Data-driven approaches in imaging scienceMS

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.

PART 1 Schedule to be decided

Algorithmic Self-Calibration in Computational Imaging
Laura Waller ( University of California, Berkeley )
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
Prof. Jong Chul Ye ( Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology )
Computational models for Coherence retrieval
Chenglong Bao ( Department of Mathematics, National University of Singapore )

PART 2 Schedule to be decided

Toward non-stationary blind image deblurring: models and techniques
Hui Ji ( Department of Mathematics, National University of Singapore )
Machine Learning for Seismic Data Processing
Jianwei Ma ( Department of Mathematics, Center of Geophysics, Harbin Institute of Technology )
Exploiting Low-Quality Visual Data using Deep Networks
Zhangyang Atlas Wang ( Department of Computer Science and Engineering, Texas A&M University (TAMU) )
On the Convergence of Block Coordinate Descent in Training DNNs with Tikhonov Regularization
Ziming Zhang ( Mitsubishi Electric Research Laboratories )

PART 3 Schedule to be decided

Operator Norm Optimization for Structural Changes in Cryo-EM Imaging
Prof. Yunho Kim ( Department of Mathematical Sciences, Ulsan National Institute of Science and Technology )
Sparse Approximation and Imaging Science
Jia Li ( School of Mathematics, Sun Yat-Sen University )
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
PhD Jae Kyu Choi ( Institute of Natural Sciences, Shanghai Jiao Tong University )
Organizers:
Chenglong Bao ( Department of Mathematics, National University of Singapore )
PhD Jae Kyu Choi ( Institute of Natural Sciences, Shanghai Jiao Tong University )
Keywords: