2 papers
cond-mat.stat-mech2021
Super-resolution of spin configurations based on flow-based generative models
Kenta Shiina, Lee Hwee Kuan, Hiroyuki Mori +2
We present a super-resolution method for spin systems using a flow-based generative model that is a deep generative model with reversible neural network architecture. Starting from…
eess.IV2020
Resolution enhancement and realistic speckle recovery with generative adversarial modeling of micro-optical coherence tomography
Kaicheng Liang, Xinyu Liu, Si Chen +4
A resolution enhancement technique for optical coherence tomography (OCT), based on Generative Adversarial Networks (GANs), was developed and investigated. GANs have been previousl…