3 code implementations • 13 Oct 2022 • Yanbo Zhang, Sara Imari Walker
By demonstrating a general theory for how macroscopic properties emerge from conservation of symmetries in the mapping between observations, we provide a machine learning framework that allows a unified approach to identifying macrostates in systems from the simple to complex, and allows the design of new examples consistent with a given macroscopic property.
no code implementations • CVPR 2021 • Yuxing Tang, Zhenjie Cao, Yanbo Zhang, Zhicheng Yang, Zongcheng Ji, Yiwei Wang, Mei Han, Jie Ma, Jing Xiao, Peng Chang
Starting with a fully supervised model trained on the data with pixel-level masks, the proposed framework iteratively refines the model itself using the entire weakly labeled data (image-level soft label) in a self-training fashion.
no code implementations • 6 May 2019 • Weiwen Wu, Haijun Yu, Peijun Chen, Fulin Luo, Fenglin Liu, Qian Wang, Yining Zhu, Yanbo Zhang, Jian Feng, Hengyong Yu
Second, we employ the direct inversion (DI) method to obtain initial material decomposition results, and a set of image patches are extracted from the mode-1 unfolding of normalized material image tensor to train a united dictionary by the K-SVD technique.
no code implementations • 24 Jul 2018 • Weiwen Wu, Fenglin Liu, Yanbo Zhang, Qian Wang, Hengyong Yu
Then, as a new regularizer, Kronecker-Basis-Representation (KBR) tensor factorization is employed into a basic spectral CT reconstruction model to enhance the ability of extracting image features and protecting spatial edges, generating the non-local low-rank cube-based tensor factorization (NLCTF) method.
no code implementations • 13 Dec 2017 • Weiwen Wu, Yanbo Zhang, Qian Wang, Fenglin Liu, Peijun Chen, Hengyong Yu
The L0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT images.
9 code implementations • 3 Aug 2017 • Qingsong Yang, Pingkun Yan, Yanbo Zhang, Hengyong Yu, Yongyi Shi, Xuanqin Mou, Mannudeep K. Kalra, Ge Wang
In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity.
no code implementations • 22 Aug 2014 • Yanbo Zhang, Xuanqian Mou
The results of three patients' CT images show that the proposed method can reduce metal artifacts remarkably.