latter provides a multi-scale representation for graph-structured …

As a rapidly growing, multidisciplinary field of mathematics, wavelet theory provides the
major mathematical multiscale representation for analyzing functions/data and has …

The x-ray imaging dose from serial cone-beam computed tomography (CBCT) scans raises
a clinical concern in most image-guided radiation therapy procedures. It is the goal of this …

X-ray Computed Tomography (CT) is widely used in clinical applications such as diagnosis
and image-guided interventions. In this paper, we propose a new deep learning based …

In recent years, image processing in a Euclidean domain has been well studied. Practical
problems in computer vision and geometric modeling involve image data defined in irregular …