In this paper we describe an investigation into the application of deep learning methods for
low-dose and sparse angle computed tomography using small training datasets. To motivate …

Underwater images suffer from degradation due to light scattering and absorption. It remains
challenging to restore such degraded images using deep neural networks since real-world …

We have developed a method to combine unsupervised and supervised deep-learning
approaches for seismic ground roll attenuation. The method consists of three components …

Photoacoustic tomography (PAT) images contain inherent distortions due to the imaging
system and heterogeneous tissue properties. Improving image quality requires the removal …

Single image generation (SIG), described as generating diverse samples that have the
same visual content as the given natural image, is first introduced by SinGAN, which builds a …

The manipulation and control of nanoscale magnetic spin textures are of rising interest as
they are potential foundational units in next-generation computing paradigms. Achieving this …

Phase retrieval (PR) concerns the recovery of complex phases from complex magnitudes.
We identify the connection between the difficulty level and the number and variety of …

Currently available methods for extracting saliency maps identify parts of the input which are
the most important to a specific fixed classifier. We show that this strong dependence on a …

The task of unsupervised image‐to‐image translation has seen substantial advancements in
recent years through the use of deep neural networks. Typically, the proposed solutions …

Image segmentation allows us to separate an image into distinct, non-overlapping parts by
utilizing specific features such as hue, texture, and shape. The technique is prevalent in …