Yoseob Han

TL;DR: This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution.

TL;DR: Recently, deep learning approaches using large receptive field neural networks such as U-Net have demonstrated impressive performance for sparse-view CT reconstruction as discussed by the authors, however, theoretical justification is still lacking, and the main goal of this paper is to reveal the limitation of U-net and propose new multi-resolution deep learning schemes.

TL;DR: Recently, deep learning approaches with various network architectures have achieved significant performance improvement over existing iterative reconstruction methods in various imaging problems.

TL;DR: Wang et al. as discussed by the authors proposed a fully data-driven deep learning algorithm for space interpolation, which can be also easily applied to non-Cartesian trajectories by adding an additional regridding layer.

TL;DR: A novel deep learning approach with domain adaptation is proposed to restore high‐resolution MR images from under‐sampled k‐space data to solve the problem of streaking artifact patterns in magnetic resonance imaging.