Zobrazeno 1 - 10
of 78
pro vyhledávání: '"Liang, Shijun"'
Deep learning (DL) methods have been extensively applied to various image recovery problems, including magnetic resonance imaging (MRI) and computed tomography (CT) reconstruction. Beyond supervised models, other approaches have been recently explore
Externí odkaz:
http://arxiv.org/abs/2410.04482
Autor:
Alkhouri, Ismail, Liang, Shijun, Huang, Cheng-Han, Dai, Jimmy, Qu, Qing, Ravishankar, Saiprasad, Wang, Rongrong
Diffusion models (DMs) are a class of generative models that allow sampling from a distribution learned over a training set. When applied to solving inverse imaging problems (IPs), the reverse sampling steps of DMs are typically modified to approxima
Externí odkaz:
http://arxiv.org/abs/2410.04479
The ability of deep image prior (DIP) to recover high-quality images from incomplete or corrupted measurements has made it popular in inverse problems in image restoration and medical imaging including magnetic resonance imaging (MRI). However, conve
Externí odkaz:
http://arxiv.org/abs/2402.04097
Autor:
Liang, Shijun, Nguyen, Van Hoang Minh, Jia, Jinghan, Alkhouri, Ismail, Liu, Sijia, Ravishankar, Saiprasad
As the popularity of deep learning (DL) in the field of magnetic resonance imaging (MRI) continues to rise, recent research has indicated that DL-based MRI reconstruction models might be excessively sensitive to minor input disturbances, including wo
Externí odkaz:
http://arxiv.org/abs/2312.07784
Deep learning (DL) techniques have been extensively employed in magnetic resonance imaging (MRI) reconstruction, delivering notable performance enhancements over traditional non-DL methods. Nonetheless, recent studies have identified vulnerabilities
Externí odkaz:
http://arxiv.org/abs/2309.05794
Although deep learning (DL) has gained much popularity for accelerated magnetic resonance imaging (MRI), recent studies have shown that DL-based MRI reconstruction models could be oversensitive to tiny input perturbations (that are called 'adversaria
Externí odkaz:
http://arxiv.org/abs/2303.12735
Autor:
Li, Yiwei, Zhang, Shihao, Chen, Fanqiang, Wei, Liyang, Zhang, Zonglin, Xiao, Hanbo, Gao, Han, Chen, Moyu, Liang, Shijun, Pei, Ding, Xu, Lixuan, Watanabe, Kenji, Taniguchi, Takashi, Yang, Lexian, Miao, Feng, Liu, Jianpeng, Cheng, Bin, Wang, Meixiao, Chen, Yulin, Liu, Zhongkai
Moir\'e superlattices that consist of two or more layers of two-dimensional materials stacked together with a small twist angle have emerged as a tunable platform to realize various correlated and topological phases, such as Mott insulators, unconven
Externí odkaz:
http://arxiv.org/abs/2209.02199
Adaptive Local Neighborhood-based Neural Networks for MR Image Reconstruction from Undersampled Data
Recent medical image reconstruction techniques focus on generating high-quality medical images suitable for clinical use at the lowest possible cost and with the fewest possible adverse effects on patients. Recent works have shown significant promise
Externí odkaz:
http://arxiv.org/abs/2206.00775
Autor:
Liu, Xiaowei, Wang, Yaojia, Guo, Qiqi, Liang, Shijun, Xu, Tao, Liu, Bo, Qiao, Jiabin, Lai, Shengqiang, Zeng, Junwen, Hao, Song, Gu, Chenyi, Cao, Tianjun, Wang, Chenyu, Wang, Yu, Pan, Chen, Su, Guangxu, Nie, Yuefeng, Wan, Xiangang, Sun, Litao, Wang, Zhenlin, He, Lin, Cheng, Bin, Miao, Feng
Defect engineering plays an important role in tailoring the electronic transport properties of van der Waals materials. However, it is usually achieved through tuning the type and concentration of defects, rather than dynamically reconfiguring their
Externí odkaz:
http://arxiv.org/abs/2104.06642
Autor:
Huang, Wei, Dong, Erxiang, Cheng, Yu, Liu, Songyi, Liang, Shijun, Yang, Yuping, Yin, Shan, Zhang, Wentao
The surface plasmon-polaritons (SPPs) switch is the key element of the integrated devices in optical computation and terahertz (THz) communications. In this paper, we propose a novel design of THz SPPs switch based on quantum engineering. Due to the
Externí odkaz:
http://arxiv.org/abs/2101.02852