In this paper we propose a new joint model for the reconstruction of tomography data under
limited angle sampling regimes. In many applications of tomography, eg electron …

X-ray computed tomography is a widely used, non-destructive imaging technique that
computes cross-sectional images of an object from a set of X-ray absorption profiles (the so …

In this paper, we propose a sinogram inpainting network (SIN) to solve limited-angle CT
reconstruction problem, which is a very challenging ill-posed issue and of great interest for …

Limited-angle computed tomography (CT) image reconstruction is a challenging
reconstruction problem in the fields of CT. With the development of deep learning, the …

In CT (computed tomography), reconstruction from undersampling projection data is often ill-
posed and suffers from severe artifact in the reconstructed images. To overcome this …

This paper aims to reduce the problems of incomplete data in computed tomography, which
happens frequently in medical image process and analysis, eg, when the high-density …

In this paper, we consider the problem of feature reconstruction from incomplete X-ray CT
data. Such incomplete data problems occur when the number of measured X-rays is …

Projection incompleteness in x-ray computed tomography (CT) often relates to sparse
sampling or detector gaps and leads to degraded reconstructions with severe streak and …

Limited-angle computed tomography is a very challenging problem in applications. Due to a
high degree of ill-posedness, conventional reconstruction algorithms will introduce blurring …

In this paper, we present a novel approach based on deep neural network for solving the
limited angle tomography problem. The limited angle views in tomography cause severe …