Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …

This paper reviewed the deep learning‐based studies for medical imaging synthesis and its
clinical application. Specifically, we summarized the recent developments of deep learning …

Underwater images are subject to intricate and diverse degradation inevitably affecting the
effectiveness of underwater visual tasks. However most approaches primarily operate in the …

This paper tries to give a gentle introduction to deep learning in medical image processing,
proceeding from theoretical foundations to applications. We first discuss general reasons for …

Commercial iterative reconstruction techniques help to reduce the radiation dose of
computed tomography (CT), but altered image appearance and artefacts can limit their …

Recent style transfer models have provided promising artistic results. However, given a
photograph as a reference style, existing methods are limited by spatial distortions or …

Over past several years, machine learning, or more generally artificial intelligence, has
generated overwhelming research interest and attracted unprecedented public attention. As …

X-ray computed tomography (CT) using sparse projection views is a recent approach to
reduce the radiation dose. However, due to the insufficient projection views, an analytic …

This paper proposes a novel and fast self-supervised solution for sparse-view CBCT
reconstruction (Cone Beam Computed Tomography) that requires no external training data …

The field of medical image reconstruction has seen roughly four types of methods. The first
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …