Zobrazeno 1 - 10
of 391
pro vyhledávání: '"Zhou Qingping"'
In this paper, we study Bayesian approach for solving large scale linear inverse problems arising in various scientific and engineering fields. We propose a fused $L_{1/2}$ prior with edge-preserving and sparsity-promoting properties and show that it
Externí odkaz:
http://arxiv.org/abs/2409.07874
Physics-guided deep learning is an important prevalent research topic in scientific machine learning, which has tremendous potential in various complex applications including science and engineering. In these applications, data is expensive to acquir
Externí odkaz:
http://arxiv.org/abs/2312.12693
Bayesian inference with deep generative prior has received considerable interest for solving imaging inverse problems in many scientific and engineering fields. The selection of the prior distribution is learned from, and therefore an important repre
Externí odkaz:
http://arxiv.org/abs/2310.17817
Electrical Impedance Tomography (EIT) is a widely employed imaging technique in industrial inspection, geophysical prospecting, and medical imaging. However, the inherent nonlinearity and ill-posedness of EIT image reconstruction present challenges f
Externí odkaz:
http://arxiv.org/abs/2310.15831
Combining the strengths of model-based iterative algorithms and data-driven deep learning solutions, deep unrolling networks (DuNets) have become a popular tool to solve inverse imaging problems. While DuNets have been successfully applied to many li
Externí odkaz:
http://arxiv.org/abs/2307.16120
Electrical Impedance Tomography (EIT) is widely applied in medical diagnosis, industrial inspection, and environmental monitoring. Combining the physical principles of the imaging system with the advantages of data-driven deep learning networks, phys
Externí odkaz:
http://arxiv.org/abs/2304.14491
Autor:
Cheng, Chen, Zhou, Qingping
This paper investigates the application of unsupervised learning methods for computed tomography (CT) reconstruction. To motivate our work, we review several existing priors, namely the truncated Gaussian prior, the $l_1$ prior, the total variation p
Externí odkaz:
http://arxiv.org/abs/2304.03895
Autor:
Zhao, Qiao, Yang, Jianzan, Xiang, Huawei, Dong, Jianhua, Li, Yue, Zhou, Qingping, Song, Xiong, Wei, Chong
Publikováno v:
In Journal of Hydrology: Regional Studies December 2024 56
Autor:
Peng, Jinlong, Zhang, Ruiyang, Ma, Fangfang, Quan, Quan, Liao, Jiaqiang, Zhou, Qingping, Niu, Shuli
Publikováno v:
In Agricultural and Forest Meteorology 15 November 2024 358
Autor:
Zhou, Qingping, Liu, Siyuan, Chen, Jiangyun, Tuersun, Yusupujiang, Liang, Zhenning, Wang, Chenxi, Sun, Jinhai, Yuan, Lei, Qian, Yi
Publikováno v:
In Journal of Affective Disorders 1 October 2024 362:569-577