PatchNR: learning from very few images by patch normalizing flow regularization
Sign Up to like & getrecommendations! Published in 2022 at "Inverse Problems"
DOI: 10.1088/1361-6420/acce5e
Abstract: Learning neural networks using only few available information is an important ongoing research topic with tremendous potential for applications. In this paper, we introduce a powerful regularizer for the variational modeling of inverse problems in… read more here.
Keywords: normalizing flow; patchnr learning; images patch; patch normalizing ... See more keywords
Normalizing Flow-Based Probability Distribution Representation Detector for Hyperspectral Anomaly Detection
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2022.3182538
Abstract: Due to the powerful reconstruction ability, deep learning based hyperspectral anomaly detection methods have been prevalent in recent years. However, the capability of neural networks and the meaning of latent space remains unexplainable to some… read more here.
Keywords: normalizing flow; hyperspectral anomaly; detection; anomaly detection ... See more keywords
MRIFlow: Magnetic resonance image super-resolution based on normalizing flow and frequency prior.
Sign Up to like & getrecommendations! Published in 2023 at "Journal of magnetic resonance"
DOI: 10.2139/ssrn.4334855
Abstract: Super-resolution (SR) is a computer vision task that involves recovering high-resolution (HR) images from low-resolution (LR) ones. While SR is applied to various disciplines, it is particularly important in the medical field which requires accurate… read more here.
Keywords: magnetic resonance; normalizing flow; frequency; resolution ... See more keywords