no code implementations • 27 Mar 2024 • Chunhui Xu, Jason T. L. Wang, Haimin Wang, Haodi Jiang, Qin Li, Yasser Abduallah, Yan Xu
Image super-resolution has been an important subject in image processing and recognition.
no code implementations • 4 Nov 2022 • Haodi Jiang, Qin Li, Zhihang Hu, Nian Liu, Yasser Abduallah, Ju Jing, Genwei Zhang, Yan Xu, Wynne Hsu, Jason T. L. Wang, Haimin Wang
We propose a new deep learning method, named MagNet, to learn from combined LOS magnetograms, Bx and By taken by SDO/HMI along with H-alpha observations collected by the Big Bear Solar Observatory (BBSO), and to generate vector components Bx' and By', which would form vector magnetograms with observed LOS data.
no code implementations • 8 Oct 2022 • Haodi Jiang, Qin Li, Yan Xu, Wynne Hsu, Kwangsu Ahn, Wenda Cao, Jason T. L. Wang, Haimin Wang
Obtaining high-quality magnetic and velocity fields through Stokes inversion is crucial in solar physics.
no code implementations • 16 Jul 2021 • Haodi Jiang, Ju Jing, Jiasheng Wang, Chang Liu, Qin Li, Yan Xu, Jason T. L. Wang, Haimin Wang
Our method consists of a data pre-processing component that prepares training data from a threshold-based tool, a deep learning model implemented as a Bayesian convolutional neural network for probabilistic image segmentation with uncertainty quantification to predict fibrils, and a post-processing component containing a fibril-fitting algorithm to determine fibril orientations.
4 code implementations • 27 Aug 2020 • Haodi Jiang, Jiasheng Wang, Chang Liu, Ju Jing, Hao liu, Jason T. L. Wang, Haimin Wang
Deep learning has drawn a lot of interest in recent years due to its effectiveness in processing big and complex observational data gathered from diverse instruments.