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 …

Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …

In recent years, deep learning (DL) is becoming an increasingly important tool for solving
inverse scattering problems (ISPs). This paper reviews methods, promises, and pitfalls of …

Super resolution problems are widely discussed in medical imaging. Spatial resolution of
medical images are not sufficient due to the constraints such as image acquisition time, low …

In this work we introduce a novel stochastic algorithm dubbed SNIPS, which draws samples
from the posterior distribution of any linear inverse problem, where the observation is …

Deep learning is becoming an increasingly important tool for image reconstruction in
fluorescence microscopy. We review state-of-the-art applications such as image restoration …

Inverse problems are essential to imaging applications. In this letter, we propose a model-
based deep learning network, named FISTA-Net, by combining the merits of interpretability …

It is a challenging research topic to identify plant disease based on diseased leaf image
processing techniques due to the complexity of the diseased leaf images. Deep learning …

Optical imaging has had a central role in elucidating the underlying biological and
physiological mechanisms in living specimens owing to its high spatial resolution, molecular …

Deep learning (DL) has had unprecedented success and is now entering scientific
computing with full force. However, current DL methods typically suffer from instability, even …