About Yuanhao Wang Yuanhao Wang Ph.D. Student, Electrical and Computer Engineering Computational Photography Computer Vision inverse problems Yuanhao Wang is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Yuanhao Wang obtained his bachelor's degree in Communication Engineering from Beijing University of Posts and Telecommunications in China in 2013. In 2016, he received his master's degree in Integrated Circuits Engineering from Tsinghua University in China. Research Interest Yuanhao’s research interests encompass a wide range of topics in computational photography Events Presented Events Sep 3 - Sep 9, 2023 Model-Based Computational Cryo-Electron Tomography Joint Reconstruction with Awareness of Noise Yuanhao Wang, Ph.D. Student, Electrical and Computer Engineering Sep 4, 17:30 - 19:00 B1 L2 R2202 Tilt-series cryo-electron tomography (cryo-ET) is an established imaging tech- nique used in several fields like biology and material science. Despite its success, cryo-ET remains an arduous task. The missing-wedge acquisition, the motion, and the high level noise are the main challenges existing in this field. In this dissertation, we tackle these challenges through the exploration of three distinct approaches: plug and play approach, adaptive differentiable density grids and adaptive tensorial density fields representation.
Model-Based Computational Cryo-Electron Tomography Joint Reconstruction with Awareness of Noise Yuanhao Wang, Ph.D. Student, Electrical and Computer Engineering Sep 4, 17:30 - 19:00 B1 L2 R2202 Tilt-series cryo-electron tomography (cryo-ET) is an established imaging tech- nique used in several fields like biology and material science. Despite its success, cryo-ET remains an arduous task. The missing-wedge acquisition, the motion, and the high level noise are the main challenges existing in this field. In this dissertation, we tackle these challenges through the exploration of three distinct approaches: plug and play approach, adaptive differentiable density grids and adaptive tensorial density fields representation.
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