Computational Imaging Group (CIG) is looking for motivated WashU students for the following research projects for Fall 2019. If you are interested in a given project, please apply by emailing your resume to the project supervisor.

Deep Single-Molecule Super-Resolution Imaging

Status: active (Shiqi Xu) Description: The spatial resolution of traditional fluorescence microscopy techniques is limited by the diffraction of light. Super-resolution single-molecule microscopies such as STORM and PALM employ novel approaches to overcome the diffraction barriers, enabling biological studies at a nanometer scale. Recent works have shown that a convolutional neural network (CNN) could provide fast, accurate reconstructions for super-resolution imaging. The goal of this project is to develop a state-of-the-art deep learning based reconstruction method for single-molecule localization microscopy. Supervision: Yu Sun (sun.yu@wustl.edu), Ulugbek Kamilov Areas: Computational Microscopy, Learning for Imaging

Learning for material imaging with light

Status: active (Ray Wu) Description: Optical imaging has been widely applied in various fields such as material imaging for nano-chip design. Recent works have shown that deep neural networks (DNNs) are able to invert multiple light scattering and to reconstruct a high-quality two-dimensional (2D) image in a very short time, which is attractive for industrial applications. The goal of this project is to design a novel DNN architecture that can reconstruct 3D objects from the light measurements and deploy the model for material imaging. Supervision: Yu Sun (sun.yu@wustl.edu), Ulugbek Kamilov Areas: Computational Microscopy, Learning for Imaging

Learning for magnetic resonance imaging (MRI)

Status: active (Weijie Gan) Description: Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to form pictures of the anatomy and the physiological processes of the body. One of the key practical limitations of MRI is the long duration of data acquisition. The goal of this project is to reduce the scan times by using deep-learning-based imaging techniques. Supervision: Xiaojian Xu (xiaojianxu@wustl.edu), Ulugbek Kamilov Areas: Medical Imaging, Learning for Imaging

Regularization with ConvNets

Status: active (Jiaming Liu) Description: Convolutional neural networks (CNNs) have been successfully applied to numerous tasks in computational imaging and computer vision. Currently, their application for regularization of ill-posed inverse problems is a popular topic of research. In this project, the goal is to investigate various strategies of incorporating CNNs for stabilizing image reconstruction with applications to optical imaging. Supervision: Yu Sun (sun.yu@wustl.edu), Ulugbek Kamilov Areas: Learning for Imaging, Medical Imaging

• Regularization with ConvNets (J. Liu, Y. Sun, U. S. Kamilov)
• Learning for 3D computational imaging (W. Gan, X. Xu, U. S. Kamilov)
• Convolutional dictionary learning for image reconstruction (F. Long, X. Xu, U. S. Kamilov)
• Unsupervised deep image learning (H. Zou, Y. Sun, U. S. Kamilov)
• Distributed algorithms for nonconvex stochastic optimization (M. Abijaoude, U. S. Kamilov)
• Learning for material imaging with light (R. Wu, Y. Sun, U. S. Kamilov)
• Image restoration with GANs (F. Zhai, Y. Sun, U. S. Kamilov)
• Learning for MRI (J. Xing, X. Xu, U. S. Kamilov)
• Learning for material imaging with light (R. Wu, Y. Sun, U. S. Kamilov)
• Dictionary Learning for Fourier Ptychographic Microscopy (S. Xu, X. Xu, U. S. Kamilov)