Jong Chul Ye

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: A new public domain statistical toolbox known as NirS-SPM is described, which enables the quantitative analysis of NIRS signal and enables the calculation of activation maps of oxy-, deoxy-hemoglobin and total hemoglobin, and allows for the super-resolution localization, which is not possible using conventional analysis tools.

TL;DR: An extension of k‐t FOCUSS to a more general framework with prediction and residual encoding, where the prediction provides an initial estimate and the residual encoding takes care of the remaining residual signals.

TL;DR: Experimental results show that the proposed patch-based convolutional neural network approach achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.

TL;DR: This work proposes an algorithm which uses a deep convolutional neural network which is applied to the wavelet transform coefficients of low‐dose CT images and effectively removes complex noise patterns from CT images derived from a reduced X‐ray dose.