no code implementations • 15 Dec 2021 • Yinan He, Lu Sheng, Jing Shao, Ziwei Liu, Zhaofan Zou, Zhizhi Guo, Shan Jiang, Curitis Sun, Guosheng Zhang, Keyao Wang, Haixiao Yue, Zhibin Hong, Wanguo Wang, Zhenyu Li, Qi Wang, Zhenli Wang, Ronghao Xu, Mingwen Zhang, Zhiheng Wang, Zhenhang Huang, Tianming Zhang, Ningning Zhao
The rapid progress of photorealistic synthesis techniques has reached a critical point where the boundary between real and manipulated images starts to blur.
Motivated by the superior performance reported by renowned region based CNN, in the second stage, another 3D U-Net is trained on the candidate region generated in the first stage.
This paper proposes a novel framework to reconstruct the dynamic magnetic resonance images (DMRI) with motion compensation (MC).
Specifically, an analytical solution can be obtained and implemented efficiently for the Gaussian prior or any other regularization that can be formulated into an $\ell_2$-regularized quadratic model, i. e., an $\ell_2$-$\ell_2$ optimization problem.
Thus, we propose a GGD-Potts model defined by a label map coupling US image segmentation and deconvolution.