Images are one of the most useful forms of data in our daily lives, and rapid progress in imaging technologies has resulted in an explosion in the number of images captured. Due to the complex structure of images, it is difficult to develop a universal mathematical theory for solving real-world imaging problems. Recently, many data-driven approaches including dictionary learning, edge-driven methods and deep learning methods have demonstrated state-of-the-art performance in various imaging tasks. This mini-symposium aims to share and explore recent progress in these data-driven methods from both theoretical and practical perspectives.

Deep Learning for Model-Based Iterative CT Reconstruction using the Plug-&-Play Framework

Charles Bouman (Purdue University)

Wafer Defect Detection Algorithm Using Deep Learning Algorithms

Myung Joo Kang (Department of Mathematical Sciences, Seoul National University)

Deep Learning for Inverse Wave Scattering Problems

Jong Chul Ye (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology)

Computational models for Coherence retrieval

Chenglong Bao (Yau Mathematical Sciences Center, Tsinghua University)

Partial Differential Equations in Manifold Learning

Zuoqiang Shi (Tsinghua University)

Machine Learning for Seismic Data Processing

Jianwei Ma (Department of Mathematics, Center of Geophysics, Harbin Institute of Technology)

Operator Norm Optimization for Structural Changes in Cryo-EM Imaging

Yunho Kim (Department of Mathematical Sciences, Ulsan National Institute of Science and Technology)

Exploiting Low-Quality Visual Data using Deep Networks

Zhangyang Wang (Department of Computer Science and Engineering, Texas A&M University (TAMU))

Learned Experts' Assessment-Based Reconstruction Network ("LEARN") for Sparse-Data CT

Yi Zhang (School of Computer Science, Sichuan University)

An Edge Driven Wavelet Frame Model for Image Restoration

Jae Kyu Choi (Institute of Natural Sciences, Shanghai Jiao Tong University)

Organizers:

Chenglong Bao (Yau Mathematical Sciences Center, Tsinghua University)

Jae Kyu Choi (Institute of Natural Sciences, Shanghai Jiao Tong University)

Keywords:

data driven, deep learning, image reconstruction, inverse problems, machine learning